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 agentmods add skills/compozy/kb/systematic-qanpx skills add compozy/kb --skill systematic-qagit clone --depth 1 https://github.com/compozy/kbWhat 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 | $0.00098 | $0.01012 |
| Opus 5 | $0.00049 | $0.00506 |
| Sonnet 5 | $0.00020 | $0.00202 |
| Haiku 4.5 | $0.00010 | $0.00101 |
Grade A, and why
systematic-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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Systematic Project QA
Procedures
Step 1: Discover the Repository QA Contract
- Read root instructions, repository docs, and CI/build files before running commands.
- Execute
python3 scripts/discover-project-contract.py --root .to surface candidate install, verify, build, test, lint, and start commands. - Prefer repository-defined umbrella commands such as
make verify,just verify, or CI entrypoints over language-default commands. - Read
references/project-signals.mdwhen command ownership is ambiguous or when multiple ecosystems are present. - Identify the changed surface and the regression-critical surface before choosing scenarios.
- Choose a QA artifact location using repository conventions. If the repository has no QA artifact convention, store scratch artifacts under
/tmp/codex-qa-<slug>.
Step 2: Define the QA Scope
- Build a short execution matrix covering baseline verification, changed workflows, and unchanged business-critical workflows.
- Read
references/checklist.mdand ensure every required category has a planned validation. - Prefer public entry points such as CLI commands, HTTP endpoints, browser flows, worker jobs, and documented setup commands over internal test helpers.
- Create the smallest realistic fixture or fake project needed to exercise the workflow when the repository does not already include one.
- Treat mocks as a local unit-test boundary only. Do not use mocks or stubs as final proof that a user flow works.
Step 3: Establish the Baseline
- Install dependencies with the repository-preferred command before testing runtime flows.
- Run the canonical verification gate once before scenario testing to establish baseline health.
- If the baseline fails, read the first failing output carefully and determine whether it is pre-existing or introduced by current work before moving on.
- Start services in the closest supported production-like mode and confirm readiness through observable signals such as health checks, startup logs, or successful handshakes.
What ships with it
5 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.
- 3d ago First seen · 66 lines · 98 tokens per session scan A d4eef6301136
systematic-qa is a skill published in the GitHub repository compozy/kb (102 stars, last pushed 13d ago), licensed MIT. It adds 98 tokens to every session and 1,012 once invoked, about $0.0005 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.
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