**Clarifying object listing approaches**

The user might have used Cloudflare API's "search" or "metadata" operations, which can get pricey, especially if they're using HEAD requests. Instead, listing objects directly seems to be the better route.

I can consider different methods like using `rclone lsf` for concurrency or employing `aws s3 sync --dryrun` to compare buckets, but sync might end up being more costly.

To start, I can implement the script in the repo, perhaps named `scripts/r2_diff_ids.py`. I also need to answer the conceptual question of the fastest and cheapest way to compute differences: using R2 inventory or straightforward list operations.

Using `aws s3api list-objects-v2` with boto3 could work well to output keys to a file, and then I can use sorting and comparison to find the differences, possibly with Python sets. A sequential approach might make sense too; instead of fully listing both buckets, I could check against a supabase queue for completed videos.

They mentioned a discrepancy of 20-30k missing items, and they want answers quickly. I can write a script that does the listing and comparison and maybe even allow for sampling options. While listing 536k objects could take 5-10 minutes, it’s manageable.

Also, I need to be cautious about outputting too much data. Writing the results to a file while providing a summary would be best. I’ll need to check existing scripts in the repo for any similar functions. It’s a multi-step task, so it might be helpful to outline a plan for clarity: confirm bucket configurations, create the diff script, run it, and produce the missing list. I should also search for any similar `diff` code that might already exist.


**Computing discrepancies in S3 buckets**

I see that the API token is empty, so they might be using S3 API instead.

We've got R2 credentials for S3, which is great! Now I need to compute the discrepancies between the 'test' and '1-cleaned-data' buckets.

I'll write a script to list keys for the source bucket under the prefix 'podcasts/'. For the cleaned bucket, I might check for `.tar` files across all keys. We just need to identify which keys match the cleaned results. The key format looks like `<upload_prefix>/<video_id>.tar`. Also, multiple prefixes could be set for uploads. While scanning the entire bucket, I might run into extra `.tar` files not related to the video pipeline, but most likely they’ll all belong. There might be additional objects like logs and analytics as well.

**Analyzing cleaned data discrepancies**

They mentioned that the number of items—502.24k—likely represents all cleaned data, which includes mostly `.tar` files plus some extras. This difference of 20-30k suggests some files might be missing.

The simplest approach would be to list all `.tar` objects in the cleaned bucket and extract their IDs from the filenames, treating them as video_ids. I'll do the same for the source bucket under the `podcasts/` prefix. After that, I can compute the difference between `source_ids` and `cleaned_ids` to identify what's missing.

We also need to handle cases with multiple audio files for each video, like transcripts and segments, which might create duplicates. Using a set will help deduplicate. I should also account for different file extensions in the source bucket, like webm, m4a, and mp3. The base name extraction is necessary, and I need to be careful of any nested directories within the `podcasts/` path. A quick sample check could confirm the file naming structure!

**Extracting IDs from audio files**

I need to include other formats like `.mp3` as well, so I'll create a generic approach. For each key under `podcasts/`, I’ll take the last segment of the path, remove the extension, and treat it as an ID. I do need to be careful, though, since some keys could have suffixes like `_original`.

In the source bucket, original audio might be named with formats like `<video_id>.m4a`. I’ll use regex to identify YouTube IDs, which are 11 characters long. However, the reference to "videoIDs" might suggest they're not strictly YouTube IDs; they could be internal.

To extract an ID accurately, I can use regex like `r'([A-Za-z0-9_-]{11})'`. Still, I have to consider other 11-character sequences that could exist. So, it might be safer to compute by checking the filename length and matching it to our allowed patterns. If it’s not an exact match, I can look for an 11-character string within it. For the cleaned bucket, I'll remember that tar filenames follow the format `<video_id>.tar`.

**Optimizing ID extraction process**

I’m focusing on the base name length of 11, which will apply to filenames like `cleaned/trail/<video_id>.tar`. I also need to consider the cleaned bucket, as there could be tar files that don't have corresponding sources, so I'll compute `cleaned_ids - source_ids` to spot any extras.

For performance, listing 500k objects in a bucket can be efficiently done using `list_objects_v2` with pagination via boto3. This lets us stream the extraction without needing to store all keys, just the video IDs in a Python set.

Storing 500k IDs might require around 25MB of memory, plus a bit more for overhead. However, they want the cheapest option. Each list request counts as Class A, which means about 1000 Class A requests overall. Cloudflare R2 charges roughly $4.50 per million operations, so 1000 requests will only cost about $0.0045, which is trivial. The whole process might take just a few minutes!


Output:
Exit code: 0
Wall time: 0 seconds
Output:
src/r2_client.py:107:                raise RuntimeError("R2 not configured: missing R2_ENDPOINT_URL (or S3_API)")
src/r2_client.py:109:                raise RuntimeError("R2 not configured: missing R2_ACCESS_KEY_ID (or AWS_ACCESS_KEY_ID)")
src/r2_client.py:111:                raise RuntimeError("R2 not configured: missing R2_SECRET_ACCESS_KEY (or AWS_SECRET_ACCESS_KEY)")
src/r2_client.py:213:        """Return S3 head_object response for a key, or None if missing/unreadable."""
src/overlap_detection.py:207:            # Check if different speakers overlap
src/download.py:541:    # Log clearly what was tried - no exception trace needed for missing files
src/download.py:660:                raise DownloadError("Downloaded file too small or missing")
src/download.py:818:        duration_diff = abs(trimmed_duration - original_duration_check)
src/download.py:820:        if duration_diff > 0.1:  # Allow 0.1s tolerance
src/download.py:822:            raise DownloadError(f"Duration mismatch between outputs: {duration_diff:.2f}s difference")


Summary: auto

## Skills
A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.
### Available skills
- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md)
- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)
### How to use skills
- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.
- How to use a skill (progressive disclosure):
  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.
  2) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  3) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  4) If `assets/` or templates exist, reuse them instead of recreating from scratch.
- Coordination and sequencing:
  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.
- Context hygiene:
  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.
  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.
  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.
- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.


**Evaluating audio processing counts**

I’m considering that if the source bucket has raw audio, it might have one object per video, while the cleaned bucket includes a tar file per video. Counts should match if everything’s processed, but if there’s a difference, that could indicate missing items or simply additional non-audio objects in the source bucket. The user mentions a 20k difference, which might mean they assume one file per video. I can parse to get unique video IDs in each bucket for a more accurate count and provide a cost estimate based on operations.


**Considering inventory reporting options**

I’m thinking that we can suggest using the Cloudflare R2 inventory report for long-term tracking, but for a quick solution, we should perform a list/diff. Options like `rclone lsf` and `aws s3 ls --recursive` come to mind, though the user prefers something simpler. We can implement a Python script from our repo. Looking at the .env file, I see necessary variables like R2_ENDPOINT_URL, but not the AWS region. I'll configure the R2Client to work with the environment settings and decide whether to apply a prefix when listing. Scanning the entire bucket could be beneficial to capture all IDs.


**Exploring user options and performance**

I’m considering that we might want to allow users to specify prefixes, like `--source-prefix podcasts/`, and potentially support multiple prefixes for cleaned objects. However, for simplicity, we could stick to defaults: source prefix `podcasts/` and cleaned suffix `.tar`. I'll make sure to include these options. Notably, listing 500k objects from R2 will take time, so I need to provide progress updates. The user asked about the fastest and cheapest way to find discrepancies. I’ll explain that running a full list of keys from each bucket and doing an offline diff is the most cost-effective approach.


**Considering options for efficiency**

For recurring tasks, I think enabling R2 inventory and diff output could be useful. There’s also a cost-effective option: if Supabase records the `r2_upload_full_key` for completed videos, we could query that to identify completed videos and perform HEAD checks on those objects. However, that could mean 500k HEAD operations, which takes time. Listing is generally more efficient. Since the user asked about searching recursively, I want to clarify that listing is recursive by default if we set a prefix with `list_objects_v2`. I’ll proceed with implementing the script for this.


**Designing the script for video ID comparison**

I’m planning to create a script that loads the .env file and sets up `R2Client` for both source and cleaned buckets. I’ll list the objects using a paginator. For the source bucket, I’ll filter keys by a default `source_prefix` of `podcasts/` and extract video IDs by removing extensions. For the cleaned bucket, I’ll filter by the `.tar` suffix. It’s essential to compute the differences and intersections, then produce summary stats.

I’ll save the results in a `reports` folder and offer options for outputs, like sample missing IDs. While printing long lists directly might be too cumbersome, I can limit the console output. If needed, I’ll create options for updating Supabase statuses but keep the script primarily focused on the ID comparison task. Since the user mentioned a significant number of objects, I want to ensure efficiency while managing the listings. Finally, I will implement this as `scripts/r2_diff_video_ids.py`.


**Planning script execution for video ID comparison**

I’m outlining the usage for running the script with default parameters. It would look something like this:

```
python3 scripts/r2_diff_video_ids.py \
  --source-bucket-type source \
  --source-prefix podcasts/ \
  --source-exts .webm .m4a .mp3 \
  --cleaned-bucket-type production \
  --cleaned-suffix .tar \
  --out-dir reports/r2-diff
```

I want to be mindful of runtime, possibly setting concurrency to speed things up. The S3 listing requests can’t be parallelized easily, which makes me consider the overall time—probably around 100-200 seconds for 1000 requests.

I should also adjust the timeout for long listings, maybe as high as 30 minutes. Writing IDs to files during execution is a smart way to manage memory and handle large sets efficiently. Finally, I’ll create a structured plan: add the script, run it, and summarize the results while ensuring not to overwhelm the user with output. Time to implement!


Explanation: Compute missing video IDs between R2 source (test) and cleaned (1-cleaned-data) by listing keys via S3 API and diffing locally.


Output: Plan updated


Summary: auto


**Inspecting configuration patterns**

I’m starting with step 1, which involves inspecting patterns. I’ve already looked at the pipeline and script, but I should probably double-check the cleaned bucket prefix from the config. It's possible that this information isn't in the .env file. So now, I’ll look into `src/config.py` or the `massive_process` to find `r2_upload_prefix` and any relevant environment variables. Understanding the config options is important to grasp the defaults.


Output:
Exit code: 0
Wall time: 0 seconds
Total output lines: 220
Output:
#!/usr/bin/env python3
"""
Configuration for Fast Pipeline v6.0 - Adaptive Compute-Aware

Key improvements:
- Auto-detects system resources (nproc, vCPUs, GPU vRAM)
- Adapts worker counts and batch sizes to available compute
- Optimized for micro-level speaker detection (0.4s events)
"""

import os
from dataclasses import dataclass, field
from typing import Optional

@dataclass
class Config:
    """
    Pipeline configuration with adaptive compute settings.

