Total 54,224 skills, AI & Machine Learning has 9017 skills
Showing 12 of 9017 skills
Analyzes and optimizes an existing agent skill for conciseness, discoverability, and adherence to best practices. Use when a skill needs improvement, is too verbose, has poor activation rates, or fails to follow progressive disclosure patterns. Do NOT use for creating a new skill from scratch — use create-skill instead.
Guide for setting up AI configuration in your application. Helps you choose between agent vs completion mode, select the right approach for your stack, and create AI Configs that make sense for your use case.
Guidance for solving ARC-AGI style pattern recognition tasks that involve git operations (fetching bundles, merging branches) and implementing algorithmic transformations. This skill applies when tasks require merging git branches containing different implementations of pattern-based algorithms, analyzing input-output examples to discover transformation rules, and implementing correct solutions. (project)
Dispatch and coordinate parallel agent execution. Manages concurrent task processing with result aggregation and error handling.
Expert guidance for natural language processing development using transformers, spaCy, NLTK, and modern NLP techniques.
Create subagent definitions (agent.md files) for independent AI workers. Use when user wants to: create an agent, build a grader/evaluator, make an A/B comparator, spawn independent workers, or create something that runs in isolation. Triggers on: '创建 agent', 'subagent', 'grade outputs independently', 'blind comparison', 'run this in parallel'. Do NOT use for skills (use trae-skill-writer) or rules (use trae-rules-writer).
Generate AI videos using varg SDK React engine. Use when creating videos, animations, talking characters, slideshows, or social media content.
Generate professional brochure designs using each::sense AI. Create tri-fold, bi-fold, corporate, travel, product, real estate, healthcare, educational, event, and service brochures with print-ready layouts.
Knowledge base for designing, reviewing, and linting agentic AI infrastructure. Use when: (1) designing a new agentic system and need to choose patterns, (2) reviewing an existing agentic architecture ADR or design doc for gaps/risks, (3) applying the lint script to an ADR markdown file to get structured findings, (4) looking up a specific agentic pattern (prompt chaining, routing, parallelization, reflection, tool use, planning, multi-agent collaboration, memory management, learning/adaptation, MCP, goal setting, exception handling, HITL, RAG, A2A, resource optimization, reasoning techniques, guardrails, evaluation, prioritization, exploration/discovery). All rules and guidance are grounded in the PDF "Agentic Design Patterns" (482 pages).
Systematic hook debugging workflow. Use when hooks aren't firing, producing wrong output, or behaving unexpectedly.
Set up and run the autonomous agent loop — auto-resolves prerequisites (MCP, wallet, registration), scaffolds files, enters perpetual cycle. Compatible with Claude Code and OpenClaw.
Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.