Total 32,343 skills, AI & Machine Learning has 5224 skills
Showing 12 of 5224 skills
This skill should be used when the user asks to "track issues", "create beads issue", "show blockers", "what's ready to work on", "beads routing", "prefix routing", "cross-rig beads", "BEADS_DIR", "two-level beads", "town vs rig beads", "slingable beads", or needs guidance on git-based issue tracking with the bd CLI.
Build LLM applications with LangChain and LangGraph. Use when creating RAG pipelines, agent workflows, chains, or complex LLM orchestration. Triggers on LangChain, LangGraph, LCEL, RAG, retrieval, agent chain.
Shared reference documents for distributed mode skills (not directly invocable)
Deep codebase exploration. Triggers: research, explore, investigate, understand, deep dive, current state.
Agent Mail inbox monitoring. Check pending messages, HELP_REQUESTs, and recent completions. Triggers: "inbox", "check mail", "any messages", "show inbox", "pending messages", "who needs help".
Sequential reasoning with deep self-reflection and backtracking. Use when problems have step-by-step dependencies, need careful logical reasoning, or require error correction. Each step includes self-reflection, and incorrect steps trigger backtracking. Ideal for debugging, mathematical proofs, sequential planning, or causal analysis where order matters.
Trace knowledge artifact lineage and sources. Find orphans, stale citations. Triggers: "where did this come from", "trace this learning", "knowledge lineage".
Comprehensive AI writing detection patterns and methodology. Provides vocabulary lists, structural patterns, model-specific fingerprints, and false positive prevention guidance. Use when analyzing text for AI authorship or understanding detection patterns.
A skill that analyzes 18-month scenarios using news headlines as input. The main analysis is performed by the scenario-analyst agent, and a second opinion is obtained from the strategy-reviewer agent. Generates a comprehensive report in Japanese including primary, secondary, tertiary impacts, recommended stocks, and reviews. Example usage: /scenario-analyzer "Fed raises rates by 50bp" Triggers: news analysis, scenario analysis, 18-month outlook, medium-to-long-term investment strategy
Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.
Skill for creating AI agent projects using the VoltAgent framework. Guide for CLI setup and manual bootstrapping.
Look up VoltAgent documentation embedded in node_modules/@voltagent/core/docs for version-matched docs. Use for API signatures, guides, and examples.