Total 31,136 skills, AI & Machine Learning has 5040 skills
Showing 12 of 5040 skills
Evaluate and improve Claude Code commands, skills, and agents. Use when testing prompt effectiveness, validating context engineering choices, or measuring improvement quality.
Illustre automatiquement le journal d'une aventure BFRPG en générant des images pour les moments clés (combats, explorations, découvertes). Utilise la génération parallèle pour une performance optimale.
Reviews Claude configuration files for security, structure, and prompt engineering quality. Use when reviewing changes to CLAUDE.md files (project-level or .claude/), skills (SKILL.md), agents, prompts, commands, or settings. Validates YAML frontmatter, progressive disclosure patterns, token efficiency, and security best practices. Detects critical issues like committed settings.local.json, hardcoded secrets, malformed YAML, broken file references, oversized skill files, and insecure agent tool access.
Universal consolidation & audit skill for Claude Code skills. Analyzes project state, detects redundancies, and safely manages skills with backup, confirmations, and rollback capabilities. Never assumes without verifying actual code and usage patterns.
This skill should be used when the user asks to "create an MCP App", "add a UI to an MCP tool", "build an interactive MCP View", or needs guidance on MCP Apps SDK patterns, UI-resource registration, MCP App lifecycle, or host integration. Provides guidance for building MCP Apps with interactive UIs.
Use when diagnosing agent failures, debugging lost-in-middle issues, understanding context poisoning, or asking about "context degradation", "lost in middle", "context poisoning", "attention patterns", "context clash", "agent performance drops"
Use when building "MCP server", "Model Context Protocol", creating "Claude tools", "MCP tools", or asking about "FastMCP", "MCP SDK", "tool development for LLMs", "external API integration for Claude"
Use when optimizing agent context, reducing token costs, implementing KV-cache optimization, or asking about "context optimization", "token reduction", "context limits", "observation masking", "context budgeting", "context partitioning"
Use when "deploying ML models", "MLOps", "model serving", "feature stores", "model monitoring", or asking about "PyTorch deployment", "TensorFlow production", "RAG systems", "LLM integration", "ML infrastructure"
Use when "HuggingFace Transformers", "pre-trained models", "pipeline API", or asking about "text generation", "text classification", "question answering", "NER", "fine-tuning transformers", "AutoModel", "Trainer API"
Multi-agent orchestration workflow for deep research: Split a research objective into parallel sub-objectives, run sub-processes using Claude Code non-interactive mode (`claude -p`); prioritize installed skills for network access and data collection, followed by MCP tools; aggregate sub-results with scripts and refine them chapter by chapter, and finally deliver "finished report file path + summary of key conclusions/recommendations". Applicable scenarios: systematic web/data research, competitor/industry analysis, batch link/dataset shard retrieval, long-form writing and evidence integration, or scenarios where users mention "deep research/Deep Research/Wide Research/multi-agent parallel research/multi-process research".
Use when designing multi-agent systems, implementing supervisor patterns, coordinating multiple agents, or asking about "multi-agent", "supervisor pattern", "swarm", "agent handoffs", "orchestration", "parallel agents"