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agent-qa-debug-fix

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

Install / Use

npx skills add sickn33/agentic-awesome-skills --skill agent-qa-debug-fix

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

92/100

Category

Operations

Supported Platforms

Claude Code
Gemini CLI
Cursor
OpenAI Codex

Our assessment of agent-qa-debug-fix

agent-qa-debug-fix scores 92/100 on our quality scale, 173rd of 633 Operations skills we index (top 28%).

Its SKILL.md is 4.0 KB long, well organised into 8 sections with 1 code example: a solid amount of guidance for an agent.

With 46,875 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
17/20
Description
15/15
Adoption
20/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 7 days ago, so agent-qa-debug-fix is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

agent-qa-debug-fix compared with similar skills

All 4 of these similar skills score higher than agent-qa-debug-fix; compare them before choosing.

SkillScoreStarsUpdatedFormat
agent-qa-debug-fix (this skill)by sickn339246.9k7d agoSKILL.md
Agent-Reachby Panniantong10087.5k16d agoCLAUDE.md
headroomby headroomlabs-ai10074.2ktodayCLAUDE.md
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Frequently asked questions

How do I install agent-qa-debug-fix?
Run npx skills add sickn33/agentic-awesome-skills --skill agent-qa-debug-fix. The install tabs above show the steps for each supported agent.
Which AI agents does agent-qa-debug-fix work with?
It is written for Claude Code, Gemini CLI, Cursor and OpenAI Codex, as a SKILL.md file. Other agents that read the same format can often use it too.
Is agent-qa-debug-fix safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is agent-qa-debug-fix still maintained?
The repository was last updated 7 days ago, so agent-qa-debug-fix is actively maintained.

name: agent-qa-debug-fix description: "Debug, patch, and verify failed Agent QA runs from MCP evidence, artifacts, logs, and local code without hiding product or infrastructure defects." category: testing risk: critical source: https://github.com/vostride/agent-qa/tree/main/skills/agent-qa-debug-fix source_repo: vostride/agent-qa source_type: official date_added: "2026-08-16" author: Vostride tags: [testing, qa, debugging, mcp, self-healing] tools: [claude, cursor, gemini, codex] license: FSL-1.1-ALv2 license_source: https://github.com/vostride/agent-qa/blob/main/LICENSE.md

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, runtime infrastructure, or agent behavior.
  4. Inspect the relevant local files directly. Do not infer patches from artifacts alone.
  5. Explain the evidence-to-change link, then apply the smallest code or YAML change that accounts for the evidence.
  6. Validate any changed Agent QA definition before execution.
  7. Re-run the narrowest affected Agent QA test, suite, hook, or unit test within the approved environment.
  8. 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, and agent_qa_validate_definition before rerunning edited YAML.
  • When MCP is unavailable, use dashboard REST APIs or local .agent-qa artifacts 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.

Related Skills

View on GitHub
GitHub Stars46.9k
CategoryOperations
Updated7d ago
Forks6.8k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions
agent-qa-debug-fix — Claude Code Skill: Install & Safety Check | SkillAgent