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Found 7,978 Skills
Agentic Workflow Pattern
Generate, validate and publish workflow, sequence and architecture diagrams, using FlowZap Code DSL. Use when the user asks to create a workflow, flowchart, sequence diagram, process map or architecture diagram. Produces .fz code and shareable playground URLs via the FlowZap MCP server or public API.
Kotlin Flow - StateFlow, SharedFlow, operators, testing
Designs git workflows covering branching strategies, trunk-based development, stacked changes, conventional commits, CI/CD pipelines, and repository hygiene. Use when setting up branching models, writing commit messages, configuring GitHub Actions, managing stacked PRs, cleaning stale branches, creating issue templates, or recovering lost commits.
Use when coordinating handoffs between workflows (e.g., skill-editor to programming-pm). Provides universal handoff schema v3.0, validation rules, distributed tracing conventions, and workflow discovery via frontmatter metadata.
Orchestrate the full skill development lifecycle from idea to publication. Guides through ideation, requirements definition, skill implementation, and marketplace registration. Use when: "create a new skill end-to-end", "skill development workflow", "build and publish a skill", "スキル開発ワークフロー", "スキルを作って公開", "新しいスキルを開発".
Searches and retrieves MLflow documentation from the official docs site. Use when the user asks about MLflow features, APIs, integrations (LangGraph, LangChain, OpenAI, etc.), tracing, tracking, or requests to look up MLflow documentation. Triggers on "how do I use MLflow with X", "find MLflow docs for Y", "MLflow API for Z".
Analyzes an MLflow session — a sequence of traces from a multi-turn chat conversation or interaction. Use when the user asks to debug a chat conversation, review session or chat history, find where a multi-turn chat went wrong, or analyze patterns across turns. Triggers on "analyze this session", "what happened in this conversation", "debug session", "review chat history", "where did this chat go wrong", "session traces", "analyze chat", "debug this chat".
Use this when you need to EVALUATE OR IMPROVE or OPTIMIZE an existing LLM agent's output quality - including improving tool selection accuracy, answer quality, reducing costs, or fixing issues where the agent gives wrong/incomplete responses. Evaluates agents systematically using MLflow evaluation with datasets, scorers, and tracing. Covers end-to-end evaluation workflow or individual components (tracing setup, dataset creation, scorer definition, evaluation execution).
Analyzes a single MLflow trace to answer a user query about it. Use when the user provides a trace ID and asks to debug, investigate, find issues, root-cause errors, understand behavior, or analyze quality. Triggers on "analyze this trace", "what went wrong with this trace", "debug trace", "investigate trace", "why did this trace fail", "root cause this trace".
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"
Fetches aggregated trace metrics (token usage, latency, trace counts, quality evaluations) from MLflow tracking servers. Triggers on requests to show metrics, analyze token usage, view LLM costs, check usage trends, or query trace statistics.