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Found 917 Skills
Review skills in any project using a dual-axis method: (1) deterministic code-based checks (structure, scripts, tests, execution safety) and (2) LLM deep review findings. Use when you need reproducible quality scoring for `skills/*/SKILL.md`, want to gate merges with a score threshold (for example 90+), or need concrete improvement items for low-scoring skills. Works across projects via --project-root.
Use when an existing agent already works without Prefactor and you need to add tracing for runs, llm calls, tool calls, and failures with minimal behavior changes.
Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides JavaScript SDK examples.
Integrates Flowlines observability SDK into Python LLM applications. Use when adding Flowlines telemetry, instrumenting LLM providers, or setting up OpenTelemetry-based LLM monitoring.
创建高质量 MCP（模型上下文协议）服务器的指南，使 LLM 能够通过精心设计的工具与外部服务交互。在构建 MCP 服务器以集成外部 API 或服务时使用，无论是 Python (FastMCP) 还是 Node/TypeScript (MCP SDK)。
Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides Python SDK examples.
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.
Trains an X/Twitter account's algorithmic feed to surface niche-relevant content and positions the account as a thought leader. Browser scripts for manual operation, Persona Engine for identity management, and 24/7 Algorithm Builder with LLM-powered engagement via Puppeteer. Use when a user wants to build their algorithm, cultivate their feed for a niche, grow a fresh account, become a thought leader, or run automated engagement with AI-generated content.
Rewrite AI-sounding text into natural, human writing by removing common LLM patterns while preserving meaning and tone.
Edit prose to sound more natural, direct, and engaging. Works top-down through four levels (Document → Paragraph → Sentence → Word) with human checkpoints at each stage. Fixes LLM patterns, writerly bad habits, and style deficits. Works for academic papers, reports, memos, essays, blog posts, proposals, and other nonfiction. Use when prose sounds robotic, dull, or inaccessible.
Model Context Protocol (MCP) server development and AI/ML integration patterns. Covers MCP server implementation, tool design, resource handling, and LLM integration best practices. Use when developing MCP servers, creating AI tools, integrating with LLMs, or when asking about MCP protocol, prompt engineering, or AI system architecture.
LLM deployment strategies including vLLM, TGI, and cloud inference endpoints.