agent-qa-debug-fix
FeaturedDebug, 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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