when-optimizing-prompts-use-prompt-architect
Comprehensive framework for analyzing, creating, and refining prompts for AI systems using evidence-based techniques
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Comprehensive framework for analyzing, creating, and refining prompts for AI systems using evidence-based techniques
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement
Generate images using Google's Gemini API. Use this skill when the user wants to generate, create, or edit images with AI. Keywords: image generation, create image, generate picture, AI art, edit image, Gemini,...
Search the hierarchical memory system for relevant memories. Searches across all tiers (working, episodic, semantic) and returns ranked results. Use for context retrieval and pattern matching. Keywords: memory,...
Use when working with my-llm-sdk
Fast pre-filter to remove articles already processed. Uses Redis for O(1) URL lookup to avoid wasting LLM calls on previously seen content.
Use LLM to analyze a raw article. Generates summary, extracts entities, categorizes content, assigns importance score, and detects breaking news.
Compose a formatted news digest from processed articles and trends. Uses LLM to generate cohesive narrative summary with embedded citations. Formats for Discord readability.
Compresses large text into minimal tokens while retaining key facts, entities, themes, and events. Used for reducing context window usage.
This skill should be used when the user asks to "understand customers", "customer research", "user personas", "customer needs analysis", "buyer journey mapping", "voice of customer", "customer segmentation", "u...
Modern R development practices emphasizing tidyverse patterns (dplyr 1.1 and later, native pipe, join_by, .by grouping), rlang metaprogramming, performance optimization, and package development. Use when Claude...
This skill should be used when the user asks to "assess technology", "technology evaluation", "tech stack analysis", "technical feasibility", "technology trends", "build vs buy", "technology roadmap", "architec...
Build LiveKit Agent backends in Python. Use this skill when creating voice AI agents, voice assistants, or any realtime AI application using LiveKit's Python Agents SDK (livekit-agents). Covers AgentSession, Ag...
This skill should be used when the user asks to "model trends with limited data", "three-valued logic analysis", "scenario generation", "transitional graphs", "qualitative trend analysis", "uncertain data analy...
HOUSTON executes feature tasks directly. No agents. Best for small features (1-3 tasks).
Build LiveKit Agent backends in TypeScript or JavaScript. Use this skill when creating voice AI agents, voice assistants, or any realtime AI application using LiveKit's Node.js Agents SDK (@livekit/agents-js)....
Expert SQL query writing, optimization, and database schema design with support for PostgreSQL, MySQL, SQLite, and SQL Server. Use when working with databases for: (1) Writing complex SQL queries with joins, su...
Professional data visualization creation using D3.js with support for interactive charts, custom visualizations, animations, and responsive design. Use for: (1) Creating custom interactive charts, (2) Building...
Process and transform arrays of data with common operations like filtering, mapping, and aggregation
Chain agent outputs as inputs in sequential or parallel pipelines for data flow orchestration
Add and manage evaluation results in Hugging Face model repositories using the new .eval_results/ format. Supports extracting scores from model cards.
Debug ML training issues and optimize performance including loss divergence, overfitting, and slow convergence
This skill should be used when the user asks to "calculate market size", "TAM SAM SOM analysis", "estimate market opportunity", "market sizing", "total addressable market", "serviceable market", "market potenti...
This skill should be used when the user asks to "identify trends", "analyze market trends", "trend forecasting", "macro trends", "micro trends", "emerging patterns", "future projections", "industry trends", or...
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库,同一仓库的多个技能可能共享相同数值;它反映仓库关注度,不代表单个技能的安装量或质量评分。