    Flow (per instructions.md):
    1. Download (max parallel workers)
    2. Quick VAD (parallel, get speech outline)
    3. Chunk at silence boundaries (VAD-aware)
    4. OSD (mark overlaps as unusable FIRST)
    5. Frame-level segmentation (17ms resolution for micro-changes)
    6. Embeddings + Conservative clustering
    7. Output metadata JSON only
    """

    # === AUTHENTICATION ===
    # NOTE: Must be provided via environment variable HF_TOKEN
    # Never hard-code tokens in repo (HF gated model access + security).
    hf_token: str = os.environ.get('HF_TOKEN', '')

    # === INPUT SOURCE (YouTube vs R2) ===
    # Default behavior remains YouTube URLs. For benchmark/testing you can pass
    # bare 11-char video IDs and set input_source='r2' to stream originals from
    # the R2 source bucket (bucket name: "test") without YouTube rate limits.
    input_source: str = "youtube"  # 'youtube' | 'r2'

    # === R2 SOURCE (input) ===
    # When input_source='r2', download.py will:
    # - Resolve video_id -> object key via HEAD (NO bucket listing)
    # - Generate a presigned GET URL
    # - ffmpeg stream-extract audio
    #
    # Prefix/extension knobs are optional; env overrides exist too:
    # - R2_SOURCE_PREFIX / R2_SOURCE_PREFIXES
    # - R2_SOURCE_EXTENSIONS
    r2_source_prefix: str = ""          # e.g. "podcasts/" (optional)
    r2_source_extensions: str = ""      # e.g. ".webm,.mp4" (optional)
    r2_presign_ttl_sec: int = 3600      # Presigned URL TTL (seconds)

    # Avoid any YouTube metadata calls during R2 runs (rate-limit safe).
    # When False, R2 downloads fall back to duration-based dynamic intro skip.
    fetch_youtube_metadata_for_r2: bool = True

    # === R2 OUTPUT (upload processed artifacts) ===
    # When enabled, pipeline will package each video's output directory into a .tar
    # and upload it to the R2 production bucket (1-cleaned-data) by default.
    r2_upload_enabled: bool = False
    r2_upload_bucket_type: str = "production"  # maps to 1-cleaned-data in src/r2_client.py
    r2_upload_prefix: str = ""                 # e.g. "benchmarks/rtx4090/2026-01-03T16-00Z"
    r2_upload_skip_if_exists: bool = False     # avoid overwriting if True
    r2_upload_delete_local_tar: bool = True    # free local disk after upload
    r2_upload_delete_local_output_dir: bool = True  # delete per-video output dir after R2 upload

    # === LOCAL CLEANUP (v8.0 - Prevent disk filling) ===
    # When enabled, cleans up processed video directories immediately after:
    # 1. Successful R2 upload (if r2_upload_enabled)
    # 2. OR after processing completes (if no R2 upload but cleanup_after_processing=True)
    # This prevents disk from filling up during long processing runs
    cleanup_after_processing: bool = True     # Clean output dir after each video
    cleanup_original_audio_after_tar: bool = True  # Delete _original.flac after tar creation

    # === AUDIO SEGMENTATION ===
    # segment_audio: Cut original FLAC into individual speaker segment files
    # Uses parallel ffmpeg (8 workers) - typically 2-3s for 500 segments
    segment_audio: bool = True

    # === BACKGROUND EXPORT ===
    # NOTE: Background export is experimental and not recommended for multiprocessing.
    # In GPU worker subprocesses, background threads die when the process exits.
    # The sync export (5-6s for 60min video) runs AFTER GPU work completes,
    # and overlaps with the NEXT video's download prefetch, so overhead is minimal.
    background_export: bool = False  # Keep False for stability
    background_export_workers: int = 4  # Number of parallel export workers (unused currently)

    # === AUDIO PROCESSING ===
    sample_rate: int = 16000
    intro_skip_seconds: float = 0.0
    outro_skip_seconds: float = 0.0

    # === INTRO SKIP (v6.8 - Dynamic) ===
    # auto_intro_skip: Automatically skip intro based on video duration
    # When True and no chapter-based intro detected:
    #   - Video ≤ 30 min: skip 180s (3 min)
    #   - Video > 30 min: skip 300s (5 min)
    # Set to False to disable automatic intro skipping
    # Manual intro_skip_seconds > 0 always takes priority
    auto_intro_skip: bool = True

    # === HIGH QUALITY AUDIO PRESERVATION (v6.8+) ===
    # preserve_original_audio: Download at highest quality, process at 16kHz
    # When True:
    #   - Downloads original audio format (bestaudio - typically 48kHz)
    #   - Creates 16kHz copy for processing (VAD, diarization, embeddings)
    #   - After processin…1039 chars truncated… # CHANGED: 100 -> 200 per instructions
    vad_window_size_samples: int = 512
    vad_speech_pad_ms: int = 30
    vad_chunk_size: float = 60.0   # Parallel VAD chunk size

    # === MODEL SELECTION ===
    # Per user request: community-1 first, 3.1 fallback
    use_community_model: bool = True  # Try community-1 first

    # === SEGMENTATION (Frame-level for micro-changes) ===
    # Per instructions: 17ms frame resolution, NOT 1.5s chunks
    segmentation_step: float = 2.5     # Sliding window step (seconds)
    segmentation_duration: float = 5.0  # Window duration (seconds)
    min_segment_duration: float = 0.2   # Min segment (catch 0.4s events)

    # === OVERLAP DETECTION ===
    detect_overlap: bool = True
    overlap_threshold: float = 0.35     # Probability for active speaker
    overlap_min_duration: float = 0.1  # Min overlap to mark
    overlap_padding_ms: int = 100      # Pad overlap boundaries (±100ms)

    # === OVERLAP DENSITY DETECTION (v6.9) ===
    # Mark short segments sandwiched between overlaps as unusable
    # These are "islands" of supposedly clean audio in high-overlap regions
    overlap_density_filter: bool = True
    overlap_density_max_gap: float = 1.0      # Max gap to adjacent overlap (seconds)
    overlap_density_max_duration: float = 3.0  # Max segment duration to mark as unusable

    # === QUALITY FILTERING (v6.1) ===
    filter_by_quality: bool = True     # Enable SNR/quality filtering
    min_snr_db: float = 15.0           # Minimum SNR for TTS usability
    min_quality_score: float = 0.3     # Minimum quality score (0-1)

    # === SEGMENT DURATION FILTERING (v6.1) ===
    # Drop speaker segments shorter than this from the FINAL output.
    #
    # IMPORTANT: we do adjacent same-speaker merging before applying this filter,
    # so short fragments (e.g. 0.4-0.9s) can be absorbed into longer continuous
    # turns when safe to do so.
    min_tts_duration: float = 1.0      # Minimum segment duration for TTS

    # === DIARIZATION ===
    min_speakers: int = 1
    max_speakers: int = 10

    # === EMBEDDINGS (OOM-protected) ===
    embedding_batch_size: int = 8      # Will be auto-tuned
    max_embedding_length: int = 16000 * 10  # Max 10s per segment

    # === CLUSTERING (Conservative - no poison merges) ===
    # Per instructions: 0.75-0.80 to prevent merging different speakers
    cluster_merge_threshold: float = 0.80
    min_segments_for_merge: int = 2

    # === CHUNK REASSIGNMENT (v7.7 - With Validation Gates) ===
    # Detects within-segment speaker changes and surgically splits/reassigns
    #
    # === v7.7 FIX: VALIDATION GATES prevent false positive splits ===
    # Previous issue: 66x speaker fragmentation (6 speakers → 400+)
    # Root cause: Fixed thresholds + create_new_on_ambiguous triggered on natural variation
    #
    # NEW GATES (see unified_embeddings.py):
    # 1. Persistence gate: Require ≥N consecutive low-sim chunks
    # 2. Relative valley gate: Per-segment adaptive threshold (outlier detection)
    # 3. Same-speaker centroid gate: Reject if both sides match original speaker
    # 4. No-new-speaker-unless-proven: Keep original speaker on ambiguous (key fix!)
    enable_chunk_reassignment: bool = True  # RE-ENABLED with validation gates
    chunk_reassignment_threshold: float = 0.30  # Normal threshold (per-segment adaptive overrides)
    chunk_reassignment_severe: float = 0.15  # Severe circuit-breaker (only extreme changes)
    chunk_reassignment_min_speech: float = 0.6  # Min speech ratio for eligibility
    chunk_reassignment_min_portion: float = 1.0  # LOWERED: 1.0s to catch "yeah/ok" (was 2.0s)

    # === v7.8: TUNABLE GATE PARAMETERS for catching small interruptions ===
    # Lower values = more aggressive splitting (catches more but risks false positives)
    chunk_reassignment_min_analysis: float = 2.0  # Min segment duration to analyze (was hardcoded 3.0)
    chunk_reassignment_persistence: int = 1  # Min consecutive low-sim chunks (was hardcoded 2)
    chunk_reassignment_centroid_threshold: float = 0.55  # Match threshold for centroid gate (was hardcoded 0.60)

    # === MUSIC DETECTION (v7.4 - Hybrid INA+PANNs) ===
    # Two-stage detection: Fast INA screening + PANNs verification
    # Stage 1: inaSpeechSegmenter (fast CNN, ~10x realtime)
    # Stage 2: PANNs CNN14 (only on suspicious segments)
    enable_music_detection: bool = True  # Enable music detection
    use_hybrid_detection: bool = False  # DISABLED: Run PANNs on EVERYTHING for strict music detection
    music_chunk_duration: float = 1.5  # Same as embedding chunks
    music_batch_size: int = 128  # Batch size for PANNs inference (v7.5: optimized for A100)

    # === STRICT DETECTION THRESHOLDS FOR TTS QUALITY (v7.1) ===
    # Zero tolerance: ANY contamination → needs_demucs or unusable

    # Per-chunk thresholds (RESTORED to working values)
    music_prob_threshold: float = 0.20  # Chunk "has music" if prob > 20%


**Finalizing progress on tasks**

I’m thinking about the R2 source prefix, which is optional. For our diff script, we just need to list things out. I noticed in download.py, they resolved via HEAD for a specific video ID, which is interesting! But for the diff, we list instead. Now, I've completed step 1, which focused on inspecting patterns, and I should mark it as done while moving onto step 2 in progress.


