agent-qa-debug-fix

agent-qa-debug-fix is a skill for Claude Code from sickn33/agentic-awesome-skills. It costs 35 tokens per session (885 once invoked), scanned A, original, MIT.

A guide for fixing failed Agent QA runs, which are automated checks of an AI agent. It connects recorded evidence with local source code, then supports a small code or YAML change followed by a focused verification run.

In plain words
What is it for?
Use it to debug and patch failed checks, repair test definitions, and rerun the narrow behavior affected by a fix.
Why use it?
It helps separate real product or infrastructure defects from test problems, so tests are not changed just to hide failures. It also limits changes to what the evidence supports.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions Codex.

Part of the agentic-awesome-skills plugin — 196 skills shipped together

Good fit Use it to debug and patch failed checks, repair test definitions, and rerun the narrow behavior affected by a fix.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/sickn33/agentic-awesome-skills/agent-qa-debug-fix
About the project

AAS Core is a local control plane for coding agents that lets them search a large catalogue of skills, choose a stack, validate it, and create a reproducible plan. It is used to assemble and review agent workflows through its CLI, local MCP server, catalogue, plugins, and Workbench. The catalogue add-ons provide the skills, plugins, bundles, and workflows that AAS Core helps agents select and validate.

sickn33/agentic-awesome-skills · 46,133 stars · on GitHub · sickn33.github.io

Install

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.

Any agent
npx skills add sickn33/agentic-awesome-skills --skill agent-qa-debug-fix
Clone the repo
git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills

Made for: Claude Code.

Or install agentic-awesome-skills, the plugin that ships this one along with the rest of its 196 skills.

Wrote 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.

agentmods badge for agent-qa-debug-fix

README.md
[![agentmods](https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/agent-qa-debug-fix/github.svg)](https://agentmods.dev/skills/sickn33/agentic-awesome-skills/agent-qa-debug-fix)
Your own site
<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/agent-qa-debug-fix"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/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.

agentmods 80×15 button for agent-qa-debug-fix

Your own site · 80×15
<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/agent-qa-debug-fix"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/agent-qa-debug-fix.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 885 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Snyk pass 7 Sept 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00035 $0.00885
Opus 5 $0.00017 $0.00443
Sonnet 5 $0.00007 $0.00177
Haiku 4.5 $0.00003 $0.00089

Measured 9d ago against content hash a3faa542300c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

plugins/agentic-awesome-skills-claude/skills/agent-qa-debug-fix/SKILL.md · 78 lines

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

  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.

Read the full file on GitHub · 78 lines

Changes

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.

  1. 9d ago First seen · 78 lines · 35 tokens per session scan A a3faa542300c

Subscribe to this mod's changes

agent-qa-debug-fix is a skill published in the GitHub repository sickn33/agentic-awesome-skills (46,133 stars, last pushed 2d ago), licensed MIT. It adds 35 tokens to every session and 885 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.