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 terrylica/cc-skills --skill pre-ship-reviewgit clone --depth 1 https://github.com/terrylica/cc-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/terrylica/cc-skills/pre-ship-review)<a href="https://agentmods.dev/skills/terrylica/cc-skills/pre-ship-review"><img src="https://agentmods.dev/badge/skills/terrylica/cc-skills/pre-ship-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/terrylica/cc-skills/pre-ship-review"><img src="https://agentmods.dev/badge/skills/terrylica/cc-skills/pre-ship-review.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 | $0.00032 | $0.02836 |
| Opus 5 | $0.00016 | $0.01418 |
| Sonnet 5 | $0.00006 | $0.00567 |
| Haiku 4.5 | $0.00003 | $0.00284 |
Grade A, and why
pre-ship-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 6d 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 — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pre-Ship Review
Structured quality review before shipping code at any checkpoint: PRs, releases, milestones. Catches the failures that occur at integration boundaries -- where contracts, examples, constants, and tests must all agree.
Core thesis: AI-generated code excels at isolated components but fails systematically at boundaries between components. This skill systematically checks those boundaries.
Self-Evolving Skill: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues.
When to Use This Skill
Use before any significant code shipment:
- Pull requests with multiple new modules that wire together
- Releases combining work from multiple contributors or branches
- Milestones where quality gates must pass before proceeding
- Any checkpoint where code with examples, constants across files, or interface extensions needs validation
NOT needed for: single-file cosmetic changes, documentation-only updates, dependency bumps.
TodoWrite Task Templates
MANDATORY: Select and load the appropriate template before starting review.
Template A: New Feature Ship
1. Detect changed files and scope (git diff --name-only against base branch)
2. Run Phase 1 - External tool checks (Pyright, Vulture, import-linter, deptry, Semgrep, Griffe)
3. Run Phase 2 - cc-skills orchestration (code-hardcode-audit, dead-code-detector, pr-gfm-validator)
4. Run Phase 2 conditional checks based on file types changed
5. Phase 3 - Verify every function parameter has at least one caller passing it by name
6. Phase 3 - Verify every config/example parameter maps to an actual function kwarg
7. Phase 3 - Check for architecture boundary violations (hardcoded feature lists, cross-layer coupling)
8. Phase 3 - Verify domain constants and formulas are correct (cross-reference cited sources)
9. Phase 3 - Audit test quality - do tests test what they claim (not side effects)?
10. Phase 3 - Check for implicit dependencies between new components
11. Phase 3 - Look for O(n^2) patterns where O(n) suffices
12. Phase 3 - Verify error messages give actionable guidance
13. Phase 3 - Confirm examples reflect actual behavior, not aspirational behavior
14. Compile findings report with severity and suggested fixes
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.
- 6d ago First seen · 274 lines · 32 tokens per session scan A df5261a8da9c
pre-ship-review is a skill published in the GitHub repository terrylica/cc-skills (72 stars, last pushed yesterday), licensed MIT. It adds 32 tokens to every session and 2,836 once invoked, about $0.0002 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 2026-09-05.
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accessibility-a11y
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generic-fullstack-code-reviewer
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generic-static-code-reviewer
Review static site code for bugs, security issues, performance problems, accessibility gaps, and CLAUDE.md compliance. Enforces pure HTML/CSS/JS standards, minimal page weight, mobile-first design. Use when completing features, before commits, or reviewing changes.
develop-feature
Autonomously implement a complete feature from request to merge-ready — documentation phase, wave-based TDD implementation, then all quality gates, without stopping for human input.
implement-slice
Implement the next smallest slice from the current plan using TDD — tests first, then code, then verification, then an atomic commit. Reads the plan from the scratchpad.