Interact with Google NotebookLM notebooks — chat with the AI, generate artifacts (slides, audio, video, mind maps, quizzes, flashcards, infographics, reports, data tables), manage sources (add URLs, YouTube, files, text), run research (fast/deep web research), and manage notes. Use when the user wants to query, create content from, or manage their NotebookLM notebooks and sources.
Manage home media services through TsArr from OpenClaw. Use for Radarr, Sonarr, Lidarr, Readarr, Prowlarr, Bazarr, qBittorrent, and Seerr tasks such as checking health, inspecting queues and history, browsing libraries, searching, adding, editing, deleting items, viewing profiles, tags, and root folders, managing torrents, managing media requests, and checking TsArr configuration.
Manage authentication for Get笔记 CLI
DAG-based lossless context management — search, expand, and recall from your full conversation history
This skill should be used when the user asks to "add accessibility", "check ARIA", "handle keyboard navigation", "add focus management", or creates UI components, forms, or interactive elements. Provides WCAG 2.2 AA patterns for keyboard navigation, ARIA roles and states, focus management, color contrast, and screen reader support.
Frappe Bench CLI command reference for site management, app management, development, and production operations. Use when running bench commands, managing sites, migrations, builds, or deployments.
Manage Apple Notes via the `memo` CLI on macOS (create, view, edit, delete, search, move, and export notes). Use when a user asks to add a note, list notes, search notes, or manage note folders.
Universal Compliance Engine for Global Product Management.
Webex CLI: query and manage Webex Admin, Calling, Contact Center, Devices, Meetings, and Messaging APIs via the `webex` command-line tool. Use for listing resources, checking configurations, debugging API calls, and administering Webex environments.
Initialize PROJECT_MANAGE.md with repository-specific project management rules, GitHub Project board binding, and field mappings. Use when setting up project management for a repository for the first time.
Expert FinOps guidance covering cloud, AI, and SaaS technology spend. Includes AI cost management, GenAI capacity planning, Anthropic billing, AWS (EC2, Bedrock, Savings Plans, CUR, commitment strategy), Azure (reservations, Savings Plans, AHB, OpenAI PTUs, portfolio liquidity), GCP (Vertex AI, Compute Engine, BigQuery), tagging governance, SaaS management (SAM, licence optimisation, SMPs, shadow IT), AI coding tools (Cursor, Claude Code, Copilot, Windsurf, Codex), ITAM, Databricks, Snowflake, OCI, and GreenOps. Use for any query about technology cost, commitment portfolio management, rightsizing, cost allocation, SaaS sprawl, AI dev tool spend, or connecting spend to business value. Built by OptimNow. --- # FinOps - Expert Guidance > Built by OptimNow. Grounded in hands-on enterprise delivery, not abstract frameworks. --- ## How to use this skill This skill covers cloud, AI, SaaS, and adjacent technology spend domains. Read `references/optimnow-methodology.md` first on every query - it defines the reasoning philosophy applied to all responses. Then load the domain reference that matches the query. ### Domain routing | Query topic | Load reference | |---|---| | AI costs, LLM inference, token economics, agentic cost patterns, AI ROI, AI cost allocation, GPU cost attribution, RAG harness costs | `references/finops-for-ai.md` | | AI investment governance, AI Investment Council, stage gates, incremental funding, AI value management, AI practice operations | `references/finops-ai-value-management.md` | | GenAI capacity planning, provisioned vs shared capacity, traffic shape, spillover, throughput units | `references/finops-genai-capacity.md` | | AWS billing, EC2 rightsizing, RIs, Savings Plans, commitment strategy, portfolio liquidity, phased purchasing, CUR, Cost Explorer, EDP negotiation, RDS cost management, database commitments | `references/finops-aws.md` | | AWS Bedrock billing, Bedrock provisioned throughput, model unit pricing, Bedrock batch inference | `referenc
Guide for developing and maintaining the Hubitat MCP Rule Server — a Groovy-based MCP server running natively on Hubitat Elevation hubs, exposing 89 tools (34 on tools/list via category gateway proxy) for device control, virtual device management, room management, rule automation, hub admin, file management, app/driver management, installed-app visibility, Rule Machine interoperability, native rule CRUD, and Developer Mode self-administration.
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:
