instructions:
## Skills
These skills are discovered at startup from multiple local sources. Each entry includes a name, description, and file path so you can open the source for full instructions.
- 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 $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)
- Discovery: Available skills are listed in project docs and may also appear in a runtime "## Skills" section (name + description + file path). These are the sources of truth; skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description, 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.
- Description as trigger: The YAML `description` in `SKILL.md` is the primary trigger signal; rely on it to decide applicability. If unsure, ask a brief clarification before proceeding.
- 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 deeply nested references; prefer one-hop files explicitly linked from `SKILL.md`.
  - 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.

source: vscode

model_provider: openai


type: message

role: user

text:
# AGENTS.md instructions for /home/ubuntu/maya3

<INSTRUCTIONS>
## Skills
These skills are discovered at startup from multiple local sources. Each entry includes a name, description, and file path so you can open the source for full instructions.
- 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 $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)
- Discovery: Available skills are listed in project docs and may also appear in a runtime "## Skills" section (name + description + file path). These are the sources of truth; skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description, 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.
- Description as trigger: The YAML `description` in `SKILL.md` is the primary trigger signal; rely on it to decide applicability. If unsure, ask a brief clarification before proceeding.
- 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 deeply nested references; prefer one-hop files explicitly linked from `SKILL.md`.
  - 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.
</INSTRUCTIONS>


text:
<environment_context>
  <cwd>/home/ubuntu/maya3</cwd>
  <approval_policy>on-request</approval_policy>
  <sandbox_mode>workspace-write</sandbox_mode>
  <network_access>restricted</network_access>
  <shell>bash</shell>
</environment_context>


text:
<environment_context>
  <sandbox_mode>workspace-write</sandbox_mode>
  <network_access>restricted</network_access>
  <writable_roots>
    <root>/home/ubuntu/maya3</root>
  </writable_roots>
  <shell>bash</shell>
</environment_context>


text:
context from previous agent.

✅ TEST SUMMARY

Docker Build

Docker not available in this environment
All prerequisites validated (Python, dependencies, GPU access)
Pipeline Test Results

Test	Status	Details
Python imports	✅ Pass	torch, supabase, r2, analytics, export all work
GPU detection	✅ Pass	8× GPUs detected (torch 2.8.0+cu128)
Supabase connection	✅ Pass	171,945 PENDING videos in queue
R2 connection	✅ Pass	Source and Production buckets accessible
Environment validation	✅ Pass	All required env vars present
Single video processing	✅ Pass	OBMrIJI_dn0 processed in 34.8s
Multi-GPU workers	✅ Pass	8 workers running in parallel
Analytics	✅ Pass	Workers reporting to Supabase
TAR creation	✅ Pass	Segments + metadata only (no _original.flac)
Fix Applied

Fixed _original.flac removal in synchronous export path (pipeline.py:1021-1026)

TAR Contents (After Fix)

{video_id}/
├── metadata.json # Full processing metadata
└── segments/ # Individual speaker clips (FLAC)
├── SPEAKER_00_0000_xxx.flac
├── SPEAKER_00_0001_xxx.flac
└── ...

Ready for Deployment

✅ Codebase tested and working
✅ Supabase queue functional (171K+ videos pending)
✅ R2 upload ready
✅ Deployment scripts created
✅ Monitoring tools ready
When you return with SSH keys, run:

1. Edit cluster config
vi scripts/deploy/cluster_config.json

2. Deploy to all clusters
python scripts/deploy/deploy_all.py --deploy

3. Start workers
python scripts/deploy/deploy_all.py --start

4. Monitor
python scripts/monitor/dashboard.py --watch

And can you check the documenation of vast ai either using SDK or cli from vast_ai.md and vast_ai_sdk.md and give me a highlevel overview of how we're going to deploy the application into multiple clusters at vast ai. If you need me to add SSH key into vast ai's console, give instructions for that and then by default it shall be added to any machines we create. and then create plan for creating instances of 24-48GB with high reliability, and is multiple of 8s as a cluster and is cheaper than 4$/hr as a cluster in total. And we can choose anything like 3090s, 4090s, or anything similar from 24GB to 48GB and is the entire cluster is less than 4$/hr in total.

and do you think it's better if we cerate a docker image and deploy the workflow or should we directly depend on teh scripts at deploy_all.py scripts and is it reliable. i'm worried hte new clusters might have different versions and environments. so it's better if we create a docker image and bake all the models that we're gonna use into it. and we just deploy the docker image and it works seamlessly.

