Total 30,768 skills, AI & Machine Learning has 4969 skills
Showing 12 of 4969 skills
You must use this when seeking cross-domain analogies, applying first-principles reasoning, or overcoming creative bottlenecks.
DSPy optimization workflows — teleprompters, metrics, evaluation, and compilation strategies. Use when optimizing DSPy programs with BootstrapFewShot, MIPROv2, or custom metrics.
This skill should be used when setting up or running the Ralph autonomous coding loop that iterates through stories, runs tests, commits, and logs learnings.
Create a new skill, and automatically initialize the plugin structure if needed
Teach Claude ANY topic - code libraries, APIs, concepts, tools, methodologies, or domains. Researches via web and docs, then retains knowledge as a permanent skill. Use when user says "/learn <topic>", "learn about X", "teach yourself Y", "become an expert on Z". Examples - "/learn stripe" for payments, "/learn GTD" for productivity, "/learn israeli-tax-law" for domain knowledge.
Execute plan files by launching multiple parallel subagents to complete tasks simultaneously. Triggers on explicit "/parallel-task" commands.
Interactive workflow for creating, configuring, connecting, and publishing AI agents on Agents.Hot using the agent-mesh CLI. Also covers CLI command reference, flags, skill publishing, and troubleshooting. Trigger words: create agent, manage agent, publish agent, agent description, agent setup, list agents, delete agent, connect agent, agent-mesh command, CLI help, agent-mesh flags, connect options, agent-mesh troubleshooting, TUI dashboard, publish skill, skill init, skill pack, skill version, skills list, unpublish skill, install skill, update skill, remove skill, installed skills.
Universal text artifact optimizer using GEPA's optimize_anything API for code, prompts, agent architectures, configs, and more
Classify domain (tech, finance, legal).
Uses a local model to describe something about an image
Coordinates skills, frameworks, and workflows throughout the project lifecycle using pattern-based sequencing, goal decomposition, phase-gate validation, and multi-agent orchestration. Use when starting multi-phase projects, sequencing frameworks, decomposing goals into capability plans, validating phase-gate readiness, coordinating subagents, or designing MCP-based tool orchestration.
Provide concrete examples—existing code patterns, style samples, input/output pairs—to guide AI toward your project's conventions