perplexity-research
Use when conducting web research with Perplexity API - covers authentication, search strategies, structured output generation, and integration patterns for Claude-interpretable results
Use when conducting web research with Perplexity API - covers authentication, search strategies, structured output generation, and integration patterns for Claude-interpretable results
Generate ROS 2 service server and client code examples for educational content. This skill should be used when creating lessons that teach request/response communication, writing exercises involving services, or generating worked examples for synchronous robot commands.
Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
Transform validated pain points into articulated user value statements for PRD v0.1 Spark. Triggers on completing problem framing, defining user outcomes, articulating value propositions, or requests like "what value do users get", "define outcomes", "articulate the benefit", "finish v0.1", "pain to value", "what do they gain". Outputs CFD- entries tagged as value hypotheses with evidence tiers. Follows Problem Framing skill in workflow.
Run expert review on a plan with parallel reviewer agents. Presents findings as Socratic questions. Use when asked to "review the plan", "get feedback on the design", "check this approach", or before implementation to validate architectural decisions. Optional argument: reviewer name (e.g., `/arc:review daniel-product-engineer` to use a specific reviewer)
Create and execute PRPs (Product Requirements Prompts) for feature implementation using Context Engineering principles. Use when planning new features, initializing PRP setup, executing existing PRPs, or when the user mentions "PRP", "feature planning", "implementation blueprint", or "context engineering". Helps achieve one-pass implementation success.
Generate professional AI-enhanced photos using ByteDance Seedream 4.5 model. Use when users want to, (1) Create portraits with various styles, (2) Generate couple or family group photos, (3) Take photos with movie characters, (4) Edit images (change clothing, background, material, style), (5) Merge multiple photos (outfit fusion, person-scenery fusion, brand design), (6) Create series of related images (seasons, character states, story sequences), (7) Design posters (movie, event, product), or (8) Use custom prompts with full creative control.
Transform any rough prompt, task description, or job into an optimized AI instruction prompt. Use when users ask to "improve this prompt", "make this better for an AI", "optimize this for Claude/GPT", or provide raw instructions that need refinement into a well-structured prompt following best practices.
Design custom voices from text prompts and produce professional narration with voice design previews.
Use when user asks to generate, create, or draw an image, photo, picture, logo, illustration, or art. Triggers include 'сгенерируй', 'нарисуй', 'создай картинку', 'сделай фото', 'фотографию', 'изображение', 'иллюстрацию', 'логотип', 'generate image', 'create image', 'draw', 'make a picture', 'visualize'.
Generate images using Google's Nano Banana Pro (gemini-3-pro-image-preview). Accepts text prompts and optionally images (for editing/transformation) as INPUT. Returns generated IMAGES as OUTPUT. Use when user asks to create, generate, edit, or draw images, infographics, visualizations, diagrams, charts, or illustrations. Excellent for data-accurate infographics and text rendering.
Generate images using AI when user wants to create pictures, draw, paint, or generate artwork. Supports text-to-image and image-to-image generation.
Converts documents into clean, chunked datasets suitable for embeddings and vector search. Produces chunked JSONL files with metadata, deduplication logic, and quality checks. Use when preparing "training data", "vector datasets", "document processing", or "embedding data".
Builds document embedding pipelines with text chunking, embedding generation, indexing, and retrieval optimization. Use when users request "embedding pipeline", "document indexing", "text chunking", "RAG preprocessing", or "semantic indexing".
You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.
Reusable logic for categorizing files as Command, Agent, Skill, or Documentation based on structure and content analysis
Use when getting started with llmemory document storage and search - covers installation, initialization, adding documents, vector search, hybrid search, semantic search, BM25 full-text search, document management, and building RAG systems with multi-tenant support
Cloudflare AI Search for semantic search and vector embeddings in Workers
Use when writing or improving any LLM prompts. Applies TDD methodology and research-backed practices (Meincke 2025): test don't assume, measure baseline, iterate rigorously. Prevents assuming universal techniques work. Triggers: 'write a prompt', 'improve prompt', 'prompt not working', general prompting, application development.
Claude Code config optimization skill. Use when: - Editing CLAUDE.md, rules/, skills/, agents/, commands/ - User asks about config best practices - Checking optimization status - User says "claude code changelog" or "claude code updates" - User asks about new features or breaking changes in Claude Code
Build question/answer style agents using InteractiveNode, explicit routing, and workflow slices.