SuperAPI AI Directory

recovering-from-errors

Design & creation

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Updated 10-16 03:14

About this skill

Recovering from errors

Failures use stable error codes and include an actionable fix. Preserve that evidence; do not replace it with a generic explanation.

Diagnose first

watch-skill doctor --json

Read the failing checks and apply only their stated fixes. doctor can repair managed ffmpeg and yt-dlp binaries, stale locks, corrupt caches, missing frame directories, and known local-model health failures. Re-run it once after remediation.

Retry the original operation once

After doctor is green, repeat the command that failed. If it returns another structured error, report its code, message, and fix. Do not loop indefinitely, silently switch cloud providers, enable cloud STT, or reprocess an already indexed video.

Common routes:

  • acquisition or extractor failure: watch-skill doctor --json, then retry the watch;
  • provider authentication: verify the matching WATCHSKILL_*_API_KEY locally;
  • provider model not found: run watch-skill setup-vision with a valid vision model;
  • low memory or local server failure: reduce the local model/context or select a cloud provider the user already has;
  • unknown video ID: watch-skill list, then use the listed ID or original source.

Security and cost policy are invariants during recovery. Never upload the video file, enable a paid provider, or raise the cost ceiling merely to make an error disappear.