Output: Plan updated


**Preparing output files**

I'm trying to gather a list of missing IDs and write them to files, using a timestamp for each. I'll create an output directory, `reports/r2-diff`, and if it doesn't exist, I'll go ahead and create it.

The files I plan to write are `source_ids.txt`, `cleaned_ids.txt`, `missing_in_cleaned.txt`, and `extra_in_cleaned.txt`. I’m considering adding a summary JSON file for counts, but I want to ensure the user gets the latest data without overwriting. Including timestamps seems like a good compromise.


**Analyzing video IDs and environment settings**

I’m considering an edge case where some video IDs could appear multiple times with different extensions, so I need to set handles for those. I have a larger question though: if the cleaned bucket has around 502k objects, it should roughly match the number of IDs if they’re all `.tar`. The source might have 536k objects, but not everything is podcasts — it may include raw audio.

I have to ensure the script uses the correct environment settings for the R2 endpoint and credentials. The R2Client reads from the environment variables, and any prefix set in `.env` would influence all operations, including listing keys.


**Clarifying prefix usage in R2Client**

I've realized that R2Client doesn't automatically apply a prefix for listing; it only uses it when building keys. This means I’ll go with the raw boto3 client for listing, using the bucket and prefix we supply.

If R2_PREFIX is set and I use R2Client, the bucket will be '1-cleaned-data', but the prefix will come from configuration. For listing, I want to keep the cleaned prefix empty by default to allow flexibility, but I'll also add an option to use client prefixes if needed. I'll ensure that R2_PREFIX won't affect the listing, although some workers might set it differently when uploading.


**Understanding file uploads and prefixes**

I've noticed that `r2.upload_file` uses `r2._full_key(remote_key)`, which includes the prefix from the environment variable R2_PREFIX. This means the object key in the bucket would be `R2_PREFIX/remote_key` if it's set.

In the pipeline, they're storing `r2_upload_full_key`, which also includes R2_PREFIX. So if R2_PREFIX is set, outputs end up under that prefix. The concern is that the user might have runs with different prefixes, leading to scattered objects. For the source bucket, using R2Client with `bucket_type='source'` reads safely, but the script should allow for a recursive search instead of only one prefix, defaulting source_prefix to empty but providing an option.


**Deciding on listing strategy**

I’m thinking we can default to listing `podcasts/`, but keeping it empty would allow us to list the entire bucket. This could pull in other keys, but there's a risk of false positives in ID parsing.

For a longer-term solution, enabling bucket inventory and comparing reports sounds good. For now, we might leverage Cloudflare R2 listing with `list_objects_v2` using `Prefix` and `Delimiter` to organize prefixes and possibly speed things up. Parallelizing the listing across prefixes could be beneficial, but it might also multiply operations significantly.

However, avoid listing the source bucket directly; instead, we can use Supabase for expected IDs, then list the cleaned bucket for differences. The main goal is to implement the script without any Python packaging issues, so I’ll proceed with creating the file and using `apply_patch`.


Input:
*** Begin Patch
*** Add File: scripts/r2_bucket_diff.py
+#!/usr/bin/env python3
+"""
+Compute video-id discrepancies between two Cloudflare R2 buckets (S3 API).
+
+Default expectations for this repo:
+  - Source/original audio lives in bucket "test" under "podcasts/".
+  - Cleaned outputs live in bucket "1-cleaned-data" as per-video "{video_id}.tar"
+    (possibly under arbitrary prefixes).
+
+This script lists objects (recursively) using S3 ListObjectsV2 and diffs the
+extracted video IDs locally.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import re
+import sys
+import time
+from dataclasses import asdict, dataclass
+from datetime import datetime, timezone
+from pathlib import Path, PurePosixPath
+from typing import Iterable, Optional
+
+# Add project root so `src.*` imports work when run from repo root.
+REPO_ROOT = Path(__file__).resolve().parent.parent
+sys.path.insert(0, str(REPO_ROOT))
+
+try:
+    from dotenv import load_dotenv
+
+    load_dotenv(dotenv_path=str(REPO_ROOT / ".env"))
+except Exception:
+    # Running without python-dotenv is fine if env vars are already set.
+    pass
+
+from src.r2_client import R2Client
+
+
+YOUTUBE_ID_RE = re.compile(r"^[A-Za-z0-9_-]{11}$")
+YOUTUBE_ID_IN_STR_RE = re.compile(r"(?<![A-Za-z0-9_-])([A-Za-z0-9_-]{11})(?![A-Za-z0-9_-])")
+
+
+@dataclass(frozen=True)
+class ScanResult:
+    bucket: str
+    prefix: str
+    suffix: str
+    objects_scanned: int
+    ids_found: int
+    keys_skipped: int
+    duration_sec: float
+
+
+def _extract_video_id_from_key(key: str) -> Optional[str]:
+    name = PurePosixPath(key).name
+    stem = name.rsplit(".", 1)[0] if "." in name else name
+    if YOUTUBE_ID_RE.match(stem):
+        return stem
+    match = YOUTUBE_ID_IN_STR_RE.search(stem)
+    if match:
+        return match.group(1)
+    return None
+
+
+def _iter_object_keys(r2: R2Client, prefix: str) -> Iterable[str]:
+    paginator = r2.client.get_paginator("list_objects_v2")
+    kwargs = {"Bucket": r2.bucket}
+    if prefix:
+        kwargs["Prefix"] = prefix
+    for page in paginator.paginate(**kwargs):
+        for obj in page.get("Contents", []) or []:
+            key = obj.get("Key")
+            if key:
+                yield key
+
+
+def scan_bucket_ids(
+    *,
+    r2: R2Client,
+    prefix: str,
+    suffix: str,
+    progress_every: int,
+) -> tuple[set[str], ScanResult]:
+    started = time.time()
+    objects_scanned = 0
+    keys_skipped = 0
+    ids: set[str] = set()
+
+    last_progress = started
+    for key in _iter_object_keys(r2, prefix=prefix):
+        objects_scanned += 1
+
+        if suffix and not key.endswith(suffix):
+            continue
+
+        video_id = _extract_video_id_from_key(key)
+        if video_id:
+            ids.add(video_id)
+        else:
+            keys_skipped += 1
+
+        if progress_every and objects_scanned % progress_every == 0:
+            now = time.time()
+            # Avoid spamming output if the scan is very fast.
+            if now - last_progress >= 0.25:
+                rate = objects_scanned / max(now - started, 1e-6)
+                print(
+                    f"  scanned={objects_scanned:,} ids={len(ids):,} skipped={keys_skipped:,} "
+                    f"rate={rate:,.0f}/s",
+                    flush=True,
+                )
+                last_progress = now
+
+    duration = time.time() - started
+    return ids, ScanResult(
+        bucket=r2.bucket,
+        prefix=prefix,
+        suffix=suffix,
+        objects_scanned=objects_scanned,
+        ids_found=len(ids),
+        keys_skipped=keys_skipped,
+        duration_sec=duration,
+    )
+
+
+def _write_lines(path: Path, lines: Iterable[str]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", encoding="utf-8") as f:
+        for line in lines:
+            f.write(f"{line}\n")
+
+
+def main() -> int:
+    p = argparse.ArgumentParser(description="Diff video IDs between two R2 buckets (source vs cleaned)")
+    p.add_argument("--source-bucket-type", default="source", help="R2Client bucket_type for source (default: source)")
+    p.add_argument(
+        "--source-prefix",
+        default="podcasts/",
+        help='Prefix to list in source bucket (default: "podcasts/"; empty => whole bucket)',
+    )
+    p.add_argument(
+        "--source-suffix",
+        default="",
+        help="Optional suffix filter for source keys (e.g. .webm). Default: no suffix filter",
+    )
+    p.add_argument(
+        "--cleaned-bucket-type",
+        default="production",
+        help="R2Client bucket_type for cleaned (default: production)",
+    )
+    p.add_argument(
+        "--cleaned-prefix",
+        default="",
+        help="Prefix to list in cleaned bucket (default: empty => whole bucket)",
+    )
+    p.add_argument(
+        "--cleaned-suffix",
+        default=".tar",
+        help='Suffix filter for cleaned keys (default: ".tar")',
+    )
+    p.add_argument(
+        "--out-dir",
+        default=str(REPO_ROOT / "reports" / "r2-diff"),
+        help='Output directory (default: "reports/r2-diff")',
+    )
+    p.add_argument("--progress-every", type=int, default=50_000, help="Progress update frequency (objects)")
+    args = p.parse_args()
+
+    # Light validation (don’t print secrets).
+    for k in ("R2_ENDPOINT_URL", "R2_ACCESS_KEY_ID", "R2_SECRET_ACCESS_KEY"):
+        if not os.environ.get(k) and not os.environ.get({"R2_ENDPOINT_URL": "S3_API"}.get(k, "")):
+            print(f"ERROR: missing env var {k} (or its alias). Check .env", file=sys.stderr)
+            return 2
+
+    out_dir = Path(args.out_dir)
+    run_id = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%SZ")
+
+    print("Scanning R2 buckets (ListObjectsV2 is already recursive)...")
+
+    src = R2Client(bucket_type=args.source_bucket_type)
+    print(f"\nSOURCE: bucket={src.bucket} prefix={args.source_prefix!r} suffix={args.source_suffix!r}")
+    source_ids, source_stats = scan_bucket_ids(
+        r2=src,
+        prefix=args.source_prefix.strip("/"),
+        suffix=args.source_suffix,
+        progress_every=args.progress_every,
+    )
+    # Keep prefixes consistent with S3 semantics: "" vs "podcasts/".
+    # If user passed "podcasts/" we should use that exact prefix in list calls.
+    # The above strip("/") is only to avoid accidental leading slashes.
+    # Re-scan if we modified the prefix in an unsafe way.
+    if args.source_prefix.startswith("/") or args.source_prefix.endswith("//"):
+        print("NOTE: normalized source prefix for S3 semantics.")
+
+    cleaned = R2Client(bucket_type=args.cleaned_bucket_type)
+    print(f"\nCLEANED: bucket={cleaned.bucket} prefix={args.cleaned_prefix!r} suffix={args.cleaned_suffix!r}")
+    cleaned_ids, cleaned_stats = scan_bucket_ids(
+        r2=cleaned,
+        prefix=args.cleaned_prefix.strip("/"),
+        suffix=args.cleaned_suffix,
+        progress_every=args.progress_every,
+    )
+    if args.cleaned_prefix.startswith("/") or args.cleaned_prefix.endswith("//"):
+        print("NOTE: normalized cleaned prefix for S3 semantics.")
+
+    missing_in_cleaned = sorted(source_ids - cleaned_ids)
+    missing_in_source = sorted(cleaned_ids - source_ids)
+    common = len(source_ids & cleaned_ids)
+
+    summary = {
+        "run_id": run_id,
+        "source": asdict(source_stats),
+        "cleaned": asdict(cleaned_stats),
+        "diff": {
+            "source_unique_ids": len(source_ids),
+            "cleaned_unique_ids": len(cleaned_ids),
+            "common_ids": common,
+            "missing_in_cleaned": len(missing_in_cleaned),
+            "missing_in_source": len(missing_in_source),
+        },
+    }
+
+    # Persist outputs (text is easiest to reuse later).
+    source_path = out_dir / f"source_ids_{run_id}.txt"
+    cleaned_path = out_dir / f"cleaned_ids_{run_id}.txt"
+    missing_cleaned_path = out_dir / f"missing_in_cleaned_{run_id}.txt"
+    missing_source_path = out_dir / f"missing_in_source_{run_id}.txt"
+    summary_path = out_dir / f"summary_{run_id}.json"
+
+    _write_lines(source_path, sorted(source_ids))
+    _write_lines(cleaned_path, sorted(cleaned_ids))
+    _write_lines(missing_cleaned_path, missing_in_cleaned)
+    _write_lines(missing_source_path, missing_in_source)
+    summary_path.parent.mkdir(parents=True, exist_ok=True)
+    summary_path.write_text(json.dumps(summary, indent=2) + "\n", encoding="utf-8")
+
+    print("\nDONE")
+    print(json.dumps(summary["diff"], indent=2))
+    print(f"\nWrote:\n  {missing_cleaned_path}\n  {missing_source_path}\n  {summary_path}")
+
+    if missing_in_cleaned:
+        print("\nSample missing_in_cleaned (first 20):")
+        for vid in missing_in_cleaned[:20]:
+            print(f"  {vid}")
+
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
+
*** End Patch


