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Video ingestion and transcript workflow with yt-dlp, ffmpeg, caption extraction, and budget-aware analysis.
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Based on SOC occupation classification
Pre-code system configuration for any AI agent. Detects model, applies token optimization, estimates input tokens, compresses context, calculates real-time savings (to the token), and tracks cost delta before coding starts. Works with all 11 models. Agent runs this BEFORE writing code—saves 75-95% tokens + cost on every project. Triggers: "before i code", "set up token optimization", "configure system", "what model am i", "estimate tokens", "how much will this save", "show me savings", "pre-code checklist", "token tracker", "cost me to code this"
Publish agent capabilities and accept Agent2Agent task requests through the local HTTP receiver.
Verify that a Vercel deployment URL displays a real page instead of a 404, auth wall, blank shell, or error page.
Verify and normalize GitHub webhook payloads for deployment automation. Use when a GitHub push, deployment_status, or workflow_run event arrives.
Iterate deployment visibility failures using only no-code-safe actions and stop when source-code changes are required.
Inventory GitHub repositories and Vercel projects, then map repos to deployments for a visibility sweep.
| name | pauli-video-watch |
| description | Video ingestion and transcript workflow with yt-dlp, ffmpeg, caption extraction, and budget-aware analysis. |
| version | 1.0.0 |
| required_environment_variables | [] |
Use for transcript extraction, frame sampling, or low-cost video analysis.
Do not run network-heavy or paid transcription workflows by default in CI.
