Use when the user wants AI to turn a requirement text, requirement document, and existing reviewed plan files into dependency-ordered implementation plans, a consolidated `checklist.md`, and, only after an explicit execution command, implement those plans with subagents in parallel where safe and in sequence where required.
Multi-agent development workflow using Agent Teams. Supports five modes: plan (architect teammate + PM teammate debate → plan.md), dev (developer teammate + code-tester teammate + qa-tester teammate + reviewer teammate iterate → code), full (plan → approval gate → dev), auto (plan → dev, no gate), and bugfix (tester + developer + reviewer TDD triad → fix + PR). Use when tasks benefit from collaborative agent teammates with peer messaging.
Create or update a structured JSON gameplan for a codebase change, including patch sequencing, dependency graph, acceptance criteria, and formal per-patch and final-state specs. Use when the user asks for a gameplan, implementation plan, milestone plan, or structured change plan.
Use when you have a written plan and want it reviewed for architecture, scope, and test coverage before execution. Use when the user says "review this plan", "autoplan", "check my plan", "is this plan solid", or before running superpowers:executing-plans or superpowers:subagent-driven-development.
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".
Independent plan/spec reviewer — reads a plan file, verifies claims against the workspace, and returns a structured verdict with findings. Supports external judges (Codex, OpenAI, etc.) or runs inline.
Analyzes ADU permit corrections letters — the first half of the corrections pipeline. Reads the corrections letter, builds a sheet manifest from the plan binder, researches state and city codes, views referenced plan sheets, categorizes each correction item, and generates informed contractor questions. This skill should be used when a contractor receives a city corrections letter for an ADU permit. It coordinates three sub-skills (california-adu for state law, adu-city-research for city rules, adu-targeted-page-viewer for plan sheet navigation) to produce research artifacts and a UI-ready questions JSON. Does NOT generate the final response package — that is handled by adu-corrections-complete after the contractor answers questions. Triggers when a corrections letter PDF/PNG is provided along with the plan binder PDF.
Convert user-provided recommendations into a timestamped, execution-ready backlog plan using the active repo profile. Use when a repo needs a one-off plan that should not overwrite PLAN_NEXT.
Adversarial AI code/plan review. Codex reviews, Claude fixes, iterative loop until approved. Auto-detects plan/code/code-vs-plan mode.
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:
