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Found 10,507 Skills
Mermaid Flowchart Generator - Auto-activating skill for Visual Content. Triggers on: mermaid flowchart generator, mermaid flowchart generator Part of the Visual Content skill category.
Turn raw PM content into a compliant, publish-ready skill by choosing build/add paths, running conformance checks, and updating docs before commit.
Enterprise template management with code boilerplates, feedback templates, and project optimization workflows
Create safe Conventional Commit messages and commits from current working tree changes.
This skill should be used when the user asks about "snow-flow commands", "CLI commands", "how to start", "swarm", "sparc", "orchestrator", "agent spawn", "memory", "task", or needs guidance on Snow-Flow CLI operations.
Unified test-fix pipeline combining test generation (session, context, analysis, task gen) with iterative test-cycle execution (adaptive strategy, progressive testing, CLI fallback). Triggers on "workflow:test-fix-gen", "workflow:test-cycle-execute", "test fix workflow".
Proposal-first development workflow with commit hygiene and decision authority rules. Enforces: propose before modifying, atomic commits, no force flags, warnings-as-errors. Use for any project where AI agents are primary developers and need guardrails.
Implement a feature or fix based on a Linear issue
This skill should be used when the user asks to "create chatbot", "virtual agent", "VA topic", "NLU", "conversation", "chat flow", "topic block", or any ServiceNow Virtual Agent development.
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).
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.