TokenOracle

Hosted MCP server for LLM cost estimation, model comparison, and budget-aware routing.

agent-repo-init

One-click initialization of a multi-agent repository from the Antigravity template. Use this skill when users want to scaffold a new project quickly (`quick` mode) or with runtime defaults (`full` mode) including LLM provider profile, MCP toggle, swarm preference context, sandbox type, and optional git init.

yapi — LLM Skill Guide

yapi is a CLI-first, git-friendly API client. You define requests in YAML files and run them from the terminal. No GUI, no accounts, no state — just files and a binary.

RAGScore

Generate QA datasets & evaluate RAG systems. Privacy-first, any LLM, local or cloud.

Io.Github.Ankitpal181/Toon Parse Mcp

MCP server that reduces LLM context by removing code comments and converting data formats to TOON

developing-with-prism

Guide for developing with Prism PHP package - a Laravel package for integrating LLMs. Activate or use when working with Prism features including text generation, structured output, embeddings, image generation, audio processing, streaming, tools/function calling, or any LLM provider integration (OpenAI, Anthropic, Gemini, Mistral, Groq, XAI, DeepSeek, OpenRouter, Ollama, VoyageAI, ElevenLabs). Activate for any Prism-related development tasks.

Mcp Pihole

Pi-hole v6 MCP server - manage DNS blocking, stats, whitelists/blacklists

llm-docs-optimizer

Optimize documentation for AI coding assistants and LLMs. Improves docs for Claude, Copilot, and other AI tools through c7score optimization, llms.txt generation, question-driven restructuring, and automated quality scoring. Use when asked to improve, optimize, or enhance documentation for AI assistants, LLMs, c7score, Context7, or when creating llms.txt files. Also use for documentation quality analysis, README optimization, or ensuring docs follow best practices for LLM retrieval systems.

Doctree Mcp

BM25 search + tree navigation over markdown docs for AI agents. No embeddings, no LLM calls.

pydantic-evals

Test and evaluate AI agents and LLM outputs using code-first evaluation framework with strong typing. Use when the user wants to: (1) Create evaluation datasets with test cases for AI agents, (2) Define evaluators (deterministic, LLM-as-Judge, custom, or span-based), (3) Run evaluations and generate reports, (4) Compare model performance across experiments, (5) Integrate evaluations with Pydantic AI agents, (6) Set up observability with Logfire, (7) Generate test datasets using LLMs, (8) Implement regression testing for AI systems.

karpathy-llm-wiki

Use when building or maintaining a personal LLM-powered knowledge base. Triggers: ingesting sources into a wiki, querying wiki knowledge, linting wiki quality, 'add to wiki', 'what do I know about', or any mention of 'LLM wiki' or 'Karpathy wiki'.

Mcp Server

AI agent tools: web search, browser, 400+ LLMs, image gen, TTS, phone verify. Pay-per-use.

anti-slop-guide

Use when drafting, editing, or reviewing any prose to detect and remove AI writing patterns including overused vocabulary (delve, tapestry, landscape), formulaic structures (binary contrasts, rule of three), throat-clearing openers, business jargon, and other LLM tells

ai-llm-security-review

Use for AI/LLM security assessments, prompt injection, RAG security, agent/tool permissioning, model supply chain, LLM red teaming, AI governance, eval design, data leakage, jailbreak testing, and secure AI application review.

Io.Github.MetriLLM/Metrillm

Benchmark local LLM models — speed, quality & hardware fitness verdict from any MCP client

phoenix-cli

Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, review experiments, inspect datasets, and query the GraphQL API. Use when debugging AI/LLM applications, analyzing trace data, working with Phoenix observability, or investigating LLM performance issues.

Social Search Mcp

Deep social media search for LLMs: Facebook, Reddit, LinkedIn, Instagram & more.

Io.Github.Selvage Lab/Selvage

An LLM-based code review MCP server with AST-powered smart context extraction

Tuning Engines

Domain-specific LLM fine-tuning — sovereign models trained on your data, zero infrastructure.

Video Summarizer

Transcribe and summarize videos from YouTube, local files, Google Drive, and Dropbox using any OpenAI-compatible LLM provider via the CLI.

/ai-llm-safety — AI/LLM Safety Design Enforcement

Every system that involves LLM agents, tool use, or prompt construction MUST treat AI safety as a first-class constraint. Prompt injection is the SQL injection of the AI era — and it's harder to fix after deployment.

Kaito Query Service

AI LLM with Gemini, MiniMax, Replicate, OpenRouter. Vision, search, code review. USDC on Base.

agent-tool-contracts

Design agent tools and CLI surfaces—schemas, naming, errors, idempotency, and discoverability for LLM callers. Use when defining tools for agents, SDKs, or AI-native CLIs.

AI / LLM Tools

rlm

Run a Recursive Language Model-style loop for long-context tasks. Uses a persistent local Python REPL and an rlm-subcall subagent as the sub-LLM (llm_query).

semantic-compression

Aggressively remove grammatical scaffolding LLMs reconstruct while preserving meaning-carrying content. Output may be fragments. Use when compressing text for prompts, reducing token count, preparing context for LLM input, or making documentation more token-efficient. Applies LLM-aware compression rules that delete predictable grammar while preserving semantics.

design-taste-frontend

Senior UI/UX Engineer. Architect digital interfaces overriding default LLM biases. Enforces metric-based rules, strict component architecture, CSS hardware acceleration, and balanced design engineering.

graflow-workflow

Create Python workflow pipelines using Graflow with a structured plan-implement-review process. Use when building task graphs, parallel pipelines, LLM workflows, or any Graflow-based automation. Triggers on requests for "workflow", "pipeline", "task graph", "Graflow", or when user wants to build an automated data/AI pipeline.

