GitHub
agents-consult
Consult multiple LLM providers via src.agents.scripts.consult to get approach suggestions before implementing changes. Use when you need design options, risk checks, or alternative solutions for a task in this...
Skills IA content media llm ai
Découvrez les skills content media llm ai, comparez leurs dépôts et consultez leurs instructions.
Catégories: content media llm ai
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Explorer
GitHub
pipeline-execution
Guide for orchestrating custom agents through the feature development workflow. Use this when asked to run the pipeline on an issue or execute the agent pipeline.
GitHub
sigil-mcp
Model Context Protocol server for Sigil MPC signing. Use for deploying Sigil as a universal agent-compatible signing server that works with Claude Desktop, VS Code, and any MCP-compliant client.
GitHub
microsoft-agent-framework
Expert knowledge of Microsoft Agent Framework for building production AI agents and workflows. Use when building agents with Microsoft's framework, multi-agent orchestration, or tool integration.
GitHub
work-delegator
Expert delegation specialist that creates comprehensive context packages for coding agents, analyzes requirements, identifies relevant files, and generates clear instructions. Activates when delegating work, as...
GitHub
mcp-manager
Conversational interface for managing MCP (Model Context Protocol) server configurations in Claude Code
GitHub
prompt-creator-10-frameworks
Use when the user asks to create prompts/提示词 for a topic and expects 10 variants using ICIO, CRISPE, BROKE, CREATE, TAG, RTF, ROSES, APE, RACE, TRACE, with strict formatted output (字段清单 + “Prompt：”行 +...
GitHub
ai-content-detection-normalization
Reduce AI-writing fingerprints and normalize content to sound human.
GitHub
autonomous-gap-detector
Protocol for agents to autonomously detect skill gaps and maintain documentation health.
GitHub
context-pack-format
รูปแบบการจัดเตรียม context ให้กระชับ มีโครงสร้าง และใช้ token อย่างมีประสิทธิภาพ
GitHub
retrieval-quality
Comprehensive guide to improving RAG retrieval quality including chunking strategies, embedding optimization, reranking, and hybrid search
GitHub
prompt-versioning
Comprehensive guide to versioning, testing, and managing prompts for AI agents including A/B testing, rollback strategies, and prompt registries
GitHub
ai-auditability
Implementing comprehensive logging, tracking, and audit trails for AI systems to ensure compliance and enable debugging.
GitHub
ai-ethics-and-compliance
Navigating the regulatory landscape and ethical frameworks for responsible AI development and deployment.
GitHub
vector-search-patterns
Implementing semantic search and similarity search using vector embeddings and vector databases.
GitHub
skill-discovery-chaining
Protocols for efficiently identifying, retrieving, and combining relevant skills from the index to solve complex problems without context overload.
GitHub
llm-and-ai-cost-optimization
Strategies for managing and reducing costs in LLM-powered applications, from token economics to RAG architectures.
GitHub
audit-trails-for-agents
Comprehensive guide to implementing audit trails and logging for AI agents including tracing, observability, compliance, and debugging
GitHub
override-mechanisms
Implementing human override capabilities for AI decisions with proper tracking, auditing, and feedback loops.
GitHub
human-in-the-loop-workflows
Implementing human approval and review workflows for AI systems to ensure quality, safety, and compliance.
GitHub
llm-judge-patterns
Comprehensive guide to using LLMs as judges for automated evaluation including prompt patterns, calibration, bias reduction, and multi-judge ensembles
GitHub
ai-risk-assessment
Identifying, assessing, and mitigating risks in AI systems including bias, safety, privacy, security, and ethical concerns.
GitHub
prompt-library-minimal
คลัง prompts แบบ minimal ที่ใช้ token น้อยที่สุดแต่ได้ผลลัพธ์ดี พร้อม templates และ best practices
GitHub
ai-model-registry
Centralized management of machine learning models throughout their lifecycle, including versioning, metadata, and production deployment.
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.