Review code changes for quality, security, and correctness. Use when the user says "review this PR", "review these changes", "check my code", "look at what I changed", or after implementing a feature. Produces a severity-organized report.
Address PR review comments systematically. Use when responding to code review feedback on a pull request.
Complete academic research skill suite covering the full pipeline: paper reading (read/explain papers with storytelling), idea generation (brainstorm research directions), experiment design (plan experiments, ablation, baselines), proof writing (mathematical proofs, LaTeX theorems), paper writing (draft to camera-ready for top venues like NeurIPS/ICLR/ACL), paper review (structured 4-step review with scoring), and professor fit analysis (evaluate advisors, cold emails, interview strategy). Trigger keywords: read paper, brainstorm, experiment design, prove, write paper, review, professor fit, advisor, cold email, LaTeX, research, NeurIPS, ICLR, ACL, arXiv, 讀論文, 寫論文, 審稿, 實驗設計, 數學證明, 研究方向, 教授分析, 選指導教授.
Use when the user asks to review code, check for issues, or says "review", "审查", "检查代码
Run a local code review against project architecture rules and quality standards. Use when the user asks to review code, check for violations, or audit changes.
This skill should be used when a user asks for a code review, feedback on a PR or MR, diff assessment, or says things like 'can you review my changes', 'look at this diff', 'is this ready to merge', 'check my code', 'review this branch', 'what do you think of these changes', or 'LGTM check'. Covers correctness, tests, performance, security, and architecture feedback on pull/merge requests or raw diffs from any platform (GitHub, GitLab).
Reference documents for deep code review (Level 3) and architecture review (Level 4). Used by code-reviewer agent for advanced review levels.
Audit designs against 18 professional rules across Figma files and code (HTML/CSS/React/Vue/Tailwind). Detects framework automatically, runs category-specific code superpowers (aria, focus, contrast, tokens, responsive, motion, forms, navigation, spacing), audits for dark patterns and ethical design issues, outputs before/after code diffs, and generates a structured developer handoff report. Triggers on: check my design, review my UI, audit my layout, is this accessible, design review, typography check, color contrast, WCAG, a11y, pixel perfect, UI critique, Figma audit, CSS check, review this component, does this look good, dark patterns, ethical design, is this GDPR compliant, check my onboarding, review my checkout, is this manipulative, any dark patterns here, check my landing page, is my UI accessible, check my design system, is this ethical, is my form accessible, check my navigation, is my dark mode correct, is this responsive, review my empty states, check my error states.
Review Markdown documents using the MRSF (Sidemark) sidecar format. Use when asked to review, comment on, or provide feedback on Markdown files. Adds structured, anchored review comments via the MRSF MCP server.
Pre-commit code quality review for Unity C# code. Checks against Rules, CODING_STANDARDS.md, NAMING_CONVENTIONS.md, and GDD Gherkin test coverage. Use when code has been written and tests are passing but before committing. Runs automatically at Step 8.5 of /uw-cmd-implement-feature. Triggers on "review this code", "check my implementation", "pre-commit review", "code review", "check before commit", "is this ready to commit", "review my changes", or any request to validate code quality before committing.
Use when the user has received review comments on a GitHub PR or GitLab MR and needs to process them — analyze, triage, coordinate fixes, respond, and resolve threads. This is THE skill for any post-review work on a PR/MR. Triggers on: "разберись с комментариями", "address review comments", "handle review feedback", "respond to reviewers", "fix review comments", "deal with PR/MR comments", "reply to review", "resolve review threads", "go through the feedback", "комментарии к PR/MR", "ревьюер оставил комментарии", "пройдись по комментариям", "тредов после ревью", "reviewer left comments", "got a review", "review feedback on my MR/PR", or any mention of processing, triaging, or responding to existing review comments on a pull request or merge request. Do NOT use for writing new reviews, creating PRs, or CI/CD monitoring — those are separate skills. --- # Address Review Feedback An orchestrator skill. Analyzes all review comments, categorizes them, detects cross-diff patterns, presents an action plan for user confirmation, then coordinates fixes, answers, and thread responses. Does NOT implement code changes itself — it produces task descriptions for implementation agents. **Core principle:** Fix what belongs to this PR. Push back when a suggestion is wrong. Never perform agreement — just act and show evidence. --- ## Phase 1: Fetch & Parse ### Platform detection ```bash REMOTE_URL=$(git remote get-url origin) # Contains github.com → GitHub (gh) # Contains gitlab → GitLab (glab) ``` ### Fetch PR/MR metadata and context Fetch both technical metadata and the PR/MR context — description, linked issues, labels, milestone. This context is essential for Phase 2: understanding the intent behind changes lets you judge whether a comment is in scope, whether a suggestion aligns with the goal, and what trade-offs matter. ```bash # GitHub — metadata + context in one call PR_INFO=$(gh pr view --json number,baseRefName,headRefName,title,body,labels,milestone,closingIssuesReferen
Orchestrate multi-agent teams with defined roles, task lifecycles, handoff protocols, and review workflows. Use when: (1) Setting up a team of 2+ agents with different specializations, (2) Defining task routing and lifecycle (inbox → spec → build → review → done), (3) Creating handoff protocols between agents, (4) Establishing review and quality gates, (5) Managing async communication and artifact sharing between agents.
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:
