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recovering-from-errors

recovering-from-errors is a skill for Claude Code from oxbshw/watch-skill. It costs 69 tokens per session (397 once invoked), scanned A, original, MIT.

An error-recovery guide for Watch Skill, a tool that examines videos. It explains how to diagnose failed downloads, missing programs, provider errors, and local vision problems.

In plain words
What is it for?
Use it to check and repair Watch Skill, then retry one failed video operation. It covers broken downloads, missing ffmpeg or yt-dlp, invalid vision models, authentication problems, and unknown video IDs.
Why use it?
It replaces vague failure messages with the reported error, its cause, and a specific repair step. It also prevents endless retries or silently changing services.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the watch-skill plugin — 10 skills, 1 command, 1 MCP server shipped together

not rated 555repo +14 changed 1mo ago A scan Socket: passSnyk: passSkillSpector: warn 69 tokens original MIT

Good fit Use it to check and repair Watch Skill, then retry one failed video operation. It covers broken downloads, missing ffmpeg or yt-dlp, invalid vision models, authentication problems, and unknown video IDs.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/oxbshw/watch-skill/recovering-from-errors
View source ↗ oxbshw/watch-skill pypi.org
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add oxbshw/watch-skill --skill recovering-from-errors
Clone the repo
git clone --depth 1 https://github.com/oxbshw/watch-skill

Made for: Claude Code.

Or install watch-skill, the plugin that ships this one along with the rest of its 10 skills, 1 command, 1 MCP server.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for recovering-from-errors

README.md
[![agentmods](https://agentmods.dev/badge/skills/oxbshw/watch-skill/recovering-from-errors/github.svg)](https://agentmods.dev/skills/oxbshw/watch-skill/recovering-from-errors)
Your own site
<a href="https://agentmods.dev/skills/oxbshw/watch-skill/recovering-from-errors"><img src="https://agentmods.dev/badge/skills/oxbshw/watch-skill/recovering-from-errors/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for recovering-from-errors

Your own site · 80×15
<a href="https://agentmods.dev/skills/oxbshw/watch-skill/recovering-from-errors"><img src="https://agentmods.dev/badge/skills/oxbshw/watch-skill/recovering-from-errors.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 69 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 397 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
  • Socket pass 9 Sept 2026
  • Snyk pass 9 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 28
    Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.
    Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00069 $0.00397
Opus 5.5 $0.00028 $0.00159
Sonnet 5.5 $0.00014 $0.00079
Haiku 4.5 $0.00007 $0.00040

Measured 1mo ago against content hash b179302c903f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

recovering-from-errors scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/recovering-from-errors/SKILL.md · 42 lines

What it actually says

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.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago Changed b179302c903f
  2. 1mo ago Changed d496f8b09a8b
  3. 2mo ago Changed 433340bb904f
  4. 2mo ago First seen · 42 lines · 69 tokens per session scan A 2cee23836650

Subscribe to this mod's changes

recovering-from-errors is a skill published in the GitHub repository oxbshw/watch-skill (555 stars, last pushed 11d ago), licensed MIT. It adds 69 tokens to every session and 397 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

DebuggingMultimodalai-agentscomputer-visionmcpocrvideo-understanding
Related

recovering-from-errors compared with skills from other repositories

recovering-from-errors next to 6 skills from other repositories
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recovering-from-errorsoxbshw/watch-skillSkill55511d agoMITA69Claude Code
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profile-vllm-performanceNVIDIA-AI-Blueprints/video-search-and-summarizationSkill2.0kyesterdayApache-2.0A93Claude Code, Codex
screenpipe-workflow-maintenancescreenpipe/screenpipeSkill22kyesterdaynoneA22Claude Code, Codex
render-html-reportscreenpipe/screenpipeSkill22kyesterdaynoneA74Claude Code, Codex