Total 50,523 skills, AI & Machine Learning has 8481 skills
Showing 12 of 8481 skills
Scan the portfolio for the highest-leverage AI opportunities and rank where to deploy operating-partner time. Ingests quarterly updates and financials across multiple portfolio companies, identifies quick wins at each, and stacks them into a single ranked action list. Use during quarterly portfolio reviews, annual planning, or when deciding which companies get AI investment first. Triggers on "AI readiness", "AI opportunity scan", "where should we deploy AI", "AI across the portfolio", "AI quick wins", or "which portcos are ready for AI".
Scaffold and fully configure a new Agentic Coding Starter Kit project — a Next.js 16 + TypeScript + Better Auth + Drizzle + PostgreSQL + AI SDK boilerplate. Use this skill whenever the user asks to set up, scaffold, create, initialize, or bootstrap an "agentic coding starter kit", "agentic app", "agentic boilerplate", a "Next.js app with auth and db", or mentions `create-agentic-app` / `npx create-agentic-app`. Walks the user through folder strategy, package-manager choice, Postgres setup (Docker / Neon / Vercel / BYO), OpenRouter AI configuration, migrations, a build check, and dev-server verification — ending with a working http://localhost:3000.
Read a story file and implement it. Loads the full context (story, GDD requirement, ADR guidelines, control manifest), routes to the right programmer agent for the system and engine, implements the code and test, and confirms each acceptance criterion. The core implementation skill — run after /story-readiness, before /code-review and /story-done.
Redis vector search guidance covering HNSW vs FLAT algorithm choice, vector index configuration (dims, distance metric, datatype), filtered hybrid search combining vector similarity with TAG or NUMERIC filters, and the RAG retrieval pattern with RedisVL. Use when defining a VECTOR field in FT.CREATE, integrating embeddings (OpenAI, Cohere, sentence-transformers), tuning HNSW parameters (M, EF_CONSTRUCTION, EF_RUNTIME), building a retrieval-augmented generation pipeline, or filtering vector results by attribute.
MCP server for querying and analyzing Facebook Ads Library data with batch processing and AI-powered video/image analysis
Summarizes a given article or webpage into key bullet points
Persistent memory system for Claude Code. Two-layer architecture (hot cache + knowledge wiki), safety hooks, /close-day end-of-day synthesis. Zero external dependencies.
Add an MCP server to pi. Use when asked to "add mcp server", "configure mcp", "add mcp", "new mcp server", "setup mcp", "connect mcp server", or "register mcp server". Handles both global and project-local configurations.
Single-pass feature implementation using Explore → Code → Test. Ships focused changes at maximum speed, with a built-in circuit breaker that stops and recommends `/apex` or `/forge` when the task turns out more complex than it looked. Use this whenever the user wants a quick win on a single, focused task — even when they don't say "oneshot" (e.g. "just", "quickly", "small change", "#42", or a GitHub issue URL for a small fix).
Automated content creation system with AI research, multi-format writing, and video generation using Claude, OpenAI, and Remotion
Bootstrap skill — teaches the agent how to find and invoke skills. Use when starting any new task or session.
Apply when context is filling up: large outputs, long files, repeated reads, fan-out planning. Route bulk to subagents; keep summaries in the main thread, not raw payloads.