agent-creator
Creates specialized AI agents with optimized system prompts using the official 4-phase SOP methodology from Desktop .claude-flow, combined with evidence-based prompting techniques and Claude Agent SDK implement...
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Creates specialized AI agents with optimized system prompts using the official 4-phase SOP methodology from Desktop .claude-flow, combined with evidence-based prompting techniques and Claude Agent SDK implement...
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms
Comprehensive PR review with multi-agent swarm specialization for security, performance, style, tests, and documentation
Specialized ML model development, training, and deployment workflow
Research topics or review code using OpenAI Codex CLI. Use for: technical research, documentation lookup, code analysis, or when external AI perspective is needed.
Advanced intent interpretation system using cognitive science principles and probabilistic intent mapping
Use when starting any conversation - establishes mandatory workflows for finding and using skills, including using Read tool before announcing usage, following brainstorming before coding, and creating TodoWrit...
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)....
Summit/IntelGraph context-engineering skill pack. Routes to the vendored Agent Skills for Context Engineering library via progressive disclosure, and applies repo-specific guardrails (atomic PRs, evidence, gove...
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.