Total 55,943 skills, AI & Machine Learning has 9312 skills
Showing 12 of 9312 skills
Multi-perspective code analysis using three AI personas (RYAN, FLASH, SOCRATES) for comprehensive decision-making. Use when complex code decisions need analysis from multiple viewpoints, or when avoiding single-perspective blind spots is critical.
After an agentic task completes, perform a retrospective analysis across 6 dimensions (goal alignment, efficiency, decision quality, error handling, communication, reusability). Score performance, identify inefficiency patterns, evaluate skill usage, and produce actionable improvement recommendations. Triggers on "how did it go", "retrospective", "review performance", "what could be better", or after any long agentic task completes.
Master skill for parallel subagent-driven execution with automatic fallback to single-agent sequential mode. Use when implementing plans with multiple independent sub-phases (SP1, SP2...) to dispatch parallel subagents, or when requiring code review between implementation and testing.
Local vision-language model for image analysis using SmolVLM-2B
Guide for creating and validating Claude Code plugin.json files. Use when creating plugins, validating plugin schemas, or troubleshooting plugin configuration.
Implements tracker subtasks tagged `implement`, publishes/updates the PR, and routes review using handoff-first context loading, lazy artifact reads, and rework_mode support.
Instruments Python and TypeScript code with MLflow Tracing for observability. Triggers on questions about adding tracing, instrumenting agents/LLM apps, getting started with MLflow tracing, or tracing specific frameworks (LangGraph, LangChain, OpenAI, DSPy, CrewAI, AutoGen). Examples - "How do I add tracing?", "How to instrument my agent?", "How to trace my LangChain app?", "Getting started with MLflow tracing", "Trace my TypeScript app"
Gemini CLI consultation workflow for coding agents. Use when technical tasks need Gemini consultation for decisions, planning, debugging, problem-solving, or pre-implementation guidance.
Reads invoice images and returns structured data. It can be called by other skills or directly by users.
Reads images of receipts, payment receipts, and Furusato Nozei donation receipts and returns structured data. It can be called from other skills or directly by users.
Build production-ready MCP servers using FastMCP v3. Guides research, scaffolding, tool/resource/prompt implementation, testing, and deployment. Targets FastMCP 3.0.0rc2 with Providers, Transforms, middleware, OAuth, and composition. Use when creating MCP servers, integrating APIs via MCP, converting OpenAPI specs or FastAPI apps, or troubleshooting FastMCP issues. NOT for building REST APIs, CLI tools, or non-MCP integrations.
Integrates Flowlines observability SDK into Python LLM applications. Use when adding Flowlines telemetry, instrumenting LLM providers, or setting up OpenTelemetry-based LLM monitoring.