Total 53,177 skills, AI & Machine Learning has 8896 skills
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Comprehensive Chinese guide for Hermes Agent framework covering installation, architecture, memory systems, skills, tools, multi-agent orchestration, and monetization strategies
Blind Spot Scan —— Read all your conversations with AI from yesterday, identify exposed cognitive blind spots (not gaps in knowledge, but thinking habits that keep certain truths consistently invisible), then select a precise chapter from WeChat Reading to fill the gap and compile a complete analysis note. Use when user says '扫盲区', '盲区', '照盲区', '看看我的思维盲区', '我昨天想漏了什么', 'blind spot', 'ljg-blind', '/ljg-blind', or requests analysis of yesterday's AI conversations for cognitive blind spots along with a targeted weread chapter to address them. Optional parameter: Pass a date (YYYY-MM-DD) to scan that day instead of yesterday. NOT FOR deep diving into a single opinion (use ljg-think), ranking down a field (use ljg-rank), reading along with a text (use ljg-read), or finding constraints in a field (use ljg-constraint).
agent-team: Read messages for one recipient agent.
Loads and manages company context for all C-suite advisor skills. Reads ~/.claude/company-context.md, detects stale context (>90 days), enriches context during conversations, and enforces privacy/anonymization rules before external API calls.
Format prompts for different LLM providers with chat templates and HNSW-powered context retrieval
Builds generative AI applications on Amazon Bedrock. Covers model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore. Use when invoking models, setting up Knowledge Bases, creating agents, applying guardrails, deploying to AgentCore, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing models (Claude, Llama, Nova, Titan). ALSO USE for prompt caching setup and debugging, quota health checks and throttling diagnosis, cost attribution and tracking, migrating between Claude model generations (4.5 to 4.6 to 4.7), chunking strategies, API selection (Converse vs InvokeModel), guardrail capabilities, and model selection. NOT for custom model training, Rekognition, or Comprehend.
Router skill for LLMQuant options workflows. Use when the user needs IV rank, option scoring, strategy construction, Greeks, P&L simulation, volatility surface, unusual activity, earnings IV crush, backtests, or hedges.
Build strong Codex Goals from rough user objectives. Use when the user asks to create, write, generate, improve, expand, or refine a Codex `/goal`; mentions Codex Goals, goal mode, persistent objectives, "持续执行", "扩充目标", "生成 goal", "keep working until", or wants Codex to ask clarifying questions before starting a long-running objective. Helps draft evidence-based goal text and may start a goal only after explicit user approval.
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra, ru-lite, ru-full, ru-ultra, ru-notes. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Russian mode: "пещерный режим", "режим пещерного", "/caveman ru", "/caveman-ru". Also auto-triggers when token efficiency is requested.
Deploy a 24/7 Claude Code agent with persistent Chromium browser on any Ubuntu VPS, controllable via Telegram
Agent Workspace Migration. Organize any project into a long-term maintainable Agent workspace with consistent support for both Claude Code and Codex: audit rule files, identify source-of-truth skills, standardize naming conventions, and generate bridges. Triggers: /dbs-agent-migration, /agent-migration, "migrate to Codex", "migrate to Claude Code", "unify AGENTS.md", "organize skill bridges", "my Agent workspace is messy", "help me unify Claude and Codex" Agent workspace migration. Turn any project into a maintainable Claude Code / Codex dual-host workspace by auditing rule files, establishing source-of-truth skills, normalizing names, and generating bridges. Trigger: /dbs-agent-migration, /agent-migration, "migrate to Codex", "migrate to Claude Code", "fix AGENTS.md", "organize skill bridges"
High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent systems, or integration with game environments (Atari, Procgen, NetHack). Achieves 2-10x speedups over standard implementations. For quick prototyping or standard algorithm implementations with extensive documentation, use stable-baselines3 instead.