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 JPeetz/agent-skills --skill debugging-and-error-recoverygit clone --depth 1 https://github.com/JPeetz/agent-skillsWrote 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/jpeetz/agent-skills/debugging-and-error-recovery)<a href="https://agentmods.dev/skills/jpeetz/agent-skills/debugging-and-error-recovery"><img src="https://agentmods.dev/badge/skills/jpeetz/agent-skills/debugging-and-error-recovery/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.
<a href="https://agentmods.dev/skills/jpeetz/agent-skills/debugging-and-error-recovery"><img src="https://agentmods.dev/badge/skills/jpeetz/agent-skills/debugging-and-error-recovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00064 | $0.02702 |
| Opus 5.5 | $0.00026 | $0.01081 |
| Sonnet 5.5 | $0.00013 | $0.00540 |
| Haiku 4.5 | $0.00006 | $0.00270 |
Grade A, and why
debugging-and-error-recovery 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 24d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 24d ago First seen · 306 lines · 64 tokens per session scan A 95d4cfdc2402
debugging-and-error-recovery is a skill published in the GitHub repository JPeetz/agent-skills (14 stars, last pushed yesterday), with no licence file. It adds 64 tokens to every session and 2,702 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 2026-09-22.
Other skills, from other repositories
qa-knowledge
To run QA engineering — requirements/gap analysis, scenario & spec design, test implementation, failure triage — over the QA knowledge base.
shisan-xinuo-roles
A set of defined agent roles for reviewing and carrying out software work, such as debugging, security checks, testing, risk review, contract checks, frontend review, and performance investigation.
ralphctl-debugging-and-error-recovery
Systematic root-cause debugging. Use when tests fail, builds break, something that worked before broke, or behaviour does not match expectations — when you need to figure out what broke and why. Follow stop-the-line → reproduce → localize → reduce → root-cause → guard-with-regression-test → verify, not guessing; the…
fix-bug
Fixes a bug through test-driven debugging, reproduces it with a failing test, locates the root cause with evidence (file:line), then applies the smallest fix that resolves it without refactoring unrelated code. Use when the user wants to fix a bug, debug an issue, resolve an error, or investigate a failing test. Not…
bug-fix
Guided end-to-end bug-fix workflow for Plan Forge tempering bugs — load → pre-fix review → write failing test → fix → validate → post-fix sweep → close. Composes /code-review, /clean-code-review, /forge-quench, and /test-sweep around the forgebug tool surface so a fix never closes without a regression check.
audit-loop
Run a recursive audit drain loop — discover findings from the running system, triage each into bug/spec/classifier lanes, repeat until convergence. USE FOR: end-to-end audit of a deployed or locally-running app, draining findings to zero. DO NOT USE FOR: single-shot tempering runs (use forgetemperingrun), one-off bug…