sanyuan-skills is a collection of reusable instructions, called agent skills, that extend Claude Code and other AI coding terminals with defined workflows. Developers use it for code review, learning, skill quality checks, skill creation, and organizing knowledge into wikis or study materials. The catalogue entries are individual skills from this collection.
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 sanyuan0704/sanyuan-skills --skill skill-reviewgit clone --depth 1 https://github.com/sanyuan0704/sanyuan-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/sanyuan0704/sanyuan-skills/skill-review)<a href="https://agentmods.dev/skills/sanyuan0704/sanyuan-skills/skill-review"><img src="https://agentmods.dev/badge/skills/sanyuan0704/sanyuan-skills/skill-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/sanyuan0704/sanyuan-skills/skill-review"><img src="https://agentmods.dev/badge/skills/sanyuan0704/sanyuan-skills/skill-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00117 | $0.01146 |
| Opus 5 | $0.00059 | $0.00573 |
| Sonnet 5 | $0.00023 | $0.00229 |
| Haiku 4.5 | $0.00012 | $0.00115 |
Grade A, and why
skill-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 13d 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Review
IRON LAW: Be specific and actionable. Never say "could be improved" without stating exactly what to change and why it matters for model output quality.
Workflow
Skill Review Progress:
- [ ] Step 1: Load Target ⚠️ REQUIRED
- [ ] 1.1 Identify skill path
- [ ] 1.2 Read SKILL.md and inventory all files
- [ ] Step 2: Analyze ⚠️ REQUIRED
- [ ] 2.1 Structure compliance
- [ ] 2.2 Description quality
- [ ] 2.3 Workflow design
- [ ] 2.4 Token efficiency
- [ ] 2.5 Anti-pattern detection
- [ ] Step 3: Report ⚠️ REQUIRED
- [ ] 3.1 Strengths (what's done well)
- [ ] 3.2 Suggestions (prioritized improvements)
Step 1: Load Target ⚠️ REQUIRED
Identify the skill to review. Accept:
- Explicit path:
/skill-review path/to/skill - Current directory context: if user is already in a skill folder
- Skill name: search within the workspace for matching skill directory
Read the full SKILL.md and list all files in the skill directory. Count SKILL.md line count — this is a key metric.
Step 2: Analyze ⚠️ REQUIRED
Load references/review-criteria.md for detailed criteria. Evaluate the skill across five dimensions:
2.1 Structure Compliance
Questions to answer:
- Does the directory follow the standard layout (SKILL.md, scripts/, references/, assets/)?
- Is SKILL.md under 500 lines?
- Does frontmatter contain only
nameanddescription(plus optionalallowed-tools,license,metadata)? - Are there unnecessary files (README.md, CHANGELOG.md, LICENSE duplicates)?
- Are references organized by domain with one level of nesting?
2.2 Description Quality
Questions to answer:
- Does the description include concrete trigger keywords and phrases?
- Does it use keyword bombing (multiple phrasings of the same intent)?
- Is it self-contained — can a router understand what this skill does without reading the body?
- Does it avoid putting "When to Use" info in the body instead of the description?
- Would a user's natural language query match this description?
What ships with it
1 file 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.
- 13d ago First seen · 119 lines · 117 tokens per session scan A cacdf2e45f61
skill-review is a skill published in the GitHub repository sanyuan0704/sanyuan-skills (3,906 stars, last pushed 4mo ago), licensed MIT. It adds 117 tokens to every session and 1,146 once invoked, about $0.0006 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…