Output: {"output":"Success. Updated the following files:\nA scripts/r2_bucket_diff.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Improving the code functionality**

I aim to get everything right. We’re using a proper dataclass `ScanResult` and the `asdict` method, which is great. However, I noticed that our type annotation requires Python 3.9. I think we’re actually running 3.10, so that should be okay. We implemented `PurePosixPath` for handling keys, which is good. I’ll check the style since some lines are long. Lastly, I found a bug: in `scan_bucket_ids`, we're counting skipped keys incorrectly for those not matching the suffix. So it requires some fixing.


**Optimizing path handling**

I see that we wrote `source_path` and `cleaned_path` as sorted sets, which is fine since sorting 500k strings may take some time, but it ensures determinism. However, it might consume extra memory and time. We could choose not to sort for writing, but that could complicate diffs. For output, sorted might be better, so I’ll keep it as is. Additionally, I need to fix the prefix normalization: using `lstrip` and ensuring prefixes end correctly. Keeping user input intact after removing the leading slash seems reasonable too!


**Implementing prefix normalization**

I’m working on implementing a function to normalize the prefix. It will trim whitespace and leading slashes while keeping any trailing slashes. If the user wants to list the whole bucket, an empty prefix is fine. However, if they pass a prefix without a trailing slash, it could inadvertently match other keys. I’m considering whether to add options like `--id-mode` or `--no-write-ids`, but that might not be necessary. After scripting, I’ll run a diff, ensuring that sensitive information like secrets isn’t shown in the output, keeping everything secure.


**Cleaning up prefix handling**

The prefix normalization looks good since it comes from the scan result, making sure we maintain consistency with S3 semantics. However, the comment about using the exact user-provided prefix is a bit messy. I should just focus on normalizing the prefix with `lstrip`, and that’ll eliminate the need for notes or rescan instructions. This will simplify things considerably. I’ll go ahead and update the code to reflect this cleaner approach.


Input:
*** Begin Patch
*** Update File: scripts/r2_bucket_diff.py
@@
 def main() -> int:
     p = argparse.ArgumentParser(description="Diff video IDs between two R2 buckets (source vs cleaned)")
@@
     args = p.parse_args()

-    # Light validation (don’t print secrets).
-    for k in ("R2_ENDPOINT_URL", "R2_ACCESS_KEY_ID", "R2_SECRET_ACCESS_KEY"):
-        if not os.environ.get(k) and not os.environ.get({"R2_ENDPOINT_URL": "S3_API"}.get(k, "")):
-            print(f"ERROR: missing env var {k} (or its alias). Check .env", file=sys.stderr)
-            return 2
+    def normalize_prefix(prefix: str) -> str:
+        # S3 prefixes should not start with "/" (keys are not absolute paths).
+        return (prefix or "").strip().lstrip("/")
+
+    # Light validation (don’t print secrets). Keep alias logic consistent with src/r2_client.py.
+    if not (os.environ.get("R2_ENDPOINT_URL") or os.environ.get("S3_API")):
+        print("ERROR: missing env var R2_ENDPOINT_URL (or alias S3_API). Check .env", file=sys.stderr)
+        return 2
+    if not (os.environ.get("R2_ACCESS_KEY_ID") or os.environ.get("AWS_ACCESS_KEY_ID")):
+        print("ERROR: missing env var R2_ACCESS_KEY_ID (or alias AWS_ACCESS_KEY_ID). Check .env", file=sys.stderr)
+        return 2
+    if not (os.environ.get("R2_SECRET_ACCESS_KEY") or os.environ.get("AWS_SECRET_ACCESS_KEY")):
+        print("ERROR: missing env var R2_SECRET_ACCESS_KEY (or alias AWS_SECRET_ACCESS_KEY). Check .env", file=sys.stderr)
+        return 2

     out_dir = Path(args.out_dir)
     run_id = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%SZ")
@@
     src = R2Client(bucket_type=args.source_bucket_type)
-    print(f"\nSOURCE: bucket={src.bucket} prefix={args.source_prefix!r} suffix={args.source_suffix!r}")
+    source_prefix = normalize_prefix(args.source_prefix)
+    print(f"\nSOURCE: bucket={src.bucket} prefix={source_prefix!r} suffix={args.source_suffix!r}")
     source_ids, source_stats = scan_bucket_ids(
         r2=src,
-        prefix=args.source_prefix.strip("/"),
+        prefix=source_prefix,
         suffix=args.source_suffix,
         progress_every=args.progress_every,
     )
-    # Keep prefixes consistent with S3 semantics: "" vs "podcasts/".
-    # If user passed "podcasts/" we should use that exact prefix in list calls.
-    # The above strip("/") is only to avoid accidental leading slashes.
-    # Re-scan if we modified the prefix in an unsafe way.
-    if args.source_prefix.startswith("/") or args.source_prefix.endswith("//"):
-        print("NOTE: normalized source prefix for S3 semantics.")

     cleaned = R2Client(bucket_type=args.cleaned_bucket_type)
-    print(f"\nCLEANED: bucket={cleaned.bucket} prefix={args.cleaned_prefix!r} suffix={args.cleaned_suffix!r}")
+    cleaned_prefix = normalize_prefix(args.cleaned_prefix)
+    print(f"\nCLEANED: bucket={cleaned.bucket} prefix={cleaned_prefix!r} suffix={args.cleaned_suffix!r}")
     cleaned_ids, cleaned_stats = scan_bucket_ids(
         r2=cleaned,
-        prefix=args.cleaned_prefix.strip("/"),
+        prefix=cleaned_prefix,
         suffix=args.cleaned_suffix,
         progress_every=args.progress_every,
     )
-    if args.cleaned_prefix.startswith("/") or args.cleaned_prefix.endswith("//"):
-        print("NOTE: normalized cleaned prefix for S3 semantics.")
*** End Patch


Output: {"output":"Success. Updated the following files:\nM scripts/r2_bucket_diff.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Summarizing script progress**

I've improved the script. I used `normalize_prefix(args.source_prefix)`, and the default is set to "podcasts/", which is good. For the list call, I added the "podcasts/" prefix as expected, so that's nice too. Now, I've completed step 2, and it's time to move on to marking it as done and starting step 3. It's good to see progress being made!


Output: Plan updated


python3 scripts/r2_bucket_diff.py --progress-every 100000


Output:
Exit code: 0
Wall time: 628.9 seconds
Output:
Scanning R2 buckets (ListObjectsV2 is already recursive)...

SOURCE: bucket=test prefix='podcasts/' suffix=''
  scanned=100,000 ids=100,000 skipped=0 rate=1,186/s
  scanned=200,000 ids=200,000 skipped=0 rate=1,233/s
  scanned=300,000 ids=300,000 skipped=0 rate=1,244/s
  scanned=400,000 ids=400,000 skipped=0 rate=1,246/s
  scanned=500,000 ids=500,000 skipped=0 rate=1,254/s

CLEANED: bucket=1-cleaned-data prefix='' suffix='.tar'
  scanned=100,000 ids=99,994 skipped=0 rate=2,557/s
  scanned=200,000 ids=199,966 skipped=0 rate=2,560/s
  scanned=300,000 ids=299,928 skipped=0 rate=2,556/s
  scanned=400,000 ids=399,921 skipped=0 rate=2,551/s
  scanned=500,000 ids=499,887 skipped=0 rate=2,549/s

DONE
{
  "source_unique_ids": 536127,
  "cleaned_unique_ids": 510094,
  "common_ids": 510094,
  "missing_in_cleaned": 26033,
  "missing_in_source": 0
}

Wrote:
  /home/ubuntu/maya3/reports/r2-diff/missing_in_cleaned_20260113_031622Z.txt
  /home/ubuntu/maya3/reports/r2-diff/missing_in_source_20260113_031622Z.txt
  /home/ubuntu/maya3/reports/r2-diff/summary_20260113_031622Z.json

Sample missing_in_cleaned (first 20):
  --1yBpF537M
  --6SYBPck08
  --7Cu0iaIMc
  --8oWvvuiA0
  --Dqz9Ouo-E
  --E53OhbXDI
  --GyPwGqduk
  --LNAtkEQA8
  --N2XUobL4c
  --W1BWKtMAY
  --X1QOW5S0o
  --_mUeErfRw
  --bOf7DScyQ
  --cvm22e5gk
  --dDjjOkY9A
  --eixaGJd2w
  --kLCLijGHM
  --mOld1ZPAo
  --qdy1Kh3us
  --zx1ie8RQ8


**Analyzing bucket scans**

I see that both buckets were scanned successfully, revealing a discrepancy. The source has 536,127 unique IDs, while the cleaned bucket has 510,094. This means there are 26,033 IDs missing in the cleaned bucket, aligning with the user’s discrepancy of 20k-30k. Interestingly, while the cleaned bucket scanned 500k objects, it shows 510,094 unique IDs. It seems the scanning process may have registered more unique IDs than I initially calculated. I should check the summary JSON for confirmation.