Vast.ai Multi-Cluster Deployment (High Level)

Vast.ai runs each “machine” as a Docker container; we deploy by launching multiple identical instances and running massive_process.py workers on each (they coordinate via Supabase queue + R2, so no inter-node networking needed).
Provision via CLI (vastai search offers → vastai create instance) or Python SDK (VastAI.search_offers() / launch_instance() in vast_ai_sdk.md (line 105)), then label/group instances and monitor with logs/analytics.
SSH Key (So New Instances Get It Automatically)

Generate a key: ssh-keygen -t ed25519 -C "you@example.com" -f ~/.ssh/maya3_vast (vast_ai.md (line 286)).
Add it to your Vast account: maya3_vast.pub)" and verify with vastai show ssh-keys (vast_ai.md (line 294)).
Important: keys only auto-apply to instances created after you add them (vast_ai.md (line 331)); for existing instances use vastai attach ssh <instance_id> (vast_ai.md (line 367)).
Plan: 24–48GB GPUs, “clusters” in 8s, < $4/hr total

Pick cluster shape: either 1×8-GPU instance (simpler ops) or 8×1-GPU instances (often easier to source); budget is sum(dph_total) across the instances.
Search offers (examples; tune thresholds):
8-GPU node: vastai search offers --type on-demand 'num_gpus=8 gpu_ram>=24 gpu_ram<=48 reliability>0.99 verified=true rentable=true dph_total<4 gpu_name=RTX_4090' -o dph_total (vast_ai.md (line 45))
1-GPU nodes: same but num_gpus=1 dph_total<0.5, then create 8 of them.
Standardize with a template so every instance uses the same image/env/onstart (vast_ai.md (line 578)), then create N instances and label them maya3-cluster-01, maya3-cluster-02, etc.
Expectation: 48GB-per-GPU options are much less likely to fit <$4/hr for 8 GPUs; 24GB (3090/4090-class) is the realistic target.
Docker Image vs deploy_all.py

For reliability, prefer a pinned Docker image + template: the repo already has a production-ish container (Dockerfile (line 13)) that installs deps into .venv and runs massive_process.py as the container entrypoint (Dockerfile (line 72)), which avoids “different machine, different env” drift.
Current SSH deploy scripts (deploy_all.py (line 36), deploy_cluster.sh (line 21)) assume ssh user@host with no port; Vast.ai SSH is typically host+port (vastai ssh-url <id>), so you’ll either need a small patch to support ports or lean into container-native ops (start/stop/recycle + vastai logs <id>).
Do you want a “cluster” to be one 8-GPU instance or eight 1-GPU instances, and are bid/interruptible instances acceptable or on-demand only?

Can you build docker image and serve it locally and test first in this machine, before serving & scaling it to other systems.

Docker can’t be built/tested in this environment: docker (and podman/buildah/nerdctl) isn’t installed, so there’s no local container runtime to run docker build/docker run.

What I did to make local Docker testing reliable/safe on your GPU machine:

Added .dockerignore to prevent baking secrets like .env into the image (and to shrink build context): .dockerignore (line 1)
Added a one-command local build + smoke-test script: smoke_test.sh (line 1)
Documented usage in README.md (line 47)
On a machine with Docker + NVIDIA Container Toolkit:

Build + basic smoke tests (GPU + imports + Supabase + R2): smoke_test.sh
End-to-end single-video test (claims 1 Supabase PENDING video on GPU 0, verifies TAR has no _original.flac): smoke_test.sh
“Serve” locally (run workers continuously): docker compose up -d (logs: docker compose logs -f)

Can you continue with docker testing from this machine, so we can safely proceed with next steps of vhoosing machines. For now, build docker, run the docker application, monitor analytics, then reset everything and be ready so that we can surely deploy and monitor in vast ai


context from previous agent.

✅ TEST SUMMARY

Docker Build

Docker not available in this environment
All prerequisites validated (Python, dependencies, GPU access)
Pipeline Test Results

Test	Status	Details
Python imports	✅ Pass	torch, supabase, r2, analytics, export all work
GPU detection	✅ Pass	8× GPUs detected (torch 2.8.0+cu128)
Supabase connection	✅ Pass	171,945 PENDING videos in queue
R2 connection	✅ Pass	Source and Production buckets accessible
Environment validation	✅ Pass	All required env vars present
Single video processing	✅ Pass	OBMrIJI_dn0 processed in 34.8s
Multi-GPU workers	✅ Pass	8 workers running in parallel
Analytics	✅ Pass	Workers reporting to Supabase
TAR creation	✅ Pass	Segments + metadata only (no _original.flac)
Fix Applied

Fixed _original.flac removal in synchronous export path (pipeline.py:1021-1026)

TAR Contents (After Fix)

{video_id}/
├── metadata.json # Full processing metadata
└── segments/ # Individual speaker clips (FLAC)
├── SPEAKER_00_0000_xxx.flac
├── SPEAKER_00_0001_xxx.flac
└── ...

