Total 51,031 skills, AI & Machine Learning has 8547 skills
Showing 12 of 8547 skills
Analyze a task, pick the right fleet type, and generate a ready-to-launch fleet (fleet.json + prompt.md files). Discovers available fleet skills dynamically. Use when the user wants to run work in parallel, asks to "plan a fleet", or says "fleet-plan".
Agent skill for implementer-sparc-coder - invoke with $agent-implementer-sparc-coder
Agent skill for agent - invoke with $agent-agent
TensorLake SDK for building agentic workflows, sandboxed code execution, and document parsing/extraction. Use when the user mentions tensorlake, or asks about TensorLake APIs/docs/capabilities. Also use when the user is building AI agents or agentic applications that need serverless workflow orchestration (parallel map/reduce DAGs), sandboxed execution of LLM-generated code, or document parsing, structured extraction, and OCR from PDFs/images. Works with any LLM provider (OpenAI, Anthropic), agent framework (LangChain, CrewAI, LlamaIndex), database, or API as the infrastructure layer.
When the user wants to build or improve a sales bot's ability to send brochures, videos, floorplans, or other media contextually during conversations. Also use when the user mentions "sending attachments," "media sharing," "document delivery," "sending brochures," or "content delivery."
Build AI agents with Pydantic AI — tools, capabilities, structured output, streaming, testing, and multi-agent patterns. Use when the user mentions Pydantic AI, imports pydantic_ai, or asks to build an AI agent, add tools/capabilities, stream output, define agents from YAML, or test agent behavior.
Track AI token consumption, costs, and usage trends using the orbit CLI. Use this skill whenever the user asks about token usage, AI costs, Claude Code spending, how many tokens were used, cost breakdown by model, session history, or token analytics. Trigger on phrases like 'how much have I spent', 'token usage', 'show me costs', 'what's my AI spending', 'how many tokens today', 'cost per model', 'list sessions', 'track usage', 'token report', 'weekly usage', 'monthly costs', or any token/cost tracking task — even casual references like 'am I spending too much on Claude', 'what did that session cost', 'show me the dashboard', or 'how much is opus costing us'.
Production-grade Next.js chatbot builder. Covers tool calling with human-in-the-loop (HITL) approval, PostgreSQL session persistence, GDPR consent gating, SQL-first search, per-tool UI rendering, message feedback, and follow-up suggestions. Use when building chat apps, conversational AI interfaces, customer support bots, or any chatbot needing database-backed sessions, tool approval workflows, consent gating, or custom tool output components. Reference implementation: fair-helpdesk project.
Domain-specific testing patterns for episodic memory operations. Use when testing episode lifecycle, pattern extraction, reward scoring, or memory retrieval.
Create new Claude Code skills with proper structure, YAML frontmatter, and best practices. Use when creating reusable knowledge modules, adding specialized guidance, or building domain-specific expertise.
Retrieve relevant episodic context from memory for informed decision-making. Use when you need past episodes, patterns, or solutions to similar tasks.
Resolve queries or URLs into compact, LLM-ready markdown using a low-cost cascade. Prioritizes llms.txt for structured docs, uses web fetch/search tools for extraction. Use when you need to fetch documentation, resolve web URLs to markdown, search for technical content, or build context from web sources.