**Viewing summary safely**

I want to check the summary file, which isn't too large. I'll use the `cat` command, but I need to be cautious and make sure there are no sensitive details in it. Since the summary should only contain counts and information about the bucket or prefix, it should be safe to view. Let's go ahead and check it to confirm the details that I need!


Output:
Exit code: 0
Wall time: 0 seconds
Output:
{
  "run_id": "20260113_031622Z",
  "source": {
    "bucket": "test",
    "prefix": "podcasts/",
    "suffix": "",
    "objects_scanned": 536127,
    "ids_found": 536127,
    "keys_skipped": 0,
    "duration_sec": 427.2929253578186
  },
  "cleaned": {
    "bucket": "1-cleaned-data",
    "prefix": "",
    "suffix": ".tar",
    "objects_scanned": 510211,
    "ids_found": 510094,
    "keys_skipped": 0,
    "duration_sec": 200.16813278198242
  },
  "diff": {
    "source_unique_ids": 536127,
    "cleaned_unique_ids": 510094,
    "common_ids": 510094,
    "missing_in_cleaned": 26033,
    "missing_in_source": 0
  }
}


**Assessing object cleanup**

I've scanned 536,127 objects, each with a unique ID, and it seems every object under podcasts corresponds to a video ID. After cleaning, I have 510,211 `.tar` objects but only 510,094 unique IDs, indicating 117 duplicates—likely due to multiple `.tar` files tied to the same ID with different prefixes. The good news is that no keys were skipped, which means all `.tar` key names contain an ID. Now, I’ll mark step 3 as complete and start on step 4.


Output: Plan updated


Summary: auto


**Clarifying data handling**

I can provide instructions for reusing output, specifically regarding the missing IDs file path at `reports/r2-diff/missing_in_cleaned_20260113_031622Z.txt`. The user might be interested in importing this into Supabase or reprocessing it. To preview the count, we can use the command `wc -l` and mention that we already have a count of 26,033.

Even though they ask for the fastest method to identify missing IDs, mentioning potential reasons for missing data—like unprocessed videos, worker failures, and duplicates—might be useful context. However, I understand that they don't want to delve into the reasons. I'll clarify that listing objects with the S3 API acts as the metadata and is cheaper than scanning the dashboard.


**Discussing data handling options**

I can suggest using a local diff, but I should also mention querying missing IDs from the Supabase database, as you already store `r2_upload_full_key`. This way, you wouldn't need to list R2 buckets.

Regarding bucket discrepancies, if the Supabase `videos` table includes statuses, you can check for rows with a status not marked as completed to find missing outputs. While sampling HEAD for `r2_upload_full_key` could help identify those, it might not be essential. Since we have generated the list, I'll ensure step 4 is completed and finalize the plan.


Output: Plan updated


**Crafting Response Summary**

I'm preparing a concise response that includes all the crucial details. First, I’ll explain that boto3/AWS CLI can list items recursively using ListObjectsV2, which is efficient and cost-effective.

Next, I’ll mention we already ran a script on their configured buckets and found 26,033 missing source IDs, and I’ll provide the path to that list with the line count.

I need to outline the costs and time involved, noting it took about 10.5 minutes total. Finally, I’ll include the command for rerunning with different prefixes and suggest how to requeue missing IDs.


**Preparing User Instructions**

I realize I should suggest generating a Supabase script to set those IDs to PENDING as a next step, but they didn’t specifically ask for that. I want to keep my final answer concise, ideally within 10 lines, and I can format it as a bullet list for clarity.

I’ll use backticks for commands and file paths, and while a header might enhance it, I think a bullet list will suffice. I need to be careful about printing IDs in chat, so I’ll just mention the file instead.

Finally, I’ll emphasize that listing objects is recursive, and they don’t need to provide prefixes; passing an empty one will suffice, but we should note that scanning the entire bucket could include unwanted items. I’ll suggest filtering by suffix like `.tar`.


**Crafting User Response**

I'm preparing a response that focuses on providing clear and essential information. First, I’ll explain that `ListObjectsV2` is recursive, allowing for complete bucket listings without needing metadata searches.

The quickest and cheapest way to get a one-time diff is to list keys from both buckets through the S3 API (using boto3 or AWS CLI) and then compute the difference locally.

For 500k objects, that totals about 1,050 list requests at a minimal cost. I ran the `scripts/r2_bucket_diff.py` script, comparing `test` `podcasts/` and `1-cleaned-data`, which yielded significant results. Missing IDs are saved, and I’ll inform the user about re-running with empty prefixes if they’re unknown. Finally, I’ll ask if they’d like a Supabase requeue command for those IDs.


**Referencing File Paths**

I need to keep my response minimal while ensuring I include relevant details about file paths, particularly the starting line numbers. The guidelines state that when referencing files, I must include the relevant start line, which seems a bit unusual to me.

For example, when I mention `scripts/r2_bucket_diff.py`, I should include it as `scripts/r2_bucket_diff.py:1`, and similarly for the output file, I’d reference it as `reports/r2-diff/missing_in_cleaned_20260113_031622Z.txt:1`. However, I realize that referencing such a large file could be cumbersome.


**Finalizing Response Details**

I need to make sure to include the required start line when referencing files. I’ll format the output file as `reports/r2-diff/missing_in_cleaned_20260113_031622Z.txt:1` and the summary JSON as `reports/r2-diff/summary_20260113_031622Z.json:1`.

It’s also important to mention the command used previously. Additionally, I should note that the listing is recursive, and boto3 relies on prefixes instead of recursion for that. Now, I’ll craft the final message!


- Fastest/cheapest way is a one-time `ListObjectsV2` on both buckets (it’s already “recursive”), extract `video_id` from each key (e.g. `podcasts/{id}.*` vs `*/{id}.tar`), then diff locally; for ~500k objects this is ~1,050 list requests total (Class A), which is usually pennies.
- I added `scripts/r2_bucket_diff.py:1` and ran it against your `.env` (`test` bucket `podcasts/` vs `1-cleaned-data` scanning all `.tar` recursively): `26,033` video IDs are in source but missing in cleaned.
- Missing list: `reports/r2-diff/missing_in_cleaned_20260113_031622Z.txt:1` (26,033 lines). Summary: `reports/r2-diff/summary_20260113_031622Z.json:1`.
- If you’re unsure about prefixes, rerun with full recursion: `python3 scripts/r2_bucket_diff.py --source-prefix '' --cleaned-prefix ''` (still filters cleaned by `.tar`).


# Context from my IDE setup:

## Active file: reports/r2-diff/summary_20260113_031622Z.json

## Open tabs:
- summary_20260113_031622Z.json: reports/r2-diff/summary_20260113_031622Z.json
- missing_in_cleaned_20260113_031622Z.txt: reports/r2-diff/missing_in_cleaned_20260113_031622Z.txt
- .env: .env

## My request for Codex:
Can you now check if these videoIDs are in supabase ? if partially found, give summary on what language metadata are those distribution of these 26k samples.


**Checking Supabase for IDs**

Okay, the user wants to check 26,000 missing IDs in Supabase. I’ll need to summarize how many are found, their statuses, and language distribution. I'll refer to Supabase credentials stored in .env. I see we can use the supabase-py library to query the videos table by youtube_id in batches. That number is large, but I think it’s manageable. I’ll inspect the schema and be cautious about rate limits and query length. The optimal batch size seems to be around 200.


**Querying Supabase IDs**

I’m preparing to handle 11-character IDs, considering that around 200 is a safe batch size. I’ll create a script to load the missing IDs list and split it into batches. The goal is to query the Supabase 'videos' table for 'youtube_id, language', and possibly 'status' and 'metadata'. I’ll compute which IDs are found and not found, produce a language distribution for the found ones, and check for the missing IDs too. I need clarity on the 'language' column, but it looks like it's there.


**Computing Language Distribution**

I need to compute the distribution of 'language' values among the records found in Supabase, and maybe even for those not found, although they likely wouldn't have a language. I'll include counts of each language, even 'unknown'. There's also the potential to assess status distributions like ‘COMPLETED,’ even if it wasn’t requested. Since the user asked for language distribution specifically, I’ll likely want to implement a new script, like `scripts/supabase_language_distribution.py`, which could also be reusable. I can run the code to output a summary for the user.


**Creating Supabase Script**

I plan to create a script named `scripts/supabase_check_missing_ids.py` to read a file of IDs and output the count of found IDs along with language distribution. Alternatively, I could run a Python snippet in the CLI for a quick summary. Since the user wants both a list and a summary, I might generate a report in `reports/r2-diff/`, possibly naming it `supabase_missing_in_cleaned_summary_{runid}.json`. I also need to check the AGENTS instructions and inspect the `src/supabase_client.py` for table and field names while confirming the supabase package is installed.


python3 -c "import supabase; print('supabase', getattr(supabase,'__version__', 'unknown'))"


python3 -c "import dotenv; print('dotenv ok')"


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
Supabase Client for Distributed Video Processing Queue

Provides:
- Atomic video claiming with lease-based locking
- Status updates after processing
- Bulk upsert for CSV imports
- Queue statistics

Usage:
    from src.supabase_client import SupabaseClient

    client = SupabaseClient()
    video = client.claim_next_video("worker-1")
    if video:
        # Process video...
        client.update_status(video['youtube_id'], 'COMPLETED', results)
"""

import os
import json
import logging
import time
import random
import socket
from datetime import datetime, timezone
from typing import Dict, Any, Optional, List
from urllib.request import Request, urlopen
from urllib.error import HTTPError, URLError
from urllib.parse import urlencode

logger = logging.getLogger("SupabaseClient")

# Status constants
STATUS_PENDING = 'PENDING'
STATUS_CLAIMED = 'CLAIMED'
STATUS_COMPLETED = 'COMPLETED'
STATUS_FAILED = 'FAILED'
STATUS_SKIPPED = 'SKIPPED'

class SupabaseClient:
    """
    Supabase REST API client for video processing queue.

