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.
npx skills add oxbshw/watch-skill --skill recovering-from-errorsgit clone --depth 1 https://github.com/oxbshw/watch-skillWrote 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.
[](https://agentmods.dev/skills/oxbshw/watch-skill/recovering-from-errors)<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="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
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.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00069 | $0.00397 |
| Opus 5 | $0.00034 | $0.00198 |
| Sonnet 5 | $0.00014 | $0.00079 |
| Haiku 4.5 | $0.00007 | $0.00040 |
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 today.
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.
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_KEYlocally; - provider model not found: run
watch-skill setup-visionwith 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.
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.
- today Changed 433340bb904f
- 8d ago First seen · 42 lines · 69 tokens per session scan A 2cee23836650
recovering-from-errors is a skill published in the GitHub repository oxbshw/watch-skill (337 stars, last pushed today), licensed MIT. It adds 69 tokens to every session and 397 once invoked, about $0.0003 per session on Opus 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.
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