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.
npx skills add DevelopersGlobal/ai-agent-skills --skill agent-qa-run-reviewgit clone --depth 1 https://github.com/DevelopersGlobal/ai-agent-skillsWrote 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/skills/developersglobal/ai-agent-skills/agent-qa-run-review)<a href="https://agentmods.dev/skills/developersglobal/ai-agent-skills/agent-qa-run-review"><img src="https://agentmods.dev/badge/skills/developersglobal/ai-agent-skills/agent-qa-run-review/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/skills/developersglobal/ai-agent-skills/agent-qa-run-review"><img src="https://agentmods.dev/badge/skills/developersglobal/ai-agent-skills/agent-qa-run-review.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 | $0.00034 | $0.01220 |
| Opus 5.5 | $0.00014 | $0.00488 |
| Sonnet 5.5 | $0.00007 | $0.00244 |
| Haiku 4.5 | $0.00003 | $0.00122 |
Grade A, and why
agent-qa-run-review 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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
agent-qa by vostride tests apps from natural-language steps. Its advanced memidex memory engine and self-improvement capabilities make past experience reusable; reviewing the evidence behind that experience keeps a release decision grounded. This workflow explains an existing run without changing tests, verdicts, or memory.
When to Use
- A user asks why an agent-qa run passed, failed, timed out, or healed.
- A release report needs to distinguish recorded execution from validation or a dry run.
- A learned record appears to disagree with the application evidence.
Use an existing project with agent-qa installed and its run artifacts available. The CLI requires Node.js 24 or newer. Run commands from the directory containing agent-qa.config.yaml; use the installed CLI, or npx --no-install agent-qa, to avoid fetching a different version during review. Check that version's --help if options differ.
Process
Step 1: Identify the exact run
Read agent-qa runs list --limit 5 --json if the run ID is unknown, then agent-qa digest RUN_ID --json and agent-qa runs overview RUN_ID --json. Match the requested test, target, build, and time; select the explicit run ID for every later read.
Verify: Record the native verdict and execution scope separately from the CLI exit status. Successful validation or a dry run does not prove application behavior. A historical pass does not establish that the current build passes; missing target/build provenance limits the conclusion.
Step 2: Inspect the decisive evidence
Use the overview's zero-based test and step indices. For example, agent-qa runs evidence RUN_ID --test 0 --step 2 --kind network --json reads that step's network evidence; other kinds include console, aria, a11y, and variables. Export a listed screenshot with agent-qa runs evidence RUN_ID --screenshot HASH --out NEW_FILE.png, then view it. Distinguish a missing artifact from an artifact you have actually inspected.
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.
- yesterday First seen · 75 lines · 34 tokens per session scan A 47e0beb9652c
agent-qa-run-review is a skill published in the GitHub repository DevelopersGlobal/ai-agent-skills (216 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 1,220 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-29.
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