    Uses atomic claims via RPC function for distributed workers.
    """

    _instance = None

    def __new__(cls, *args, **kwargs):
        """Singleton pattern - reuse connection."""
        if cls._instance is None:
            cls._instance = super().__new__(cls)
            cls._instance._initialized = False
        return cls._instance

    def __init__(self, url: str = None, key: str = None):
        """
        Initialize Supabase client.

        Args:
            url: Supabase project URL (loads from URL env var if None)
            key: Service role key (loads from SUPABASE_ADMIN env var if None)
        """
        if self._initialized:
            return

        self.url = url or os.environ.get('URL', '')
        self.key = key or os.environ.get('SUPABASE_ADMIN', '')

        if not self.url or not self.key:
            logger.warning("Supabase credentials not configured. Set URL and SUPABASE_ADMIN env vars.")

        self.rest_url = f"{self.url}/rest/v1"
        self.headers = {
            'apikey': self.key,
            'Authorization': f'Bearer {self.key}',
            'Content-Type': 'application/json',
            'Prefer': 'return=representation'
        }

        self._initialized = True
        logger.info(f"SupabaseClient initialized: {self.url}")

    def _request(
        self,
        method: str,
        endpoint: str,
        data: dict = None,
        params: dict = None,
        headers_override: dict = None
    ) -> Any:
        """Make HTTP request to Supabase REST API."""
        url = f"{self.rest_url}/{endpoint}"
        if params:
            url = f"{url}?{urlencode(params)}"

        headers = {**self.headers, **(headers_override or {})}

        body = json.dumps(data).encode('utf-8') if data else None

        req = Request(url, data=body, headers=headers, method=method)

        timeout_s = float(os.environ.get("SUPABASE_HTTP_TIMEOUT_SEC", "60") or 60)
        max_attempts = int(os.environ.get("SUPABASE_HTTP_MAX_ATTEMPTS", "6") or 6)
        retryable_codes = {408, 409, 425, 429, 500, 502, 503, 504}

        last_err: Exception | None = None
        for attempt in range(1, max_attempts + 1):
            try:
                with urlopen(req, timeout=timeout_s) as resp:
                    content = resp.read().decode("utf-8")
                    return json.loads(content) if content else None
            except HTTPError as e:
                error_body = e.read().decode("utf-8", errors="replace")
                last_err = e
                if e.code in retryable_codes and attempt < max_attempts:
                    sleep_s = min(30.0, (2 ** (attempt - 1)) * (0.5 + random.random()))
                    logger.warning(
                        f"Supabase HTTP {e.code} on {endpoint} (attempt {attempt}/{max_attempts}): "
                        f"{error_body[:200]}. Retrying in {sleep_s:.1f}s..."
                    )
                    time.sleep(sleep_s)
                    continue
                logger.error(f"Supabase error {e.code}: {error_body}")
                raise
            except (URLError, socket.timeout, TimeoutError) as e:
                last_err = e
                if attempt < max_attempts:
                    sleep_s = min(30.0, (2 ** (attempt - 1)) * (0.5 + random.random()))
                    logger.warning(
                        f"Supabase network/timeout on {endpoint} (attempt {attempt}/{max_attempts}): {e}. "
                        f"Retrying in {sleep_s:.1f}s..."
                    )
                    time.sleep(sleep_s)
                    continue
                logger.error(f"Network error: {getattr(e, 'reason', e)}")
                raise

        # Defensive: should have returned or raised.
        if last_err is not None:
            raise last_err
        return None

    def _rpc(self, function_name: str, params: dict = None) -> Any:
        """Call Supabase RPC function."""
        url = f"{self.url}/rest/v1/rpc/{function_name}"

        body = json.dumps(params or {}).encode('utf-8')
        req = Request(url, data=body, headers=self.headers, method='POST')

        timeout_s = float(os.environ.get("SUPABASE_HTTP_TIMEOUT_SEC", "60") or 60)
        max_attempts = int(os.environ.get("SUPABASE_HTTP_MAX_ATTEMPTS", "6") or 6)
        retryable_codes = {408, 409, 425, 429, 500, 502, 503, 504}

        last_err: Exception | None = None
        for attempt in range(1, max_attempts + 1):
            try:
                with urlopen(req, timeout=timeout_s) as resp:
                    content = resp.read().decode("utf-8")
                    return json.loads(content) if content else None
            except HTTPError as e:
                error_body = e.read().decode("utf-8", errors="replace")
                last_err = e
                if e.code in retryable_codes and attempt < max_attempts:
                    sleep_s = min(30.0, (2 ** (attempt - 1)) * (0.5 + random.random()))
                    logger.warning(
                        f"Supabase RPC HTTP {e.code} {function_name} (attempt {attempt}/{max_attempts}): "
                        f"{error_body[:200]}. Retrying in {sleep_s:.1f}s..."
                    )
                    time.sleep(sleep_s)
                    continue
                logger.error(f"RPC error {e.code}: {error_body}")
                raise
            except (URLError, socket.timeout, TimeoutError) as e:
                last_err = e
                if attempt < max_attempts:
                    sleep_s = min(30.0, (2 ** (attempt - 1)) * (0.5 + random.random()))
                    logger.warning(
                        f"Supabase RPC network/timeout {function_name} (attempt {attempt}/{max_attempts}): {e}. "
                        f"Retrying in {sleep_s:.1f}s..."
                    )
                    time.sleep(sleep_s)
                    continue
                logger.error(f"RPC network error: {getattr(e, 'reason', e)}")
                raise

        if last_err is not None:
            raise last_err
        return None

    def claim_next_video(
        self,
        worker_id: str,
        lease_duration_sec: int = 900,
        language: str = None
    ) -> Optional[Dict[str, Any]]:
        """
        Atomically claim the next PENDING video.

        Uses RPC function with FOR UPDATE SKIP LOCKED for safe concurrent access.
        Falls back to REST API claim if RPC not available.

        Args:
            worker_id: Unique identifier for this worker
            lease_duration_sec: How long to hold the claim (default 15 min)
            language: Optional language filter

        Returns:
            Video dict with youtube_id, youtube_url, title, etc. or None if queue empty
        """
        try:
            # Try RPC function first (atomic)
            result = self._rpc('claim_next_video', {
                'p_worker_id': worker_id,
                'p_lease_duration_sec': lease_duration_sec
            })

            if result and len(result) > 0:
                video = result[0]
                logger.info(f"Claimed video: {video.get('youtube_id')} by {worker_id}")
                return video
            return None

        except HTTPError as e:
            if e.code == 404:
                # RPC function not found, fall back to REST API
                logger.warning("claim_next_video RPC not found, using REST fallback")
                return self._claim_video_rest(worker_id, lease_duration_sec, language)
            raise

    def _claim_video_rest(
        self,
        worker_id: str,
        lease_duration_sec: int,
        language: str = None
    ) -> Optional[Dict[str, Any]]:
        """
        Fallback claim using REST API (less atomic but works without RPC).

        Note: This has a small race condition window between SELECT and UPDATE.
        For production, use the RPC function.
        """
        # Find oldest pending video
        params = {
            'status': f'eq.{STATUS_PENDING}',
            'order': 'created_at.asc',
            'limit': '1'
        }
        if language:
            params['language'] = f'eq.{language}'

        videos = self._request('GET', 'videos', params=params)

        if not videos:
            return None

        video = videos[0]


Output:
Exit code: 0
Wall time: 0.7 seconds
Output:
supabase 2.27.0


Output:
Exit code: 0
Wall time: 0 seconds
Output:
dotenv ok


**Planning data queries**

I think using supabase-py is the easiest option, but I'll need to implement my own retry and backoff mechanism around the `execute` function. We have `approval_policy=never` and network enabled, so we can run this without issues, while also ensuring we don't leak any secrets in the logs.

We need to confirm if the IDs exist by querying Supabase; the results will help us calculate found and not-found counts. I'll also consider duplicates and work from the unique IDs in the missing list. Analyzing language distribution might be necessary too. The user wants to check if the video IDs are in Supabase and get a summary of language metadata for those samples.


**Checking ID distributions**

I need to check which of the 26,000 missing IDs are present in Supabase and compute the language distribution for those found. For the missing ones, we can't calculate their distribution, but I’ll summarize total IDs, how many were found, and their language distribution.

It seems the Supabase table should have a 'language' field since prepare_benchmark uses it, although it might store codes or even be null at times. I'll also create a report file for IDs missing in Supabase for further analysis. This will help with clarity.


**Updating the plan for ID checks**

The user has asked me to check if specific video IDs are in Supabase and to provide a summary of the language metadata for those samples. While they likely prefer a summary, it's still useful to create output files. I’ll need a new plan since the previous one has ended.

Here's the updated plan:
1. Load the missing ID list and batch query Supabase.
2. Aggregate found/not found counts and language distribution.
3. Write report files and present the summary.

I'll implement this as a script, potentially `scripts/supabase_check_ids.py`. This should streamline future tasks!


**Implementing the language summary script**

I could run a quick Python snippet directly, but since the user may want an updated list once I’m done, creating a script is definitely beneficial. I’ll implement `scripts/supabase_language_summary.py` that accepts an `--ids-file` path, defaulting to the latest missing list, while also allowing for a custom path.

The script will include various parameters, like defaults for the table name, ID column, and fields to select. I’ll also load environment variables using dotenv and ensure the necessary configurations are present. Following coding conventions is a priority here!


**Querying and handling results**

Next, I’ll query for each batch of IDs using Supabase. The query will select both the `youtube_id` and `language`. I'll build a mapping for found records and count the occurrences of each language.

I need to incorporate a robust retry mechanism with exponential backoff. If exceptions occur, the script should print an error message. While Supabase-py is comfortable to use, I can also leverage a custom SupabaseClient method for retries. I’ll ensure that the parameters are encoded properly and address how the `in` filter works in PostgREST.

Finally, I’ll implement a script that reads the missing list and outputs files for found IDs, not found IDs, and language distribution. I'll also present a summary in the console. Updating the plan accordingly is essential, and I’ll use the existing missing list path for this task!


Explanation: Query Supabase for the 26k missing-in-cleaned IDs and summarize which exist plus their language distribution.


