Structures AI and ML product decisions including model selection, data requirements, evaluation frameworks, and responsible AI considerations. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Triggers on "AI product", "LLM feature", "AI canvas", "build with AI", "AI integration", "AI-powered", "machine learning feature".
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations.
Use this skill when designing, documenting, or generating REST API specifications using OpenAPI/Swagger. Trigger on keywords like OpenAPI, Swagger, API spec, REST documentation, API schema, request body, response schema, and API client generation. Also apply when adopting design-first API development, validating API contracts, or setting up auto-generated API documentation for FastAPI, Express, or NestJS endpoints. --- # OpenAPI & REST API Design ## When to Use - Documenting REST APIs - Generating API clients - API design-first development - Defining webhook contracts - Establishing pagination, versioning, or auth patterns for a new service ## When NOT to Use - Internal-only scripts or automation that do not expose HTTP endpoints - CLI tools and command-line utilities without a REST interface - GraphQL APIs where a different specification format applies --- ## Core Patterns ### 1. OpenAPI 3.1 Specification Structure A complete spec skeleton showing every top-level section. Use `$ref` to split large specs into per-resource files. ```yaml
Browse Bilibili pages using agent-browser for visual exploration and DOM interaction.
Use when launching Clownfish in GitHub Actions to create or update one guarded GitHub implementation PR from issue/PR refs, a ClawSweeper report, a custom maintainer prompt, or opt any PR into ClawSweeper-reviewed Clownfish automerge.
Claim the AI bounty on tDVV by delegating to the canonical repo SKILL and its branch docs; this public skill is AA/CA only and requires explicit write confirmation.
Master Media Prompter — expert AI image & video creation via Leonardo.ai (100% browser-based)
Local command skill example with explicit command whitelisting.
Cognithor - Agent OS: Local-first autonomous agent operating system. 16 LLM providers, 17 channels, 112+ MCP tools, 5-tier memory, A2A protocol, knowledge vault, voice, browser automation, Computer-use, self-healing, self-improving. Python 3.12+, Apache 2.0.
Browse entities in the Remnic knowledge graph and surface their facts and relationships. Trigger phrases include "tell me about the entity", "look up", "what do we know about".
Debug AI traces, find exceptions, analyze sessions, and manage prompts via Langfuse MCP. Use when debugging AI pipelines, investigating errors, analyzing latency, managing prompt versions, or setting up Langfuse. Triggers on "langfuse", "traces", "debug AI", "find exceptions", "what went wrong", "why is it slow", "datasets", "evaluation sets".
Creates dbt models following project conventions. Use when working with dbt models for: (1) Creating new models (any layer - discovers project's naming conventions first) (2) Task mentions "create", "build", "add", "write", "new", or "implement" with model, table, or SQL (3) Modifying existing model logic, columns, joins, or transformations (4) Implementing a model from schema.yml specs or expected output requirements Discovers project conventions before writing. Runs dbt build (not just compile) to verify. --- # dbt Model Development **Read before you write. Build after you write. Verify your output.** ## Critical Rules 1. **ALWAYS run `dbt build`** after creating/modifying models - compile is NOT enough 2. **ALWAYS verify output** after build using `dbt show` - don't assume success 3. **If build fails 3+ times**, stop and reassess your entire approach ## Workflow ### 1. Understand the Task Requirements - What columns are needed? List them explicitly. - What is the grain of the table (one row per what)? - What calculations or aggregations are required? ### 2. Discover Project Conventions ```bash cat dbt_project.yml find models/ -name "*.sql" | head -20 ``` Read 2-3 existing models to learn naming, config, and SQL patterns. ### 3. Find Similar Models ```bash # Find models with similar purpose find models/ -name "*agg*.sql" -o -name "*fct_*.sql" | head -5 ```
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:
