Use when creating or modifying OpenCode rules (.md/.mdc files) that customize agent behavior. Trigger when user wants to create a rule, codify repeated instructions, persist guidance across sessions, or scope rules to specific files, prompts, environments, or workflows.
Build validated web research processes through self-annealing loops. Takes a research goal, generates search steps, tests against sample companies, scores accuracy, and iterates until 90%+. Use when creating new research workflows, building claygent/agent prompts, or systematizing any web research task.
Bootstrap and iterate TypeScript/TSX JupyterLab plugins in Plugin Playground for plugin development, using command-driven workflows and extension references.
Generate complete, production-ready Agent Skill packages via a 7-step pipeline. Each step has defined inputs, outputs, and quality constraints.
Control a headless Chrome browser via Pinchtab's REST API
System architecture and design thinking — requirements analysis, component design, data modeling, scaling strategy, and trade-off analysis. Use when: "design this system", "what's the architecture for", "trade-offs for X", "how should we architect", "system design for", "API design", "data model for", "service boundaries", "architecture doc", "create an ADR". When the design thinking is done, this skill hands off to /ship:write-docs to write the design document. Note: this is NOT for visual design (use /ship:visual-design) or implementation planning (use /ship:design). --- # Architectural Design Think through system design decisions rigorously before writing them down. This skill is about the **thinking** — requirements, components, trade-offs, boundaries. When the design is ready, you MUST invoke `Skill("write-docs")` to write the design document — do not write the doc inline. ## Scale to Complexity Not every decision needs all 5 phases. Match the depth to the decision: - **Small** (single component, clear constraints) — Phase 1 briefly, Phase 2, Phase 5. Skip deep dive and scaling. - **Medium** (multi-component, some unknowns) — All 5 phases, but keep each concise. - **Large** (new system, significant unknowns, cross-team) — All 5 phases in full depth, with diagrams and explicit load estimates. ## Red Flag **Never:** - Skip requirements gathering and jump straight to a solution - Design without understanding existing constraints (tech stack, team, timeline) - Omit trade-off analysis — every decision has alternatives that were rejected for a reason - Skip the Boundaries section — it's the core anti-drift mechanism - Propose a design without verifying assumptions against the actual codebase - Conflate "what we want" with "what exists" — be explicit about the gap ## Phase 1: Requirements Gathering Before designing anything, understand what you're solving. ### Functional Requirements - What must the system do? List concrete capabilities. - What are the input/output co
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Update the GitHub issue with concise execution status, blockers, or PR handoff details.
Capability summary
Implement tasks from an OpenSpec change. Use when the user wants to start implementing, continue implementation, or work through tasks.
Pattern extraction and skill promotion from session data. Detects repeated patterns and creates draft skills for user approval.
React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.
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:
