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/3awny/qship/qchecknpx skills add 3awny/qship --skill qcheckgit clone --depth 1 https://github.com/3awny/qshipWrote 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/3awny/qship/qcheck)<a href="https://agentmods.dev/skills/3awny/qship/qcheck"><img src="https://agentmods.dev/badge/skills/3awny/qship/qcheck.svg" alt="Measured on agentmods" 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 | $0.00055 | $0.03374 |
| Opus 5 | $0.00028 | $0.01687 |
| Sonnet 5 | $0.00011 | $0.00675 |
| Haiku 4.5 | $0.00006 | $0.00337 |
Grade A, and why
qcheck scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 338 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
Step 0: Apply Auto-Memory
The memory index is already loaded in your system prompt — no re-read needed. As you review, apply the relevant feedback_* entries (analogous code, redundancy patterns, qbcheck-before-reporting, actor-critic review, single-source-of-truth, etc.). When a finding maps to a known memory, cite the entry name in the report so the user can trace the rule.
If a specific memory file looks relevant to a finding, read just that one file from the auto-memory directory listed in the system prompt.
Step 1: Gather Changes & Context
1.1 Get Git SHAs
BASE_SHA=$(git merge-base HEAD develop)
HEAD_SHA=$(git rev-parse HEAD)
If git merge-base fails (detached HEAD, or running on develop/main), STOP and ask the user for an explicit base — don't silently fall back to HEAD~1. Reviewing just the last commit on develop is almost never what's wanted; the user usually means a specific PR range, a Jira branch they forgot to switch to, or a tag.
1.2 Gather Diff
git diff --stat $BASE_SHA..$HEAD_SHA
git diff $BASE_SHA..$HEAD_SHA
Also check for unstaged/staged changes not yet committed:
git diff
git diff --cached
1.3 Read Guidelines
Read the review guidelines:
- Project-level:
CLAUDE.mdin the repository root - User-level:
~/.claude/CLAUDE.md(user preferences and lessons learned)
1.4 Identify What Was Built
Summarize from the diff and recent commits:
- What feature/fix was implemented
- Which files were changed and why
- The plan or requirements (if a plan file exists in
docs/plans/)
Step 2: Launch Parallel Review Agents
Deploy BOTH review agents simultaneously in a single message with two Task tool calls:
2.1 Superpowers Code Reviewer
Dispatch using the superpowers:code-reviewer subagent type:
Task(
subagent_type: "superpowers:code-reviewer",
description: "Comprehensive code review",
prompt: |
You are reviewing code changes for production readiness.
Your task:
1. Review: <WHAT_WAS_IMPLEMENTED>
2. Compare against: <PLAN_OR_REQUIREMENTS> (or "No formal plan -- review based on code quality and correctness")
3. Check code quality, architecture, testing
4. Categorize issues by severity
5. Assess production readiness
What Was Implemented: <DESCRIPTION>
Requirements/Plan: <PLAN_REFERENCE>
Git Range: Base: <BASE_SHA>, Head: <HEAD_SHA>
Run: git diff --stat <BASE_SHA>..<HEAD_SHA>
Run: git diff <BASE_SHA>..<HEAD_SHA>
Review Checklist:
- Code Quality: separation of concerns, error handling, type safety, DRY, edge cases
- Architecture: design decisions, scalability, performance, security
- Testing: tests test logic (not mocks), edge cases covered, integration tests
- Requirements: all plan requirements met, no scope creep, breaking changes documented
- Production Readiness: migrations, backward compatibility, documentation
Output Format:
### Strengths
### Issues (Critical / Important / Minor with file:line references)
### Recommendations
### Assessment: Ready to merge? [Yes/No/With fixes]
)
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.
- 5d ago First seen · 338 lines · 55 tokens per session scan A bfaea382a9ce
qcheck is a skill published in the GitHub repository 3awny/qship (2 stars, last pushed 2mo ago), licensed MIT. It adds 55 tokens to every session and 3,374 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
remove-ai-marks
Strip multi-vendor AI provenance from owned files: hidden Unicode (Layer A), statistical sampling watermarks via rewrite (Layer B — always offer), and C2PA/EXIF/XMP/container metadata on PNG/JPEG/WebP/SVG/PDF/DOCX/ODT/HTML/MD. Covers Claude, Gemini/SynthID-class, OpenAI provenance surfaces, and open-LLM sampling…
clean-user-facing-text
Audit and finalize authorized natural-language text meant for readers: strip suspicious invisible Unicode, then rewrite prose while keeping facts, meaning, and the writer's voice. Use when the user asks to clean, humanize, polish, or finalize articles, manuscripts, reports, documentation, emails, product copy, UI…
oracle
Author an IMPL-BLIND spec-conformance oracle for an acceptance criterion the policy worklist (clad oracle --required) demands — an empty worklist means don't author unless the user explicitly asks. YOU spawn a blind sub-agent from a spec-only brief, then record it. Activate only when the connected project contains…
reviewer
Philosophical guardrails enforcer — independently audits code, tests, and spec for layered-integrity, Why>What, error-as-data, and the related Ironclad philosophical invariants. Activate only when the connected project contains spec.yaml or the user explicitly names Cladding; ignore ordinary requests in uninitialized…
doctor
Diagnose Cladding runtime health — Claude Code hook liveness and version, CI package pinning, lifecycle governance, and sentinel-miss frequency by phase × cause × fallback. Use when hooks may be silent, CI may float across Cladding releases, scan or run results look thinner than expected, or before tuning the host…
run
Run cladding's autonomous loop — iterate ready features, dispatch the developer + reviewer personas through the active host adapter, run L1 gates, halt on HUMANREQUIRED or transport failure. Use only when the user explicitly asks for autonomous progress; the loop will modify files. Activate only when the connected…