Total 31,089 skills, AI & Machine Learning has 5030 skills
Showing 12 of 5030 skills
Agent Context Isolation
Store a learning, pattern, or decision in the memory system for future recall
Ensures alignment between user and Claude during feature/spec planning through a structured interview process. Use this skill when the user invokes /plan-interview before implementing a new feature, refactoring, or any non-trivial implementation task. The skill runs an upfront interview to gather requirements across technical constraints, scope boundaries, risk tolerance, and success criteria before any codebase exploration. Do NOT use this skill for: pure research/exploration tasks, simple bug fixes, or when the user just wants standard planning without the interview process.
Write structured VGL (Visual Generation Language) JSON prompts for Bria's FIBO image generation models. Use this skill when creating detailed image descriptions in JSON format for text-to-image generation, image editing, inpainting, outpainting, background generation, or captioning. Triggers include requests to write structured prompts, create VGL JSON, describe images for AI generation, or work with Bria/FIBO's structured_prompt format. Also use when converting natural language image requests into the deterministic JSON schema required by FIBO models.
Bootstrap agentic development environment from agent.toml manifest
Create and edit videos using Google's Veo 2 and Veo 3 models. Supports Text-to-Video, Image-to-Video, Reference-to-Video, Inpainting, and Video Extension. Available parameters: prompt, image, mask, mode, duration, aspect-ratio. Always confirm parameters with the user or explicitly state defaults before running.
Use when generating visual assets with Bria.ai - product photos, hero images, icons, backgrounds. Includes batch generation (multiple images concurrently), pipeline workflows (generate → edit → remove background), and parallel API patterns. Use for websites, presentations, e-commerce catalogs, or any task needing multiple AI-generated images.
List all Langfuse prompts with their labels and versions. Use when checking available prompts, verifying label assignments, or getting an overview of prompt status.
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms
AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous tasks, sharing knowledge between agents, building knowledge base. Skip when: no learning needed, ephemeral one-off tasks, external data sources available, read-only exploration.
View Langfuse prompts. Use when checking prompt contents, comparing versions, or debugging prompt issues.
Retrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, constructing context from retrieved documents, adding citations, or implementing hybrid search.