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.
git clone --depth 1 https://github.com/rjmurillo/ai-agentsWrote 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.
[](https://agentmods.dev/commands/rjmurillo/ai-agents/qa)<a href="https://agentmods.dev/commands/rjmurillo/ai-agents/qa"><img src="https://agentmods.dev/badge/commands/rjmurillo/ai-agents/qa/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.
<a href="https://agentmods.dev/commands/rjmurillo/ai-agents/qa"><img src="https://agentmods.dev/badge/commands/rjmurillo/ai-agents/qa.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00023 | $0.00292 |
| Opus 5.5 | $0.00009 | $0.00117 |
| Sonnet 5.5 | $0.00005 | $0.00058 |
| Haiku 4.5 | $0.00002 | $0.00029 |
Grade A, and why
qa 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 1mo 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.
This is a copy
84% identical to analyst — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
PR Quality Gate - QA Review
Run the qa quality gate locally on your current changes before pushing.
Context
Branch Information
Use the Bash tool to run git branch --show-current to determine the current branch.
Use $ARGUMENTS as the base branch if provided, otherwise default to main.
Review Criteria
Apply the criteria from: @.github/prompts/pr-quality-gate-qa.md
Changed Files
Use the Bash tool to run git diff "<base_branch>" --name-only to list changed files.
Full Diff
Use the Bash tool to run git diff "<base_branch>" to obtain the full diff.
Output Format
Provide your verdict in EXACTLY this format. Do not add preambles, explanations, or additional text before the verdict:
VERDICT: [PASS|WARN|CRITICAL_FAIL]
MESSAGE: [One sentence summary]
[Detailed findings following prompt structure]
Then emit a fenced JSON block conforming to .agents/schemas/pr-quality-gate-output.schema.json with "agent": "qa".
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.
- 1mo ago Changed · -1 lines 60661deccf3d
- 1mo ago Changed · -1 lines 09608eb97b87
- 1mo ago First seen · 50 lines · 23 tokens per session scan A b1e20634c895
qa is a command published in the GitHub repository rjmurillo/ai-agents (48 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 292 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 84% identical to analyst, differing in 10 lines, and is treated as a copy.
Other commands, from other repositories
review-deep
Review change vs spec, plan, code quality heuristics, and repo rules; append remediation phase if needed.
write-test-plan
Generate or update change test plan.
review-skills
Review changed skills: automated bash checks + 9 structural dimensions (D1-D9) + 5 intent checks (M1-M5). PASS/FAIL verdict. Fix in-place.
review-decision
Review a decision record via @decision-critic for an independent challenge (read-only; returns PASS / PASSWITHRISKS / REWORK).
apply-review-feedback
Classify and apply accepted PR/MR review feedback.
upload-report
Run automation and upload or verify Katalon Platform reports for Katalon Studio/KRE, JUnit XML, and Playwright reports. Use when you need to combine Katalon MCP project/result discovery with Katalon CLI execution, Katalon Report Uploader, or @katalon/playwright-reporter; configure report folders, report types…