python3 - <<'PY'
import csv
from collections import Counter
path='reports/r2_audit_completed_20260108T160504Z.csv'
c=Counter()
with open(path, newline='', encoding='utf-8') as f:
    r=csv.DictReader(f)
    for row in r:
        c[row.get('checked_bucket') or ''] += 1
print('bucket_counts')
for b,n in c.most_common():
    print(b or '<empty>', n)
PY


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
bucket_counts
1-cleaned-data 44686


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.


**Checking data parsing**

I’m thinking it's great to have all the cleaned data in one place. I need to check whether there’s anything unusual with the `r2_tar_key` or bucket prefixes. I worry the parser might strip the bucket if it's included, so I’ll want to verify that. Using `parsed_bucket_hint` can help with that. Also, I’m considering counting by `parsed_kind` to see if there are any clues regarding the `bucket_hint`.


python3 - <<'PY'
import csv
from collections import Counter
path='reports/r2_audit_completed_20260108T160504Z.csv'
kind=Counter(); hint=Counter();
with open(path, newline='', encoding='utf-8') as f:
    r=csv.DictReader(f)
    for row in r:
        kind[row.get('parsed_kind') or ''] += 1
        if row.get('parsed_bucket_hint'):
            hint[row['parsed_bucket_hint']] += 1
print('kinds', dict(kind))
print('bucket_hints', hint.most_common()[:10])
PY


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
kinds {'path': 44686}
bucket_hints []


Summary: auto


python3 - <<'PY'
import csv
path='reports/r2_audit_completed_20260108T160504Z.csv'
with open(path, newline='', encoding='utf-8') as f:
    r=csv.DictReader(f)
    total=0
    with_slash=0
    for row in r:
        total+=1
        key=row.get('parsed_key','')
        if '/' in key:
            with_slash+=1
print('total', total)
print('keys_with_slash', with_slash)
PY


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
total 44686
keys_with_slash 691


**Updating progress on steps**

I've got 691 keys with a slash prefix, which should be okay. So, I'm moving forward with the plan. I've completed step 3, and now I'm working on step 4. I'm focusing on getting everything right, making sure each part is handled carefully. It's exciting to see things progressing, and I want to make sure I'm on track for the next stages. Let's keep this momentum going!


Output: Plan updated


**Finalizing step completion**

I’m looking at the report, and it seems like there are no mismatches to summarize. That’s a relief! I'll go ahead and mark step 4 as completed. It feels good to check that off the list, knowing everything is in order. I just want to ensure everything’s correct and clear before moving on, so let’s keep things organized. It's important to keep track of progress!


Output: Plan updated


**Clarifying storage details**

I need to clarify that the "R2 url" corresponds to `r2_tar_key` stored in Supabase, and we checked its existence using S3 head, but it’s not verifying public HTTP access. That seems to be the underlying point they’re getting at. Also, I should mention the earlier count discrepancy. To help, I'll open the summary file to provide a snapshot timestamp for a clearer understanding. Let’s get that reading done!


Output:
Exit code: 0
Wall time: 0 seconds
Output:
completed_total=44686
snapshot_completed_before=2026-01-08T16:05:04.370192+00:00
missing_r2_ref=0
bad_r2_ref=0
exists_in_r2=44686
missing_in_r2=0
head_errors=0
missing_r2_ref_sample=
missing_in_r2_sample=
head_errors_sample=
bad_r2_ref_sample=


- Audited Supabase `videos` where `status='COMPLETED'` (snapshot `processing_completed_at <= 2026-01-08T16:05:04.370192+00:00`).
- Result: `44,686/44,686` completed videos have a non-empty `r2_tar_key` and the referenced object exists in R2 (S3 `HEAD`) in bucket `1-cleaned-data`.
- Report: `reports/r2_audit_completed_20260108T160504Z.csv` and summary: `reports/r2_audit_completed_20260108T160504Z.csv.summary.txt`
- Re-runnable checker script: `scripts/audit_completed_r2.py:1` (run `python3 scripts/audit_completed_r2.py` to re-audit newer completions).