GitHub CLI (gh) comprehensive reference for repositories, issues, pull requests, Actions, projects, releases, gists, codespaces, organizations, extensions, and all GitHub operations from the command line.
Professional-grade virtual film director and prompt engineer for Seedance 2.0 (即梦). Transforms vague ideas into cinematic, production-ready video prompts with Hollywood-caliber shot design. Covers every workflow — text-to-video, image-to-video, multi-modal references, video extension, character swap, dialogue-driven short films, and music-synced edits. Ships with a cinematography dictionary (50+ safe camera-move phrases), a director style library (Villeneuve, Wes Anderson, Shinkai, Wuxia & more), a 3-layer lighting & quality-anchor system that kills the "plastic AI look," and a built-in structured validation checklist so every prompt passes before delivery. Supports bilingual output (Chinese/English) with smart >15 s auto-segmentation for long-form storytelling.
Execute 6502/6510 assembly workflows through c64bridge.
Build automated fleet monitoring workflows using n8n. Use this skill when asked to create agents, automations, or monitoring systems that connect Geotab to external services like Slack, Discord, email, or other APIs.
Use when a task needs deterministic data cleaning or export on a local dataset, such as filling missing numeric values, dropping null rows, deduplicating, selecting columns, renaming columns, deriving columns, filtering rows, sorting, standardizing, or writing a cleaned CSV.
Use this skill when the user makes a technical decision, establishes a new pattern, defines business rules, or explicitly asks to remember or save a guideline. Also use this skill when you are about to implement a feature, write code, plan an architecture, or make a technical decision - you MUST retrieve contextual memory first to follow established patterns. Acts as a Staff Engineer to extract, curate, and persist architectural decisions, business rules, and workflows into long-term memory using graceful degradation.
Explore an Obsidian vault using Enzyme — surface connections between ideas, find latent patterns across notes. Use when the user wants to explore their thinking, draw connections, or search their vault by concept rather than keyword.
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Assess your codebase's resistance to AI-assisted development tools.
The Skill plugin gives the agent a persistent, structured knowledge base. Knowledge is organised into named **skills**, each containing any number of **items** — individual pieces of information. Items are indexed semantically, so the agent can search across all skills by meaning rather than exact keywords.
Analyze code and LLM practices against Claude Code's production-grade engineering patterns. Use when the user asks to 'assess my code against Claude Code', 'how would Claude Code do this', 'what patterns does CC use for X', 'review my LLM approach', or invokes /what-would-cc-do:assess or /what-would-cc-do:claudecodefy.
Monitor and query Claude Code sessions — list sessions, search conversations, check costs, view AI fluency score, see live running agents. Use when the user asks about their Claude Code usage, costs, session history, or running agents. --- ## You operate the `claude-view` HTTP API **If the claude-view MCP tools are available in your environment, prefer using them instead of curl.** This skill is the fallback for environments without MCP support. claude-view runs a local server on port 47892 (or `$CLAUDE_VIEW_PORT`). All endpoints return JSON (camelCase field names). Base URL: `http://localhost:47892` ## Resolving the server 1. Check if running: `curl -sf http://localhost:47892/api/health` 2. If not running, tell user: `npx claude-view` ## Endpoints | Intent | Method | Endpoint | Key Params | |--------|--------|----------|------------| | List sessions | GET | `/api/sessions` | `?limit`, `?q`, `?filter`, `?sort`, `?offset`, `?branches`, `?models`, `?time_after`, `?time_before` | | Get session detail | GET | `/api/sessions/{id}` | — | | Search sessions | GET | `/api/search` | `?q` (required), `?limit`, `?offset`, `?scope` | | Dashboard stats | GET | `/api/stats/dashboard` | `?project`, `?branch`, `?from`, `?to` | | AI Fluency Score | GET | `/api/score` | — | | Token stats | GET | `/api/stats/tokens` | — | | Live sessions | GET | `/api/live/sessions` | — | | Live summary | GET | `/api/live/summary` | — | | Server health | GET | `/api/health` | — | ## Reading responses All responses are JSON with camelCase field names. Key shapes: **Sessions list:** `{ sessions: [{ id, project, displayName, gitBranch, durationSeconds, totalInputTokens, totalOutputTokens, primaryModel, messageCount, turnCount, commitCount, modifiedAt }], total, hasMore }` **Session detail:** All session fields plus `commits: [{ hash, message, timestamp, branch }]` and `derivedMetrics: { tokensPerPrompt, reeditRate, toolDensity, editVelocity }` **Search:** `{ query, totalSessions, totalMatches, elapsedMs,
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:
