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 lingxling/awesome-skills-cn --skill agent-qa-debug-fixgit clone --depth 1 https://github.com/lingxling/awesome-skills-cnWrote 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/lingxling/awesome-skills-cn/agent-qa-debug-fix)<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/agent-qa-debug-fix"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/agent-qa-debug-fix/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/lingxling/awesome-skills-cn/agent-qa-debug-fix"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/agent-qa-debug-fix.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 | $0.00035 | $0.00885 |
| Opus 5.5 | $0.00014 | $0.00354 |
| Sonnet 5.5 | $0.00007 | $0.00177 |
| Haiku 4.5 | $0.00003 | $0.00089 |
Grade A, and why
agent-qa-debug-fix 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 agent-qa-debug-fix — 0 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent QA Debug Fix
Overview
Repair a failed Agent QA run from recorded evidence and the relevant local source. Treat the classifier as a hypothesis, make the smallest justified change, and verify the narrowest affected behavior without rewriting a test merely to conceal a real defect.
When to Use
- A failed Agent QA run has already been triaged and now requires a code or YAML repair.
- Artifacts and logs point to a test, hook, product, runtime, or agent-behavior defect.
- A proposed fix must be verified with the narrowest Agent QA or unit-test rerun.
- The user asks to self-heal or update a stale Agent QA definition from evidence.
Preconditions and Approval Boundary
- Confirm the repository, workspace, target environment, and files the user authorizes you to modify.
- Inspect the planned test's external side effects before rerunning it; obtain explicit confirmation for production-facing, destructive, or irreversible actions.
- Preserve unrelated user changes and keep the patch limited to the evidenced failure.
- Do not expose credentials or sensitive application data from artifacts and logs.
Workflow
- Start with evidence collection:
agent_qa_get_runagent_qa_get_run_stepsagent_qa_get_run_artifactagent_qa_get_run_logsagent_qa_get_run_execution_logs
- Call
agent_qa_classify_failureand treat its category as a hypothesis, not a verdict. - Identify the failing surface: test definition, hook, application under test, runtime infrastructure, or agent behavior.
- Inspect the relevant local files directly. Do not infer patches from artifacts alone.
- Explain the evidence-to-change link, then apply the smallest code or YAML change that accounts for the evidence.
- Validate any changed Agent QA definition before execution.
- Re-run the narrowest affected Agent QA test, suite, hook, or unit test within the approved environment.
- Report the root cause, changed files, verification command or MCP action, result, and remaining risk.
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 · 78 lines · 35 tokens per session scan A a3faa542300c
agent-qa-debug-fix is a skill published in the GitHub repository lingxling/awesome-skills-cn (310 stars, last pushed 9d ago), licensed MIT. It adds 35 tokens to every session and 885 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to agent-qa-debug-fix, differing in 0 lines, and is treated as a copy.
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