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 h4vzz/awesome-ai-agent-skills --skill debugginggit clone --depth 1 https://github.com/h4vzz/awesome-ai-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/h4vzz/awesome-ai-agent-skills/debugging)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/debugging"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/debugging/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/h4vzz/awesome-ai-agent-skills/debugging"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/debugging.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 | $0.00027 | $0.02257 |
| Opus 5 | $0.00014 | $0.01128 |
| Sonnet 5 | $0.00005 | $0.00451 |
| Haiku 4.5 | $0.00003 | $0.00226 |
Grade A, and why
Debugging 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 9d 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.
This is a copy
100% identical to debugging — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugging
This skill equips an AI agent with a systematic methodology for diagnosing and resolving software bugs. Rather than guessing at fixes, the agent follows a structured process — reproduce, isolate, diagnose, fix, verify — to find root causes and produce reliable corrections. It handles a wide range of bug categories including logic errors, runtime exceptions, race conditions, memory leaks, and performance regressions across multiple languages and runtime environments.
Workflow
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Reproduce the problem. Confirm the bug is observable and repeatable. Gather the exact error message, stack trace, log output, or description of unexpected behavior. Identify the minimum input or sequence of steps that triggers the issue. If the bug is intermittent, note the frequency and any environmental conditions (load, timing, specific data) that correlate with its appearance.
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Isolate the fault location. Use the stack trace, error message, and code structure to narrow down the region of code responsible. Trace data flow backward from the point of failure to find where the value diverged from expectations. Eliminate unrelated code paths by checking whether the bug persists when components are stubbed out or bypassed. For large codebases, use binary search strategies — disable half the system, check if the bug still occurs, and repeat.
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Diagnose the root cause. Once the faulty region is identified, determine exactly why the code misbehaves. Common root causes include: incorrect assumptions about input (null, empty, out-of-range), state mutation from a concurrent thread, stale cache or memoized value, incorrect operator precedence, missing await on an async call, or a dependency version incompatibility. Distinguish the root cause from its symptoms — a NullPointerException is a symptom; the root cause may be a missing validation three function calls earlier.
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Develop and apply the fix. Write the smallest change that addresses the root cause without introducing side effects. If the fix involves changing a shared interface, trace all callers to ensure compatibility. Prefer defensive fixes that handle the error class broadly (e.g., adding input validation) over narrow patches that only address the single observed failure.
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.
- 9d ago First seen · 190 lines · 27 tokens per session scan A d4f7eb060e0e
Debugging is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (34 stars, last pushed today), licensed MIT. It adds 27 tokens to every session and 2,257 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to debugging, differing in 4 lines, and is treated as a copy.
Other skills, from other repositories
openclaw-debugger
Trigger when an OpenClaw agent is broken, silent, crashing, stuck, not responding, returning empty output, or the user says "my agent is down", "agent not working", "/openclaw-debugger". Walks through the standard OpenClaw 2026.4 diagnosis checklist and prints a report.
eslint
Skill "eslint" from ashish7802/awesome-api-skills, covering eslint skill, ecosystem graph preview, recommended next skills, quick start and production patterns.
jaeger
Skill "jaeger" from ashish7802/awesome-api-skills, covering jaeger skill, ecosystem graph, quick start, production patterns and trace sampling.
loki
Skill "loki" from ashish7802/awesome-api-skills, covering loki skill, ecosystem graph, quick start, production patterns and logql vs full text.
sentry
Sentry tracks exceptions, traces, and crashes. This skill covers the @sentry/node integration for backend error capturing and the Sentry REST API for release management.
Issue Triage
Turn messy bug reports into actionable tasks and reproduction steps.