mcp-manager
Conversational interface for managing MCP (Model Context Protocol) server configurations in Claude Code
Discover documentation llm ai skills, compare source repositories and read the instructions for each workflow.
Conversational interface for managing MCP (Model Context Protocol) server configurations in Claude Code
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 +...
Reduce AI-writing fingerprints and normalize content to sound human.
Protocol for agents to autonomously detect skill gaps and maintain documentation health.
รูปแบบการจัดเตรียม context ให้กระชับ มีโครงสร้าง และใช้ token อย่างมีประสิทธิภาพ
Comprehensive guide to improving RAG retrieval quality including chunking strategies, embedding optimization, reranking, and hybrid search
Comprehensive guide to versioning, testing, and managing prompts for AI agents including A/B testing, rollback strategies, and prompt registries
Implementing comprehensive logging, tracking, and audit trails for AI systems to ensure compliance and enable debugging.
Navigating the regulatory landscape and ethical frameworks for responsible AI development and deployment.
Implementing semantic search and similarity search using vector embeddings and vector databases.
Protocols for efficiently identifying, retrieving, and combining relevant skills from the index to solve complex problems without context overload.
Strategies for managing and reducing costs in LLM-powered applications, from token economics to RAG architectures.
Comprehensive guide to implementing audit trails and logging for AI agents including tracing, observability, compliance, and debugging
Implementing human override capabilities for AI decisions with proper tracking, auditing, and feedback loops.
Implementing human approval and review workflows for AI systems to ensure quality, safety, and compliance.
Comprehensive guide to using LLMs as judges for automated evaluation including prompt patterns, calibration, bias reduction, and multi-judge ensembles
Identifying, assessing, and mitigating risks in AI systems including bias, safety, privacy, security, and ethical concerns.
คลัง prompts แบบ minimal ที่ใช้ token น้อยที่สุดแต่ได้ผลลัพธ์ดี พร้อม templates และ best practices
Centralized management of machine learning models throughout their lifecycle, including versioning, metadata, and production deployment.
See the main LLM Cost Optimization skill for comprehensive coverage of token economics and optimization strategies.
Pre-written message templates for incident communication across different channels and severity levels.
Comprehensive guide to securing LLM applications including prompt injection prevention, jailbreak detection, guardrails, and red teaming methodologies
Comprehensive guide to implementing permission systems for AI agent tools including RBAC, approval workflows, and security policies
Implementing function calling (tool use) with LLMs for structured outputs and external integrations.
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