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 Parcha-ai/parcha-skills --skill autoqagit clone --depth 1 https://github.com/Parcha-ai/parcha-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/parcha-ai/parcha-skills/autoqa)<a href="https://agentmods.dev/skills/parcha-ai/parcha-skills/autoqa"><img src="https://agentmods.dev/badge/skills/parcha-ai/parcha-skills/autoqa/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/parcha-ai/parcha-skills/autoqa"><img src="https://agentmods.dev/badge/skills/parcha-ai/parcha-skills/autoqa.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00093 | $0.02409 |
| Opus 5 | $0.00046 | $0.01205 |
| Sonnet 5 | $0.00019 | $0.00482 |
| Haiku 4.5 | $0.00009 | $0.00241 |
Grade A, and why
autoqa scanned grade A with 1 finding 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
they resolve. Name witness files by row: curl output with status codes, page snapshots or How it starts
The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
autoqa — QA any repo against its own running app
You are the QA engineer for this repo. Every verdict is witnessed — it points at an artifact (an HTTP response, a page snapshot, a log line, a DB row) that shows the result, not merely a file that exists. The full witness contract is in Hard rules below.
Phase 0 — RESOLVE
Establish three facts before anything else:
- Target repo — path or URL the user pointed at (ask only if truly absent).
- Target instance — a running deployment to test against (URL/port), or the instruction to bring one up locally.
- Repo config — look for
AUTOQA.mdat the repo root or underdocs/. It is the repo's reusable baseline, not the complete plan: how to run, how to auth, stable catalog/core checks, and known env caveats. If found, read it now and skip the generic discovery in Phase 1, but never skip Phase 2's diff discovery. Missing config means full Phase 1 — and a repo you QA repeatedly earns one: writeAUTOQA.mdfrom what Phase 1 taught you so later runs can start from that baseline.
Done when: repo path, instance URL (or "must boot"), and config-or-none are stated.
Phase 1 — DISCOVER
Read the repo the way a new engineer would, in this order, stopping when the three questions below are answered. Full source-priority list and what each source answers: references/discovery.md.
- How does it run? Dockerfile / compose / Procfile / Makefile / CI workflows / README.
- How do I authenticate? env samples, auth middleware, dev-token conventions, CLAUDE.md / AGENTS.md.
- What are the features? a feature catalog or spec doc if the repo ships one (use it — it beats inference), else routes/pages/CLI entrypoints enumerated from code.
Done when: you can write down the run command, an auth recipe, and a feature inventory — each traced to the file that taught you it.
Phase 2 — PLAN
Build the plan from the union of two sources:
- Baseline inventory — every feature/check required by
AUTOQA.md, or Phase 1 when no config exists. - Diff inventory — cases derived from the actual change under test. Resolve the base from the user's target or PR; otherwise use the merge-base with the repository's default remote branch. Include committed, staged, unstaged, and relevant untracked changes. Read the diff and the acceptance/design docs it changes or cites. Derive behavior-level cases for changed user entry points, APIs/contracts, schemas/migrations, background work, configuration and feature flags, compatibility/fallbacks, failure handling, security or authorization boundaries, concurrency/idempotency, rollout/rollback, and cleanup. Do not mistake a large unit-test list for this inventory.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 169 lines · 93 tokens per session scan A d6fe2feeebd9
autoqa is a skill published in the GitHub repository Parcha-ai/parcha-skills (59 stars, last pushed today), licensed MIT. It adds 93 tokens to every session and 2,409 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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