systematic-qa

A full-project quality check that tests software from setup through its main user workflows.

In plain words
What is it for?
Use it to discover and run build, lint, test, startup, and end-to-end checks, including realistic test data where needed.
Why use it?
It helps find regressions that a single test or build command may miss by checking the repository’s own verification requirements.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/compozy/kb/systematic-qa
Any agent
npx skills add compozy/kb --skill systematic-qa
Clone the repo
git clone --depth 1 https://github.com/compozy/kb

Made for: Claude Code, Codex.

Per session 98 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,012 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00098 $0.01012
Opus 5 $0.00049 $0.00506
Sonnet 5 $0.00020 $0.00202
Haiku 4.5 $0.00010 $0.00101

Measured 3d ago against content hash d4eef6301136, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/discover-project-contract.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.agents/skills/systematic-qa/SKILL.md · 66 lines

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

  1. Read root instructions, repository docs, and CI/build files before running commands.
  2. Execute python3 scripts/discover-project-contract.py --root . to surface candidate install, verify, build, test, lint, and start commands.
  3. Prefer repository-defined umbrella commands such as make verify, just verify, or CI entrypoints over language-default commands.
  4. Read references/project-signals.md when command ownership is ambiguous or when multiple ecosystems are present.
  5. Identify the changed surface and the regression-critical surface before choosing scenarios.
  6. 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

  1. Build a short execution matrix covering baseline verification, changed workflows, and unchanged business-critical workflows.
  2. Read references/checklist.md and ensure every required category has a planned validation.
  3. Prefer public entry points such as CLI commands, HTTP endpoints, browser flows, worker jobs, and documented setup commands over internal test helpers.
  4. Create the smallest realistic fixture or fake project needed to exercise the workflow when the repository does not already include one.
  5. 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

  1. Install dependencies with the repository-preferred command before testing runtime flows.
  2. Run the canonical verification gate once before scenario testing to establish baseline health.
  3. 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.
  4. Start services in the closest supported production-like mode and confirm readiness through observable signals such as health checks, startup logs, or successful handshakes.

Read the full file on GitHub · 66 lines

Files

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.

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. 3d ago First seen · 66 lines · 98 tokens per session scan A d4eef6301136

Subscribe to this mod's changes

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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