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Found 3,132 Skills
Build, refactor, debug, or review a Convex backend inside a Next.js app. Use when the user mentions Convex, `convex/nextjs`, `npx convex dev`, `NEXT_PUBLIC_CONVEX_URL`, `useQuery`, `useMutation`, `usePaginatedQuery`, schema/indexes, auth, App Router server components/actions, realtime data, chat, notifications, collaborative features, or deploying Convex with Vercel. Also use when deciding whether Convex is a good fit for a Next.js app that needs reactive shared state. Do not use for generic frontend-only Next.js work or non-Convex backends unless the task is specifically about adopting, migrating to, or evaluating Convex.
Searches the web via Exa’s Search API and returns source URLs (optionally with highlights, full text, summaries, and subpages). Use when the user asks to “search with Exa”, “use Exa”, “find sources/URLs”, “do web research”, “retrieve webpage text”, “get highlights/summaries”, “filter by domain/date/category”, or needs fresh results (news, real-time lookups).
Used in Git development when you need to abandon current attempts and roll back to a historical commit. It automatically archives the current state to the archive/ branch and establishes bidirectional links (source and target) in ARCHIVE.md to ensure traceability of the development decision flow. Suitable for scenarios requiring safe rollback while retaining the context of failed attempts.
Help users discover and install Claude Code skills. Use this skill when users ask "how to do X", "find a skill that can do X", "are there any skills that can..." or want to expand AI capabilities. Search, verify quality, and assist with installation through the skills.sh ecosystem.
Orchestrate multi-agent AI workflows with ultrawork, discipline agents, team mode, and hash-anchored editing for autonomous code development
TypeScript-native multi-agent orchestration framework that decomposes goals into task DAGs automatically with MCP and live tracing
A minimal teaching framework for understanding AI Agent architecture with core loop, fake LLM interface, and skill discovery system
Self-evolving autonomous agent framework with skill tree growth, browser/desktop/mobile control, and hierarchical memory system
Build AI agents with in-process agent loops using Anthropic or OpenAI APIs, custom tools, MCP servers, and multi-turn conversations
Self-referential self-improving AI agents that optimize for any computable task using meta-learning and code generation
Install and use World2Agent (W2A) sensors to give AI agents structured, real-time perception of the real world
Build and run durable background coding agents with workflow orchestration, isolated sandboxes, and GitHub integration on Vercel.