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Found 5,602 Skills
Before starting a task or taking a critical step, surface and verify the assumptions the agent is making. Checks 4 types - technical (libraries, APIs), data (files, formats), business logic (rules, scope), and user intent (what the user actually wants). Triggers on ambiguous requests, multi-step tasks, or whenever "are you sure", "check first", "don't assume" appears.
When a step fails during an agentic task, classify the error (transient, configuration, logic, or permanent), apply the right recovery strategy, and escalate to the user when all strategies are exhausted. Triggers on error messages, exceptions, tracebacks, "failed", "not working", "retry", or when 2 consecutive steps fail.
Cross-tool compatibility workflow. Use when generating AGENTS.md files for compatibility with other AI coding tools, or creating tool-specific instruction files from CLAUDE.md.
Agentic Workflow Pattern
Orchestrator-only workflow for migrating/rewriting codebases with full TDD and agent delegation
Background Agent Pings
Create, validate, and convert skills for the agent ecosystem. Enforces standardized structure for consistency. Enables self-evolution by creating new skills on demand, converting MCP servers and codebases to skills.
General RPI (Research, Plan, Implement, Iterate) execution skill. It is used for engineering tasks where users require "research first, then plan, then implement, and finally iterate", or when tasks are highly complex, high-risk, or have unclear impact. This skill does not rely on specific command-line tools or platforms, and is applicable to any AI Agent that supports skill mechanisms.
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"
This skill should be used when cleaning up codebases that have accumulated dead code, redundant implementations, and orphaned artifacts — especially codebases maintained by coding agents. Triggers on "find dead code", "clean up unused code", "remove redundant code", "prune this codebase", "dead code sweep", "code cleanup", or when a codebase has gone through multiple agent-driven refactors and likely contains overlooked remnants. Systematically identifies cruft, categorizes findings, and removes confirmed dead code with user approval.
Send and receive cryptographically signed messages between AI agents using the Agent Messaging Protocol (AMP). Use when the user asks to "send a message to an agent", "check agent inbox", "message another agent", "reply to a message", "notify an agent", or any inter-agent communication task.