GitHub
mcp-manager
Conversational interface for managing MCP (Model Context Protocol) server configurations in Claude Code
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documentation llm ai AI 技能
发现 documentation llm ai 相关 Skills，比较来源仓库和用途，查看适合你工作流的技能说明。
分类目录: documentation llm ai
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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 +...
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GitHub
ai-content-detection-normalization
Reduce AI-writing fingerprints and normalize content to sound human.
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GitHub
autonomous-gap-detector
Protocol for agents to autonomously detect skill gaps and maintain documentation health.
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GitHub
context-pack-format
รูปแบบการจัดเตรียม context ให้กระชับ มีโครงสร้าง และใช้ token อย่างมีประสิทธิภาพ
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GitHub
retrieval-quality
Comprehensive guide to improving RAG retrieval quality including chunking strategies, embedding optimization, reranking, and hybrid search
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GitHub
prompt-versioning
Comprehensive guide to versioning, testing, and managing prompts for AI agents including A/B testing, rollback strategies, and prompt registries
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GitHub
ai-auditability
Implementing comprehensive logging, tracking, and audit trails for AI systems to ensure compliance and enable debugging.
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GitHub
ai-ethics-and-compliance
Navigating the regulatory landscape and ethical frameworks for responsible AI development and deployment.
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GitHub
vector-search-patterns
Implementing semantic search and similarity search using vector embeddings and vector databases.
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GitHub
skill-discovery-chaining
Protocols for efficiently identifying, retrieving, and combining relevant skills from the index to solve complex problems without context overload.
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GitHub
llm-and-ai-cost-optimization
Strategies for managing and reducing costs in LLM-powered applications, from token economics to RAG architectures.
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GitHub
audit-trails-for-agents
Comprehensive guide to implementing audit trails and logging for AI agents including tracing, observability, compliance, and debugging
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GitHub
override-mechanisms
Implementing human override capabilities for AI decisions with proper tracking, auditing, and feedback loops.
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GitHub
human-in-the-loop-workflows
Implementing human approval and review workflows for AI systems to ensure quality, safety, and compliance.
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GitHub
llm-judge-patterns
Comprehensive guide to using LLMs as judges for automated evaluation including prompt patterns, calibration, bias reduction, and multi-judge ensembles
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GitHub
ai-risk-assessment
Identifying, assessing, and mitigating risks in AI systems including bias, safety, privacy, security, and ethical concerns.
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GitHub
prompt-library-minimal
คลัง prompts แบบ minimal ที่ใช้ token น้อยที่สุดแต่ได้ผลลัพธ์ดี พร้อม templates และ best practices
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GitHub
ai-model-registry
Centralized management of machine learning models throughout their lifecycle, including versioning, metadata, and production deployment.
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GitHub
llm-token-optimization
See the main LLM Cost Optimization skill for comprehensive coverage of token economics and optimization strategies.
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GitHub
communication-templates
Pre-written message templates for incident communication across different channels and severity levels.
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GitHub
llm-security-red-teaming
Comprehensive guide to securing LLM applications including prompt injection prevention, jailbreak detection, guardrails, and red teaming methodologies
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GitHub
tool-permission-model
Comprehensive guide to implementing permission systems for AI agent tools including RBAC, approval workflows, and security policies
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GitHub
llm-function-calling
Implementing function calling (tool use) with LLMs for structured outputs and external integrations.
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