- 📄 SKILL.md
example-skill
Example Claude skill
Example Claude skill
Build production-ready Tavily integrations with best practices baked in. Reference documentation for developers using coding assistants (Claude Code, Cursor, etc.) to implement web search, content extraction, crawling, and research in agentic workflows, RAG systems, or autonomous agents.
Query previous pi sessions to retrieve context, decisions, code changes, or other information. Use when you need to look up what happened in a parent session or any other session file.
直接调用后端 API,直接控制项目配置、文件管理、知识库、MCP 等核心功能
Enforces design review gate after brainstorming — bridges superpowers:brainstorming into the metaswarm quality pipeline
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Four sweep operations: (1) Model perf sweep — find optimal batch size / TGS for a model. Use for: sweep batch size, tune TGS, benchmark throughput, find optimal config. (2) Node perf sweep — compare per-node GPU performance to find outliers. Use for: check nodes, node performance, find slow node, compare nodes. (3) Node network health sweep — detect inter-node network issues via multi-node bisection. Use for: network health, IB issues, RCCL problems, node pair testing, isolate network problem. (4) Model sweep — run all model configs on one or two commits. Use for: regression test, validate commit, test all models, smoke test, CI, compare branches.
Build and run multi-agent pipelines using AgentFlow. Use when the user wants to orchestrate codex, claude, or kimi agents in parallel, in sequence, or in iterative loops. Trigger when the user mentions multi-agent workflows, fan-out tasks, code review pipelines, iterative implementation loops, running agents on EC2/ECS, or any task that needs multiple AI agents coordinated together. Also trigger for "agentflow", "pipeline", "graph of agents", "fanout", "shard", or "run codex on remote".
Run one iteration of the autoresearch loop — study existing attack methods, design a better optimizer, implement it, benchmark it, and commit. Meant to be called repeatedly via /loop.
Use this when the user needs to choose between multiple ML routes after survey but before committing to implementation. Compares candidate approaches, selects one, records rejected routes, and keeps a fallback.
Guide agent through geospatial ETL workflows using built-in, learned, and fabricated geo tools
skill-sample/ ├─ SKILL.md ⭐ Required: skill entry doc (purpose / usage / examples / deps) ├─ manifest.sample.json ⭐ Recommended: machine-readable metadata (index / validation / autofill) ├─ LICENSE.sample ⭐ Recommended: license & scope (open source / restriction / commercial) ├─ scripts/ │ └─ example-run.py ✅ Runnable example script for quick verification ├─ assets/ │ ├─ example-formatting-guide.md 🧩 Output conventions: layout / structure / style │ └─ example-template.tex 🧩 Templates: quickly generate standardized output └─ references/ 🧩 Knowledge base: methods / guides / best practices ├─ example-ref-structure.md 🧩 Structure reference ├─ example-ref-analysis.md 🧩 Analysis reference └─ example-ref-visuals.md 🧩 Visual reference
More Agent Skills specs Anthropic docs: https://agentskills.io/home
├─ ⭐ Required: YAML Frontmatter (must be at top) │ ├─ ⭐ name : unique skill name, follow naming convention │ └─ ⭐ description : include trigger keywords for matching │ ├─ ✅ Optional: Frontmatter extension fields │ ├─ ✅ license : license identifier │ ├─ ✅ compatibility : runtime constraints when needed │ ├─ ✅ metadata : key-value fields (author/version/source_url...) │ └─ 🧩 allowed-tools : tool whitelist (experimental) │ └─ ✅ Recommended: Markdown body (progressive disclosure) ├─ ✅ Overview / Purpose ├─ ✅ When to use ├─ ✅ Step-by-step ├─ ✅ Inputs / Outputs ├─ ✅ Examples ├─ 🧩 Files & References ├─ 🧩 Edge cases ├─ 🧩 Troubleshooting └─ 🧩 Safety notes
Skill files are scattered across GitHub and communities, difficult to search, and hard to evaluate. SkillWink organizes open-source skills into a searchable, filterable library you can directly download and use.
We provide keyword search, version updates, multi-metric ranking (downloads / likes / comments / updates), and open SKILL.md standards. You can also discuss usage and improvements on skill detail pages.
Quick Start:
Import/download skills (.zip/.skill), then place locally:
~/.claude/skills/ (Claude Code)
~/.codex/skills/ (Codex CLI)
One SKILL.md can be reused across tools.
Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.
A skill is a reusable capability package, usually including SKILL.md (purpose/IO/how-to) and optional scripts/templates/examples.
Think of it as a plugin playbook + resource bundle for AI assistants/toolchains.
Skills use progressive disclosure: load brief metadata first, load full docs only when needed, then execute by guidance.
This keeps agents lightweight while preserving enough context for complex tasks.
Use these three together:
Note: file size for all methods should be within 10MB.
Typical paths (may vary by local setup):
One SKILL.md can usually be reused across tools.
Yes. Most skills are standardized docs + assets, so they can be reused where format is supported.
Example: retrieval + writing + automation scripts as one workflow.
Some skills come from public GitHub repositories and some are uploaded by SkillWink creators. Always review code before installing and own your security decisions.
Most common reasons:
We try to avoid that. Use ranking + comments to surface better skills: