132 boosters for "lm" — open source, verified from GitHub, ready to install
An AI-powered GitHub issue orchestrator that automatically fetches issues, spawns sub-agents to implement fixes, opens PRs, and monitors review feedback. Ideal for developers wanting to automate bug triage and fix deployment workflows.
This skill helps you build LLM-powered applications with Claude. Choose the right surface based on your needs, detect the project language, then read the relevant language-specific documentation. Scan the target file (or, if no target file, the prompt and project) for non-Anthropic provider markers
mcp-builder is a guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to integrate with external services and APIs. It helps developers build well-designed tool interfaces in Python or TypeScript for AI-powered applications.
Reality Checker is a skeptical integration agent that enforces evidence-based certification and defaults to 'NEEDS WORK' until overwhelming proof of production readiness is provided. Teams shipping to production benefit from its principled gate-keeping against premature deployments.
A specialized AI agent that automatically detects, classifies, and fixes data anomalies in production pipelines using local SLMs and semantic clustering, with zero data loss guarantee. Data engineers and platform teams benefit most when dealing with broken pipelines that can't afford downtime.
Promptfoo is an LLM evaluation and testing toolkit that helps developers systematically test, benchmark, and validate prompt performance across different models and scenarios. It's essential for teams building LLM applications who need rigorous quality assurance and prompt optimization.
Train language models using TRL (Transformer Reinforcement Learning) on fully managed Hugging Face infrastructure. No local GPU setup required—models train on cloud GPUs and results are automatically saved to the Hugging Face Hub. Use this skill when users want to: Use Unsloth () instead of standard
This skill is for running evaluations against models on the Hugging Face Hub on local hardware. It does not cover: If the user wants to run the same eval remotely on Hugging Face Jobs, hand off to the skill and pass it one of the local scripts in this skill.
This skill automates the process of adding, extracting, and managing evaluation results in Hugging Face model cards, supporting multiple data sources including Artificial Analysis API and custom evaluations with vLLM/lighteval. It's valuable for ML practitioners and model maintainers who need to track and display model performance metrics.
Yielding Bear provides a single unified API that routes every LLM request to the cheapest capable model across 16+ providers — saving 60-80% vs calling OpenAI, Anthropic, or Google directly. 1. Get an API key at https://yieldingbear.com/api 2. Set environment variable:
Openmemory JS is a local persistent memory store for LLM applications that enables long-term context retention across Claude Desktop, GitHub Copilot, and other AI platforms. Developers building AI agents and applications benefit from enhanced memory management without external dependencies.
"name": "mcp-markdownify-server", "description": "MCP Markdownify Server - Model Context Protocol Server for Converting Almost Anything to Markdown", "author": "@zcaceres (@zachcaceres | zach.dev)",
"version": "9.18.0", "description": "v9.18.0 — Multi-LLM orchestration for Claude Code with Double Diamond workflows, provider routing, safety gates, and automation. Use '/octo:auto' or just describe what you need. Run /octo:setup.", "repository": "https://github.com/nyldn/claude-octopus",
Connect a Kubernetes cluster (EKS, GKE, AKS, or KOPS) to CAST AI for cost optimization, autoscaling, and security scanning. Covers API key generation, Helm chart installation of the CAST AI agent, and Terraform provider setup. Log in to https://console.cast.ai and navigate to API > API Access Keys.
A skill for adapting and optimizing Hugging Face or custom LLM models to run efficiently on vLLM with Ascend NPU support, enabling developers to validate and deploy models with deterministic testing and single-commit delivery.
A Cursor-integrated coding standards and linting configuration toolkit for NeMo Curator projects, helping teams enforce consistent data processing code quality with Ruff-based rules and exceptions.
Claw Compactor is a 6-layer token compression skill for OpenClaw agents that reduces workspace token spend by 50–97% through deterministic rules and an LLM-driven memory system called Engram. It's designed for developers building token-efficient AI agents who need automatic cost optimization at session start.
Halmos is a symbolic execution tool for EVM smart contracts that integrates with Copilot to help developers write and verify property-based tests in Solidity. It enables security-focused developers to automatically find bugs and verify contract correctness using SMT solvers.
Automates the discovery, fixing, and PR management of GitHub issues by spawning sub-agents to implement solutions and handle code reviews. Developers working on active repositories benefit from reduced manual triage and implementation overhead.
A system prompt that guides LLMs to analyze Factorio game implementations and generate detailed natural language plans for achieving objectives. Useful for developers creating AI-driven game planning systems or educational tools.
An orchestrator booster that automatically fetches GitHub issues, spawns AI sub-agents to implement fixes, opens pull requests, and manages review feedback. Ideal for teams looking to automate bug triage and fix workflows.
A clean, LLM-optimized MCP server that enables Claude to browse Reddit posts, search content, and analyze user data directly. Developers and AI builders benefit from seamless Reddit integration in their Claude-powered applications.
LLM-first SEO analysis skill with 16 specialized sub-skills, 10 specialist agents, and 33 scripts for website, blog, and GitHub repository optimization. For prompt reliability in Codex/agent IDEs, map common user wording to a fixed workflow: When the user requests SEO analysis, follow this routing:
A PHP SDK for building AI agents with structured outputs and multi-agent orchestration, enabling developers to decompose complex tasks into specialized subagents with isolated contexts and independent execution.
