agent-qa-debug-fix

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Debug, patch, and verify failed Agent QA runs from MCP evidence, artifacts, logs, and local code without hiding product or infrastructure defects.

AI & Automation 45,362 stars 6648 forks Updated today MIT

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# 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 1. Start with evidence collection: - `agent_qa_get_run` - `agent_qa_get_run_steps` - `agent_qa_get_run_artifact` - `agent_qa_get_run_logs` - `agent_qa_get_run_execution_logs` 2. Call `agent_qa_classify_failure` and treat its category as a hypothesis, not a verdict. 3. Identify the failing surface: test definition, hook, application under test,...

Details

Author
sickn33
Repository
sickn33/agentic-awesome-skills
Created
7 months ago
Last Updated
today
Language
Python
License
MIT

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