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Found 1,270 Skills
Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpoin...
Comprehensive documentation audit and generation. Launches parallel agents for high-level docs, module-level docs, decision records, and state diagrams. Use when: documentation gaps, post-implementation docs, README updates, architecture docs.
Configure a Stop hook that surfaces unfinished todos before a session ends and suggests creating GitHub issues for deferred work. Use when you want unfinished Claude Code session tasks automatically flagged for GitHub issue creation at session end.
TuriX Computer Use Agent for macOS desktop automation. Use when you need to perform visual UI tasks that lack CLI or API access, such as opening apps, clicking buttons, navigating GUIs, or multi-step visual workflows.
Open standards and governance rules for Agent Skills. It is used for creation, modification, refactoring, migration, audit and maintenance of skills, and provides platform-independent structural standards, frontmatter specifications, progressive disclosure and quality gates.
The entry point for building a client website. Walks you through a structured intake — business details, design preferences, content needs — then orchestrates the full build and deploy pipeline.
Defragment and reorganize agent memory files: split bloated files, merge duplicates, remove stale information, and restructure the memory hierarchy. Use when memory files have grown unwieldy, contain redundancies, or need reorganization. Run periodically (weekly) or on demand.
Build and deploy agentic finance applications on the Alva platform. Access 250+ financial data sources (crypto, equities, macro, on-chain, social), run cloud-side analytics, backtest trading strategies, and release interactive playbooks -- all from your AI agents.
Use when a migration is already known to stay on the LangChain agent side, including agent setup, tools, structured output, retrieval, and short-term memory.
OpenClaw-RL framework for training personalized AI agents via reinforcement learning from natural conversation feedback
#1 on DeepResearch Bench (Feb 2026). Any-to-Any AI for agents. Combines deep reasoning with all modalities through sophisticated multi-agent orchestration. Research, videos, images, audio, dashboards, presentations, spreadsheets, and more.
2-stage pipeline: trace (causal investigation) -> deep-interview (requirements crystallization) with 3-point injection