agent-qa-debug-fix
Fixes errors in AI agent tests. It analyzes test results, finds problems, and helps fix the code. Ensures agents work without failures.
What it does
This agent fixes failed program tests. It analyzes evidence from a failed test, like logs and files, and makes the smallest necessary code change to fix the issue, without hiding real defects.
When to use it
- ✓When a program test fails and needs fixing.
- ✓If logs and files indicate an error in the test, program, or environment.
- ✓To verify if a small fix works before running all tests.
Example of what to ask your AI
“Fix the failed test in the staging environment, using the available information.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The agent first collects all information about the failed test. Then, it analyzes this information to understand the cause of the error and makes the smallest possible change to the code or configuration to fix it, without creating new problems.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill agent-qa-debug-fixOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/agent-qa-debug-fix/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from 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.
Fix Rules
- Do not invent selectors, screen states, screenshots, logs, or source files.
- Do not rewrite a test merely to make it pass when the artifact shows a product or runtime defect.
- Preserve canonical Agent QA IDs when editing tests, suites, hooks, or memory files.
- Prefer
agent_qa_validate_test,agent_qa_validate_suite, andagent_qa_validate_definitionbefore rerunning edited YAML. - When MCP is unavailable, use dashboard REST APIs or local
.agent-qaartifacts and state that MCP evidence was unavailable. - Stop and report the blocker when evidence cannot distinguish between materially different fixes.
Example
User: Fix the failed staging checkout run, but do not touch production.
Expected handling: collect the failed run evidence, classify it, inspect the implicated local
definition and application code, patch only the evidenced cause, validate, rerun the single
staging test, and report changed files plus remaining uncertainty.
Limitations
- Requires access to the relevant run evidence and local source; artifacts alone may not establish root cause.
- Cannot guarantee that an intermittent browser, device, network, or provider failure is fixed after one successful rerun.
- Does not authorize production changes, data mutation, dependency installation, or broader refactoring beyond the user's approved scope.
- A passing narrow rerun does not replace the repository's normal test suite or human review.
FAQ
What are 'Agent QA runs'?
These are automated test executions to check if a program is working correctly.
What do 'debug' and 'patch' mean?
'Debug' means finding and fixing errors. 'Patch' means applying a small fix to a program.
Can I use this agent to make big changes to the program?
No, this agent is designed only to make the smallest fix needed for a specific failed test, without altering other parts of the program.
Details
Original description by the author
Debug, patch, and verify failed Agent QA runs from MCP evidence, artifacts, logs, and local code without hiding product or infrastructure defects.
Popularity
- GitHub stars
- 48k
- Ranking position
- #2056
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
More skills from this repository
00-andruia-consultant
Helps your agent start or improve AI projects. It analyzes code, suggests tools, and creates action plans in Spanish.
sickn33/agentic-awesome-skills
007
Lets your agent perform security audits and threat modeling. It's useful for protecting projects against attacks and vulnerabilities.
sickn33/agentic-awesome-skills
10-andruia-skill-smith
Helps your agent create or improve AI tools. It defines how tools work, writes documentation, and validates the structure.
sickn33/agentic-awesome-skills
20-andruia-niche-intelligence
Lets your agent create a summary about a specific industry. It's useful for understanding the context, rules, usage patterns, and risks of a project, using reliable sources.
sickn33/agentic-awesome-skills
Similar skills
agent-architecture-audit
Makes your agent check if everything is working correctly. It finds and fixes problems in AI systems, ensuring they perform as expected.
affaan-m/ECC
agent-eval
Helps your agent compare different AI coding tools. It measures which one is faster, cheaper, and more reliable for specific tasks.
affaan-m/ECC
agent-harness-construction
Makes your agent better understand how to perform tasks. It optimizes the tools and instructions the agent uses to increase its chances of success.
affaan-m/ECC