Io.Github.Spences10/Mcp Turso Cloud

MCP server for integrating Turso with LLMs

Io.Github.Alex Feel/Mcp Context Server

An MCP server that provides persistent multimodal context storage for LLM agents.

Evaluation framework for LLM knowledge inputs — prompts, RAG corpora, skills, agent workflows. Fix the model, vary the artifact. Built-in statistical rigor: bootstrap CI, Krippendorff α, length-debias, saturation curves.

Decompose

Decompose text into classified semantic units. Authority, risk, attention. No LLM.

LLM Optimizer

AI brand visibility analytics: visibility scores, optimizations, video, Reddit, and search rankings.

Io.Github.Ricky610329/Mup

MCP server that turns HTML MUP panels into interactive UI tools for LLMs.

magpie

Performs GPU kernel correctness and performance evaluation and LLM inference benchmarking with Magpie. Analyzes single or multiple kernels (HIP/CUDA/PyTorch), compares kernel implementations, runs vLLM/SGLang benchmarks with profiling and TraceLens, and runs gap analysis on torch traces. Creates kernel config YAMLs, discovers kernels in a project, and queries GPU specs. Use when the user mentions Magpie, kernel analyze or compare, HIP/CUDA kernel evaluation, vLLM/SGLang benchmark, gap analysis, TraceLens, creating kernel configs, or discovering GPU kernels.

agent-project-development

This skill should be used when the user asks to "start an LLM project", "design batch pipeline", "evaluate task-model fit", "structure agent project", or mentions pipeline architecture, agent-assisted development, cost estimation, or choosing between LLM and traditional approaches. NOT for evaluating agent quality or building evaluation rubrics (use agent-evaluation), NOT for multi-agent coordination or agent handoffs (use multi-agent-patterns).

Io.Github.Kc23go/Anybrowse

Converts any URL to clean, LLM-ready Markdown using real Chrome browsers

autocontext

Iterative strategy generation and evaluation system. Use when the user wants to evaluate agent output quality, run improvement loops, queue tasks for background evaluation, check run status, or discover available scenarios. Provides LLM-based judging with rubric-driven scoring.

Mindpm

Persistent project memory for LLMs via SQLite. Never re-explain your project again.

DomainKits

Domain intelligence platform that turns your LLM into a professional domain consultant.

gdsfactory Component Designer Skill

This skill lets an LLM agent **generate**, **visualize**, and **iteratively modify** photonic-IC components using the [gdsfactory](https://github.com/gdsfactory/gdsfactory) Python library.

Simpliq Server

Simpliq Data Proxy MCP Server - Connects LLMs to SQL Databases via Semantic Mapping

synalinks

Build neuro-symbolic LLM applications with Synalinks framework. Use when working with DataModel, Program, Generator, Module, training LLM pipelines, in-context learning, structured output, JSON operators, Branch/Decision control flow, FunctionCallingAgent, RAG/KAG, or Keras-like LLM workflows.

Stockfish MCP

A Stockfish MCP server to allow an LLM to play chess against Stockfish

SecurityScan

Scan GitHub-hosted AI skills for vulnerabilities: prompt injection, malware, OWASP LLM Top 10.

llm-resources

Garden.Stanislav.Svelte Llm/Svelte Llm Mcp

An MCP server that provides access to Svelte 5 and SvelteKit documentation

Io.Github.PyJudge/Pdf4vllm

PDF reader for vision LLMs. Auto-detects text corruption and switches to image mode.

trulens-evaluation-setup

Configure feedback functions and selectors for TruLens evaluations

sidecar

Spawn conversations with other LLMs (Gemini, GPT, ChatGPT, Codex, o3, DeepSeek, Qwen, Grok, Mistral, etc.) and fold results back into your context. TRIGGER when: user asks to talk to, chat with, use, call, or spawn another LLM or model; user mentions Gemini, GPT, ChatGPT, Codex, o3, DeepSeek, Claude (as a sidecar target), Qwen, Grok, Mistral, or any non-current model by name; user asks to get a second opinion from another model; user wants parallel exploration with a different model; user says "sidecar", "fork", or "fold". CRITICAL RULES: (1) ALWAYS launch sidecar CLI commands with Bash tool's run_in_background: true. Never run sidecar start/resume/continue in the foreground. (2) The fold summary returns on stdout when the user clicks Fold in the GUI or the headless agent finishes. Use TaskOutput to read it when the background task completes. (3) Use --prompt for the start command (NOT --briefing). --briefing is only for subagent spawn. (4) NEVER use o3 or o3-pro unless the user explicitly asks for it by name. These models are extremely expensive ($10-60+ per request). If the user asks for o3, warn them about the cost before proceeding. Default to gemini for most tasks. (5) When the user asks to query MULTIPLE LLMs simultaneously (e.g., "ask Gemini AND ChatGPT", "compare Gemini vs GPT"), ALWAYS use --no-ui (headless) for all of them unless the user explicitly requests interactive. Opening multiple Electron windows at once is disruptive. Launch them all in parallel with run_in_background: true.