Ready for Deployment

✅ Codebase tested and working
✅ Supabase queue functional (171K+ videos pending)
✅ R2 upload ready
✅ Deployment scripts created
✅ Monitoring tools ready
When you return with SSH keys, run:

1. Edit cluster config
vi scripts/deploy/cluster_config.json

2. Deploy to all clusters
python scripts/deploy/deploy_all.py --deploy

3. Start workers
python scripts/deploy/deploy_all.py --start

4. Monitor
python scripts/monitor/dashboard.py --watch

And can you check the documenation of vast ai either using SDK or cli from vast_ai.md and vast_ai_sdk.md and give me a highlevel overview of how we're going to deploy the application into multiple clusters at vast ai. If you need me to add SSH key into vast ai's console, give instructions for that and then by default it shall be added to any machines we create. and then create plan for creating instances of 24-48GB with high reliability, and is multiple of 8s as a cluster and is cheaper than 4$/hr as a cluster in total. And we can choose anything like 3090s, 4090s, or anything similar from 24GB to 48GB and is the entire cluster is less than 4$/hr in total.

and do you think it's better if we cerate a docker image and deploy the workflow or should we directly depend on teh scripts at deploy_all.py scripts and is it reliable. i'm worried hte new clusters might have different versions and environments. so it's better if we create a docker image and bake all the models that we're gonna use into it. and we just deploy the docker image and it works seamlessly.

Vast.ai Multi-Cluster Deployment (High Level)

Vast.ai runs each “machine” as a Docker container; we deploy by launching multiple identical instances and running massive_process.py workers on each (they coordinate via Supabase queue + R2, so no inter-node networking needed).
Provision via CLI (vastai search offers → vastai create instance) or Python SDK (VastAI.search_offers() / launch_instance() in vast_ai_sdk.md (line 105)), then label/group instances and monitor with logs/analytics.
SSH Key (So New Instances Get It Automatically)

Generate a key: ssh-keygen -t ed25519 -C "you@example.com" -f ~/.ssh/maya3_vast (vast_ai.md (line 286)).
Add it to your Vast account: maya3_vast.pub)" and verify with vastai show ssh-keys (vast_ai.md (line 294)).
Important: keys only auto-apply to instances created after you add them (vast_ai.md (line 331)); for existing instances use vastai attach ssh <instance_id> (vast_ai.md (line 367)).
Plan: 24–48GB GPUs, “clusters” in 8s, < $4/hr total

Pick cluster shape: either 1×8-GPU instance (simpler ops) or 8×1-GPU instances (often easier to source); budget is sum(dph_total) across the instances.
Search offers (examples; tune thresholds):
8-GPU node: vastai search offers --type on-demand 'num_gpus=8 gpu_ram>=24 gpu_ram<=48 reliability>0.99 verified=true rentable=true dph_total<4 gpu_name=RTX_4090' -o dph_total (vast_ai.md (line 45))
1-GPU nodes: same but num_gpus=1 dph_total<0.5, then create 8 of them.
Standardize with a template so every instance uses the same image/env/onstart (vast_ai.md (line 578)), then create N instances and label them maya3-cluster-01, maya3-cluster-02, etc.
Expectation: 48GB-per-GPU options are much less likely to fit <$4/hr for 8 GPUs; 24GB (3090/4090-class) is the realistic target.
Docker Image vs deploy_all.py

For reliability, prefer a pinned Docker image + template: the repo already has a production-ish container (Dockerfile (line 13)) that installs deps into .venv and runs massive_process.py as the container entrypoint (Dockerfile (line 72)), which avoids “different machine, different env” drift.
Current SSH deploy scripts (deploy_all.py (line 36), deploy_cluster.sh (line 21)) assume ssh user@host with no port; Vast.ai SSH is typically host+port (vastai ssh-url <id>), so you’ll either need a small patch to support ports or lean into container-native ops (start/stop/recycle + vastai logs <id>).
Do you want a “cluster” to be one 8-GPU instance or eight 1-GPU instances, and are bid/interruptible instances acceptable or on-demand only?

Can you build docker image and serve it locally and test first in this machine, before serving & scaling it to other systems.

Docker can’t be built/tested in this environment: docker (and podman/buildah/nerdctl) isn’t installed, so there’s no local container runtime to run docker build/docker run.

What I did to make local Docker testing reliable/safe on your GPU machine:

Added .dockerignore to prevent baking secrets like .env into the image (and to shrink build context): .dockerignore (line 1)
Added a one-command local build + smoke-test script: smoke_test.sh (line 1)
Documented usage in README.md (line 47)
On a machine with Docker + NVIDIA Container Toolkit:

Build + basic smoke tests (GPU + imports + Supabase + R2): smoke_test.sh
End-to-end single-video test (claims 1 Supabase PENDING video on GPU 0, verifies TAR has no _original.flac): smoke_test.sh
“Serve” locally (run workers continuously): docker compose up -d (logs: docker compose logs -f)

Can you continue with docker testing from this machine, so we can safely proceed with next steps of vhoosing machines. For now, build docker, run the docker application, monitor analytics, then reset everything and be ready so that we can surely deploy and monitor in vast ai


approval_policy: on-request

type: workspace-write

effort: medium

summary: auto