Output: Plan updated


Summary: auto


I need to implement the next steps. First, I'll load the missing IDs list from the file. I think verifying that it has 26,033 lines is important, so I'll use a command to check the line count, like `wc -l`. This will confirm everything is in order. By running this shell command, I can ensure I'm working with the right data, which helps me avoid any mistakes as I continue with the process.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
26033 reports/r2-diff/missing_in_cleaned_20260113_031622Z.txt


**Updating progress steps**

I've completed step 1, and it's time to mark it as done. Now, I need to move on to step 2, so I'm going to keep that in progress. It feels good to see things moving along! Just need to focus on the next steps and make sure everything is on track. I want to ensure that I'm staying organized and efficient as I tackle each part of the plan. Let's keep this momentum going!


Output: Plan updated


**Planning Supabase queries**

I need to query Supabase, so I’m thinking about creating a script to handle this. An in-line Python snippet could produce a quick summary, but a full script also makes sense. The user asked if I can check if these video IDs are in Supabase and provide summary language metadata for the 26k samples.

I'll write a reusable script in `scripts/`, specifically `supabase_language_summary.py`. We might face issues like RLS on the 'videos' table being disabled, or hitting a 1000-row limit on queries.


**Implementing Supabase query script**

In PostgREST, specifying `in_` with 200 IDs gives me up to 200 rows, which is good. I’m considering whether `supabase-py` might enforce a `range` or `limit`, but it seems not. One thing to note is that I can run into URL length issues, so I’m sticking to a batch size of 200.

I also need to account for hitting a 429 error, so implementing a backoff strategy is a must. I’ll write a script to check video IDs in Supabase and summarize language distribution. I’m thinking about using `httpx` for requests, and I’ll catch exceptions for retries, just in case. The function to load IDs from a file seems fine, especially since it already has unique entries.


**Creating Supabase client and querying**

I’ll start by creating the Supabase client using the URL and key from the environment. If those are missing, I need to raise an error.

Next, I’m preparing to query the Supabase table for the specified IDs in batches. I’ll keep track of found records and maintain counters for language and status. If a query fails, I’ll implement a retry mechanism with an exponential backoff strategy—using a sleep function with a bit of randomness to avoid overwhelming the server.

After getting the results, I’ll manage the language data, ensuring to default to 'unknown' when necessary. Then I can compute the IDs found versus not found and set up for writing outputs to the specified directory.


**Setting up run ID and output paths**

I’ll start by creating a `run_id` based on the current timestamp, although I could also integrate an existing `run_id` parsed from the input file name.

Next, I need to define paths for various outputs: `found_ids_path`, `not_found_ids_path`, `language_distribution_path` as a JSON file, and a summary JSON as well. I also want to calculate distribution percentages, including for missing IDs as 'not_in_supabase,' but I need to focus on the language metadata specifically for those that are in Supabase.

The user asked about the language distribution for the 26k samples, especially those found in Supabase. I'll be ready to output the full language distribution JSON and include counts for those not in Supabase, plus highlight the top languages. To manage speed, considering each request takes about 0.2 seconds, I anticipate around 30 seconds total, updating progress every 25 batches while ensuring not to print the service key. Time to implement this!


**Running the script for quick summary**

To provide a summary for the user quickly, I can run the script and read the output file afterward. I’m thinking it’s efficient to patch this now by adding a new script that helps me achieve that. I'll apply the necessary changes and ensure everything is set up so I can generate the summary effortlessly. This should streamline the process, making it easier to deliver the results to the user promptly!


Input:
*** Begin Patch
*** Add File: scripts/supabase_language_summary.py
+#!/usr/bin/env python3
+"""
+Check which video IDs exist in Supabase and summarize language metadata.
+
+Typical use in this repo:
+  - Feed it a list of YouTube video IDs (one per line).
+  - It queries Supabase `videos` for those IDs in batches.
+  - Outputs:
+      - found_ids.txt
+      - not_found_ids.txt
+      - language_distribution.json
+      - summary.json
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import random
+import sys
+import time
+from collections import Counter
+from dataclasses import asdict, dataclass
+from datetime import datetime, timezone
+from pathlib import Path
+
+REPO_ROOT = Path(__file__).resolve().parent.parent
+sys.path.insert(0, str(REPO_ROOT))
+
+try:
+    from dotenv import load_dotenv
+
+    load_dotenv(dotenv_path=str(REPO_ROOT / ".env"))
+except Exception:
+    pass
+
+from supabase import create_client
+
+
+@dataclass(frozen=True)
+class SupabaseCheckSummary:
+    run_id: str
+    ids_total: int
+    ids_unique: int
+    ids_found: int
+    ids_not_found: int
+    table: str
+    id_column: str
+    language_column: str
+    batch_size: int
+    duration_sec: float
+
+
+def _load_ids(path: Path) -> list[str]:
+    ids: list[str] = []
+    with path.open("r", encoding="utf-8") as f:
+        for line in f:
+            video_id = line.strip()
+            if not video_id or video_id.startswith("#"):
+                continue
+            ids.append(video_id)
+    return ids
+
+
+def _chunks(items: list[str], size: int) -> list[list[str]]:
+    return [items[i : i + size] for i in range(0, len(items), size)]
+
+
+def _write_lines(path: Path, lines: list[str]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", encoding="utf-8") as f:
+        for line in lines:
+            f.write(f"{line}\n")
+
+
+def _retry_sleep_seconds(attempt: int) -> float:
+    # Exponential backoff with jitter, capped.
+    base = 0.75 * (2 ** max(0, attempt - 1))
+    return min(30.0, base + random.random() * 0.5)
+
+
+def main() -> int:
+    p = argparse.ArgumentParser(description="Check Supabase for IDs and summarize language distribution")
+    p.add_argument("--ids-file", required=True, help="Path to newline-delimited video IDs")
+    p.add_argument("--table", default="videos", help="Supabase table (default: videos)")
+    p.add_argument("--id-column", default="youtube_id", help="ID column name (default: youtube_id)")
+    p.add_argument("--language-column", default="language", help="Language column name (default: language)")
+    p.add_argument("--batch-size", type=int, default=200, help="IDs per request (default: 200)")
+    p.add_argument(
+        "--out-dir",
+        default=str(REPO_ROOT / "reports" / "supabase-check"),
+        help='Output directory (default: "reports/supabase-check")',
+    )
+    p.add_argument("--max-attempts", type=int, default=6, help="Retries per batch (default: 6)")
+    p.add_argument("--progress-every", type=int, default=20, help="Progress print every N batches (default: 20)")
+    args = p.parse_args()
+
+    ids_path = Path(args.ids_file)
+    if not ids_path.exists():
+        print(f"ERROR: ids file not found: {ids_path}", file=sys.stderr)
+        return 2
+
+    supabase_url = os.environ.get("URL") or os.environ.get("SUPABASE_URL") or ""
+    supabase_key = os.environ.get("SUPABASE_ADMIN") or ""
+    if not supabase_url or not supabase_key:
+        print("ERROR: Missing Supabase credentials (URL/SUPABASE_URL and SUPABASE_ADMIN).", file=sys.stderr)
+        return 2
+
+    run_id = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%SZ")
+    out_dir = Path(args.out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    all_ids = _load_ids(ids_path)
+    unique_ids = sorted(set(all_ids))
+    if not unique_ids:
+        print("ERROR: No IDs loaded.", file=sys.stderr)
+        return 2
+
+    client = create_client(supabase_url, supabase_key)
+
+    language_counter: Counter[str] = Counter()
+    found_ids: set[str] = set()
+
+    started = time.time()
+    batches = _chunks(unique_ids, max(1, int(args.batch_size)))
+    for batch_index, batch in enumerate(batches, start=1):
+        last_error: Exception | None = None
+        for attempt in range(1, max(1, int(args.max_attempts)) + 1):
+            try:
+                result = (
+                    client.table(args.table)
+                    .select(f"{args.id_column},{args.language_column}")
+                    .in_(args.id_column, batch)
+                    .execute()
+                )
+                rows = result.data or []
+                for row in rows:
+                    video_id = row.get(args.id_column)
+                    if not video_id:
+                        continue
+                    found_ids.add(video_id)
+                    language = row.get(args.language_column) or "unknown"
+                    language = str(language).strip() or "unknown"
+                    language_counter[language] += 1
+                last_error = None
+                break
+            except Exception as e:
+                last_error = e
+                if attempt >= args.max_attempts:
+                    break
+                sleep_s = _retry_sleep_seconds(attempt)
+                print(
+                    f"Warning: Supabase batch {batch_index}/{len(batches)} failed "
+                    f"(attempt {attempt}/{args.max_attempts}): {e}. Retrying in {sleep_s:.1f}s...",
+                    file=sys.stderr,
+                )
+                time.sleep(sleep_s)
+
+        if last_error is not None:
+            print(
+                f"ERROR: Supabase batch {batch_index}/{len(batches)} failed permanently: {last_error}",
+                file=sys.stderr,
+            )
+            return 1
+
+        if args.progress_every and (batch_index % int(args.progress_every) == 0 or batch_index == len(batches)):
+            elapsed = time.time() - started
+            print(
+                f"progress: batches={batch_index}/{len(batches)} found_ids={len(found_ids):,} "
+                f"elapsed={elapsed:.1f}s",
+                flush=True,
+            )
+
+    duration = time.time() - started
+    not_found_ids = sorted(set(unique_ids) - found_ids)
+
+    # Persist outputs.
+    found_path = out_dir / f"found_ids_{run_id}.txt"
+    not_found_path = out_dir / f"not_found_ids_{run_id}.txt"
+    langs_path = out_dir / f"language_distribution_{run_id}.json"
+    summary_path = out_dir / f"summary_{run_id}.json"
+
+    _write_lines(found_path, sorted(found_ids))
+    _write_lines(not_found_path, not_found_ids)
+
+    langs_payload = {
+        "run_id": run_id,
+        "language_column": args.language_column,
+        "languages": dict(language_counter.most_common()),
+    }
+    langs_path.write_text(json.dumps(langs_payload, indent=2) + "\n", encoding="utf-8")
+
+    summary = SupabaseCheckSummary(
+        run_id=run_id,
+        ids_total=len(all_ids),
+        ids_unique=len(unique_ids),
+        ids_found=len(found_ids),
+        ids_not_found=len(not_found_ids),
+        table=args.table,
+        id_column=args.id_column,
+        language_column=args.language_column,
+        batch_size=int(args.batch_size),
+        duration_sec=duration,
+    )
+    summary_path.write_text(json.dumps(asdict(summary), indent=2) + "\n", encoding="utf-8")
+
+    print("\nDONE")
+    print(json.dumps(asdict(summary), indent=2))
+    print(f"\nWrote:\n  {found_path}\n  {not_found_path}\n  {langs_path}\n  {summary_path}")
+
+    # Print a compact top-20 languages table.
+    if language_counter:
+        total_lang_rows = sum(language_counter.values())
+        print("\nTop languages (by row count):")
+        for language, count in language_counter.most_common(20):
+            pct = 100.0 * count / max(1, total_lang_rows)
+            print(f"  {language:>10}  {count:>7,}  ({pct:5.1f}%)")
+
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
+
*** End Patch


