Agent Skills
Instruction packs that give your AI agent know-how — some work anywhere, some only with the tool they came with.
✦ Standalone skills2,378
Self-contained. Install one into any project and it works on its own — no other software needed.
🧰 Tool add-ons428
Come bundled with a specific tool and only work together with it — they teach your agent how to operate that tool.
🔌 Needs setup first94
Each relies on something you set up separately — such as an app, a command-line tool or an account — and its page names what that is.
mastra-ai
2,378 standalone skillsagent-graphs
✓★ 19by launchdarkly
Create and manage agent graphs — directed graphs of configs connected by edges with handoff logic. Use when building multi-agent workflows where configs route to each other.
launchdarkly-flag-command
✓★ 19by launchdarkly
Resolve `/flag` style requests into the right LaunchDarkly flag lookup flow. Use when the user types `/flag`, asks to quickly find a flag by name/key, wants a direct flag detail summary, or needs fast disambiguation between similar flags.
snippets
✓★ 19by launchdarkly
Create and manage prompt snippets — reusable text blocks referenced inside config variation prompts. Keeps common instructions, personas, and guardrails consistent across multiple configs.
tinybird-python-sdk-guidelines
★ 19by tinybirdco
Tinybird Python SDK for defining datasources, pipes, and queries in Python. Use when working with tinybird-sdk, Python Tinybird projects, or data ingestion and queries in Python.
tinybird-typescript-sdk-guidelines
★ 19by tinybirdco
Tinybird TypeScript SDK for defining datasources, pipes, and queries with full type inference. Use when working with @tinybirdco/sdk, TypeScript Tinybird projects, or type-safe data ingestion and queries.
create-page
✓★ 17by notion
Create a new Notion page, optionally under a specific parent. Automatically structures content based on page type (meeting notes, project pages, etc.).
create-task
✓★ 17by notion
Create a new task in the user's Notion tasks database with sensible defaults for due date, status, owner, and project.
search
✓★ 17by notion
Search the user's Notion workspace using the Notion MCP server. Use for finding pages, databases, and content by keywords or natural-language queries.
spec-to-implementation
✓★ 17by notion
Turn product or tech specs into concrete Notion tasks. Breaks down spec pages into detailed implementation plans with clear tasks, acceptance criteria, and progress tracking.
tasks-build
✓★ 17by notion
Build a task from a Notion page URL. Fetches task details, marks it in progress, implements the work, and updates status in Notion.
find
✓★ 17by notion
Quickly find pages or databases in Notion by title keywords. Returns precise matches rather than comprehensive results.
create-database-row
✓★ 17by notion
Insert a new row into a specified Notion database using natural-language property values. Handles property name matching and validation.
database-query
✓★ 17by notion
Query a Notion database by name or ID and return structured, readable results with optional filters and sorting.
research-documentation
✓★ 17by notion
Search across your Notion workspace, synthesize findings from multiple pages, and create comprehensive research documentation with proper citations and actionable insights.
tasks-explain-diff
✓★ 17by notion
Generate a rich Notion document explaining code changes. Creates comprehensive documentation with background, intuition, code walkthrough, and verification steps.
tasks-plan
✓★ 17by notion
Create an implementation plan from a Notion task or specification. Breaks down requirements into actionable steps with estimates and dependencies.
tasks-setup
✓★ 17by notion
Set up a Notion task board for tracking tasks. Guides users through using a template or connecting an existing board.
knowledge-capture
✓★ 17by notion
Transform conversations and discussions into structured documentation pages in Notion. Captures insights, decisions, and knowledge from chat context with proper organization and linking.
meeting-intelligence
✓★ 17by notion
Prepare meeting materials by gathering context from Notion, enriching with research, and creating both an internal pre-read and external agenda saved to Notion.
