Total 55,893 skills, AI & Machine Learning has 9298 skills
Showing 12 of 9298 skills
Creative problem-solving and ideation using SCAMPER, First Principles, Random Word, and AI-optimized techniques. Use when generating ideas, breaking creative blocks, brainstorming alternatives, or innovating.
This skill generates comprehensive chapter content for intelligent textbooks after the book-chapter-generator skill has created the chapter structure. Use this skill when a chapter index.md file exists with title, summary, and concept list, and detailed educational content needs to be generated at the appropriate reading level with rich non-text elements including diagrams, infographics, and MicroSims. (project, gitignored)
Optimize, rewrite, and evaluate prompts using the Anthropic 1P interactive prompt-engineering tutorial patterns (clear/direct instructions, role prompting, XML-tag separation, output formatting + prefilling, step-by-step “precognition”, few-shot examples, hallucination reduction, complex prompt templates, prompt chaining, and tool-use XML formats). Use for 提示词优化/Prompt优化/Prompt engineering, rewriting system+user prompts, enforcing structured outputs (XML/JSON), reducing hallucinations, building multi-step prompt templates, adding few-shot examples, or designing prompt-chaining/tool-calling scaffolds.
Build comprehensive AI-native brand asset systems that maintain consistency across all AI-generated content. Train AI tools on brand guidelines, create reusable prompt libraries, and manage visual/voice assets at scale. Use when ", " mentioned.
CRITICAL skill for executing multiple runSubagent calls in a SINGLE function_calls block for true parallelism. Essential for efficient multi-task workflows, subagent coordination, and maximizing throughput.
Smart article illustration skill. Analyzes article content and generates illustrations at positions requiring visual aids with multiple style options. Use when user asks to "add illustrations to article", "generate images for article", or "illustrate article".
Post-compaction context recovery. Detects in-progress RPI and evolve sessions, loads knowledge, shows recent work and pending tasks. Triggers: "recover", "lost context", "where was I", "what was I working on".
Structured multi-perspective deliberation through adversarial dialogue
Orchestrate in-session Task tool teams for parallel work. Fan-out research, implementation, review, and documentation across subagents. Use when: parallel tasks, fan-out, subagent team, Task tool, in-session agents.
Motto: The LLM is the dice. It narrates the outcome.
Build MCP servers in Python with FastMCP. Workflow: define tools and resources, build server, test locally, deploy to FastMCP Cloud or Docker. Use when creating MCP servers, exposing tools/resources/prompts to LLMs, building Claude integrations, or troubleshooting FastMCP module-level server, storage, lifespan, middleware, OAuth, or deployment errors.
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