Output: {"output":"Success. Updated the following files:\nA scripts/supabase_language_summary.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


python3 scripts/supabase_language_summary.py --ids-file reports/r2-diff/missing_in_cleaned_20260113_031622Z.txt --out-dir reports/r2-diff/supabase --batch-size 200 --progress-every 10


Output:
Exit code: 0
Wall time: 14.1 seconds
Output:
progress: batches=10/131 found_ids=28 elapsed=1.3s
progress: batches=20/131 found_ids=81 elapsed=2.3s
progress: batches=30/131 found_ids=150 elapsed=3.4s
progress: batches=40/131 found_ids=195 elapsed=4.4s
progress: batches=50/131 found_ids=242 elapsed=5.5s
progress: batches=60/131 found_ids=322 elapsed=6.6s
progress: batches=70/131 found_ids=391 elapsed=7.7s
progress: batches=80/131 found_ids=441 elapsed=8.6s
progress: batches=90/131 found_ids=499 elapsed=9.5s
progress: batches=100/131 found_ids=552 elapsed=10.4s
progress: batches=110/131 found_ids=619 elapsed=11.3s
progress: batches=120/131 found_ids=686 elapsed=12.2s
progress: batches=130/131 found_ids=739 elapsed=13.1s
progress: batches=131/131 found_ids=742 elapsed=13.1s

DONE
{
  "run_id": "20260113_034632Z",
  "ids_total": 26033,
  "ids_unique": 26033,
  "ids_found": 742,
  "ids_not_found": 25291,
  "table": "videos",
  "id_column": "youtube_id",
  "language_column": "language",
  "batch_size": 200,
  "duration_sec": 13.144932746887207
}

Wrote:
  reports/r2-diff/supabase/found_ids_20260113_034632Z.txt
  reports/r2-diff/supabase/not_found_ids_20260113_034632Z.txt
  reports/r2-diff/supabase/language_distribution_20260113_034632Z.json
  reports/r2-diff/supabase/summary_20260113_034632Z.json

Top languages (by row count):
      telugu      465  ( 62.7%)
          kn       44  (  5.9%)
          ml       36  (  4.9%)
          pa       32  (  4.3%)
          gu       29  (  3.9%)
     unknown       21  (  2.8%)
          hi       16  (  2.2%)
          ta       14  (  1.9%)
     bengali       13  (  1.8%)
       tamil       13  (  1.8%)
       hindi       12  (  1.6%)
          te       11  (  1.5%)
          or       11  (  1.5%)
          mr        8  (  1.1%)
          bn        5  (  0.7%)
     punjabi        4  (  0.5%)
     kannada        3  (  0.4%)
     marathi        2  (  0.3%)
    assamese        2  (  0.3%)
   malayalam        1  (  0.1%)


**Evaluating missing IDs**

It’s interesting to see that only 742 out of 26,000 missing IDs are present in the Supabase videos table. This suggests that most missing IDs might not have been processed. The user expects to see them all, but the results indicate otherwise. I should double-check since the YouTube ID column might not match up. There could be a table with these raw audio IDs, like a `podcasts` table. Given that they previously synchronized CSV files with Supabase, the videos table should contain all selected IDs. However, it's possible the table holds around 500k records, and many raw audio files may not have been included.


**Analyzing missing IDs in Supabase**

This could explain the situation: there are 26,000 missing IDs in the cleaned data, but they exist in raw audio and weren't selected or inserted into Supabase. The user asked me to check this, and it appears that out of 26,033 raw audio IDs, only 742 are in Supabase, implying the rest were never queued. We did scan all objects under the podcasts directory without suffix filters. We extracted video IDs based on the filename stem and scanned 536,127 objects. It's interesting that every key matched exactly 11 characters. The extraction respects the basename and stem conventions, which should allow accurate ID retrieval even with variations in keys.


**Examining ID extraction results**

I noted that we had 0 skipped keys, which means every key's basename either matched exactly or contained an 11-character substring related to the ID. Since we didn’t set a source suffix filter, we might have included all keys, potentially adding non-audio files that still contained IDs. Scanning the cleaned bucket for `.tar` suffixes revealed 510,094 unique IDs, while we scanned 510,211 objects total. This suggests we have 117 duplicates, possibly due to multiple `.tar` files sharing the same ID substring, leading to extraction collisions.


**Updating plan for missing IDs**

I see that there are 26,033 missing IDs from the source bucket that aren’t found in the cleaned bucket. Many of these might not be in Supabase, so I need to summarize this for the user and possibly output files. We wrote some reports to the reports directory, but I need to update our plan. Step 2, querying, is done. For Step 3, which involves writing the report and summary, that’s already completed. I'll also note that step 3 is in progress until I summarize it for the user. So, I'll mark Step 2 as complete and update the plan to reflect these statuses.


Output: Plan updated


**Evaluating language distribution**

Alright, I’m thinking we should provide more detailed distributions since we’ve already shown the top languages. The user might appreciate a full distribution, so it’s worth summarizing that. I’ll consider how to present a good overview and maybe highlight key languages from the JSON file on language distribution. It seems like a clear path forward to enhance the output for the user. Let’s open the file and get into it!


Output:
Exit code: 0
Wall time: 0 seconds
Output:
{
  "run_id": "20260113_034632Z",
  "language_column": "language",
  "languages": {
    "telugu": 465,
    "kn": 44,
    "ml": 36,
    "pa": 32,
    "gu": 29,
    "unknown": 21,
    "hi": 16,
    "ta": 14,
    "bengali": 13,
    "tamil": 13,
    "hindi": 12,
    "te": 11,
    "or": 11,
    "mr": 8,
    "bn": 5,
    "punjabi": 4,
    "kannada": 3,
    "marathi": 2,
    "assamese": 2,
    "malayalam": 1
  }
}


**Computing language distribution**

I can calculate the percentages for each language among the 742 entries we found. I want to mention the distribution, starting with Telugu at 465 (62.7%). It makes sense to summarize the overall count and highlight the top languages. The user might want to know more about the metadata for these language distributions among the 26k samples. I should clarify that only 742 are in Supabase, with the majority being Telugu. Also, I could note that 25,291 samples weren't found in Supabase.


**Calculating language distribution**

I can compute the counts, noting that there are 25,291 entries not found, which I might refer to as "not in Supabase." This means that out of the total 26,033 samples, 742 were found, making up 2.85%. The user likely wants to see the distribution for these 26k samples, even for those not found. While I can't show that directly, I can highlight that the majority, 97.15%, are "not in Supabase." Here’s a summary of the findings and language distribution for those found.


**Examining language codes and statuses**

I’ve noticed that some languages are presented as codes (like `kn`, `ml`) while others are in full names (such as `telugu`, `bengali`). There are also duplicates, like `hi` for `hindi` and `te` for `telugu`, suggesting inconsistent metadata. I could mention normalizing to ISO codes, even though the user didn't ask. It might be useful to check the statuses of the 742 entries—maybe some are queued or unprocessed. I can quickly compute the statuses for these records as well.


**Considering status distribution**

Since we found 742 entries, the user might appreciate knowing the statuses to decide if they need to rerun any processes. Although they didn't specifically ask for it, including a quick breakdown of status distributions (like PENDING and COMPLETED) could add value. This can clarify why some IDs might not be cleaned, indicating they were not processed or perhaps failed. I can quickly query Supabase to get the statuses for these found IDs. We already have the found IDs list, so I can efficiently manage this in batches.


**Computing distribution and statuses**

The user has 500k completed samples, with 20k missing, and may want clarification on whether the missing samples are those not in Supabase. If they’re not in Supabase, then they haven’t been processed. For the 742 found, I can fetch their statuses and see if they’re pending or failed. I plan to query Supabase for this, potentially modifying the script to include status columns for future use. While the primary focus is on language distribution, I’ll mention that I can check statuses as well if they’re interested later. Now, let's calculate percentages for the found distributions!


python3 - <<'PY'
found=742
total=26033
print(found/total*100)
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
2.850228556063458


Summary: auto


Output: Plan updated


Output:
Exit code: 0
Wall time: 0 seconds
Output:
{
  "run_id": "20260113_034632Z",
  "ids_total": 26033,
  "ids_unique": 26033,
  "ids_found": 742,
  "ids_not_found": 25291,
  "table": "videos",
  "id_column": "youtube_id",
  "language_column": "language",
  "batch_size": 200,
  "duration_sec": 13.144932746887207
}


**Summarizing language metadata distribution**

I need to create a concise final response, perhaps five bullet points, about the language metadata distribution from the 26k samples, highlighting the top languages. Here’s my approach:

- I checked 26,033 IDs from the missing list against Supabase’s `videos.youtube_id`, finding 742 (2.85%) and 25,291 not found.
- The language distribution for the 742 found includes: Telugu (465), Kannada (44), Malayalam (36), Punjabi (32), Gujarati (29), unknown (21), Hindi (16), Tamil (14), Bengali (13), and Tamil (13). The full list is available in JSON.
- I'll output files for found IDs, not found IDs, language distribution, and summary.
- If needed, I can check status distribution for those 742, noting that language values may include both codes and names that could require normalization.


- Checked `reports/r2-diff/missing_in_cleaned_20260113_031622Z.txt:1` (26,033 IDs) against Supabase `videos.youtube_id`: `742` found (2.85%), `25,291` not found (`reports/r2-diff/supabase/summary_20260113_034632Z.json:1`).
- Language distribution for the `742` found (`reports/r2-diff/supabase/language_distribution_20260113_034632Z.json:1`): `telugu=465`, `kn=44`, `ml=36`, `pa=32`, `gu=29`, `unknown=21`, `hi=16`, `ta=14`, `bengali=13`, `tamil=13`, `hindi=12`, `te=11`, `or=11`, `mr=8`, `bn=5`, `punjabi=4`, `kannada=3`, `marathi=2`, `assamese=2`, `malayalam=1`.
- Full lists: `reports/r2-diff/supabase/found_ids_20260113_034632Z.txt:1` and `reports/r2-diff/supabase/not_found_ids_20260113_034632Z.txt:1`.