sandbox-agent
★ 16by rivet-dev
Deploy, configure, and integrate Sandbox Agent - a universal API for orchestrating AI coding agents (Claude Code, Codex, OpenCode, Amp) in sandboxed environments. Use when setting up sandbox-agent server locally or in cloud sandboxes (E2B, Daytona, Docker), creating and managing agent sessions via SDK or API, streaming agent events and handling human-in-the-loop interactions, building chat UIs for coding agents, or understanding the universal schema for agent responses.
slack-agent
★ 16by vercel
Use when working on Slack agent/bot code, Chat SDK applications, Bolt for JavaScript projects, or projects using @chat-adapter/slack or @slack/bolt. Provides development patterns, testing requirements, and quality standards.
geist-learning-lab
★ 15by vercel
Build explorative, interactive learning experiences as Next.js apps using the Geist design system. Use when creating tutorials, explorable explanations, interactive lessons, code sandboxes, quizzes, or any educational UI. Covers the Learning Loop pedagogy, 23+ learning component patterns, progress tracking, spaced repetition, and Bret-Victor-style interactive exploration — all with Geist's dark-first minimal aesthetic.
developing-genkit-go
★ 14by genkit-ai
Develop AI-powered applications using Genkit in Go. Use when the user asks to build AI features, agents, flows, or tools in Go using Genkit, or when working with Genkit Go code involving generation, prompts, streaming, tool calling, or model providers.
pinecone-full-text-search
✓★ 14by pinecone-io
Create, ingest into, and query a Pinecone full-text-search (FTS) document index using the graduated document-schema API (Python SDK 10.0.0, API version 2026-07). Use when the user or agent asks to build a text search index on Pinecone, add dense or sparse vector fields, ingest documents, construct score_by clauses (text / query_string / dense_vector / sparse_vector), or compose with text-match filters ($match_phrase / $match_all / $match_any). Ships `scripts/ingest.py` for safe bulk ingestion (batch_upsert + error inspection + readiness polling); query construction is documented inline in this skill — write `documents.search(...)` calls directly, validated against `pc.indexes.describe(...)` output.
developing-genkit-dart
★ 14by genkit-ai
Generates code and provides documentation for the Genkit Dart SDK. Use when the user asks to build AI agents in Dart, use Genkit flows, or integrate LLMs into Dart/Flutter applications.
developing-genkit-python
★ 14by genkit-ai
Develop AI-powered applications using Genkit in Python. Use when the user asks about Genkit, AI agents, flows, or tools in Python, or when encountering Genkit errors, import issues, or API problems.
pinecone-n8n
✓★ 14by pinecone-io
Build n8n workflows using the Pinecone Assistant node or Pinecone Vector Store node. Use when building RAG pipelines, chat-with-docs workflows, configuring Pinecone nodes in n8n, troubleshooting Pinecone n8n nodes, or asking about best practices for Pinecone in n8n.
pinecone-quickstart
✓★ 14by pinecone-io
Interactive Pinecone quickstart for new developers. Choose between two paths - Database (create an integrated index, upsert data, and query using Pinecone MCP + Python) or Assistant (create a Pinecone Assistant for document Q&A). Use when a user wants to get started with Pinecone for the first time or wants a guided tour of Pinecone's tools.
pinecone-query
✓★ 14by pinecone-io
Query integrated indexes using text with Pinecone MCP. IMPORTANT - This skill ONLY works with integrated indexes (indexes with built-in Pinecone embedding models like multilingual-e5-large). For standard indexes or advanced vector operations, use the CLI skill instead. Requires PINECONE_API_KEY environment variable and Pinecone MCP server to be configured.
pinecone-cli
✓★ 14by pinecone-io
Guide for using the Pinecone CLI (pc) to manage Pinecone resources from the terminal. The CLI supports ALL index types (standard, integrated, sparse) and all vector operations — unlike the MCP which only supports integrated indexes. Use for batch operations, vector management, backups, namespaces, CI/CD automation, and full control over Pinecone resources.