Hindsight: Agent Memory That Learns
Universal SEO skill for Claude Code. 26 sub-skills + 19 sub-agents covering technical SEO, E-E-A-T, schema, GEO/AEO, agent readiness (Lighthouse Agentic Browsing, WebMCP, llms.txt), backlinks, local SEO, e-commerce, international SEO, Google APIs, and PDF/Excel reporting. 9 optional extensions for live SEO data.
FreeToken brings datacenter-scale model serving to your desktop. Run massive models locally, fast and efficiently.
Python framework for recursive, task-level and harness-level optimization of agent harnesses around fixed LLM weights, tested on professional benchmarks.
DeepInfer: High-performance, large-scale inference framework for DeepSeek models
High-performance AI pipeline engine with a C++ core and 50+ Python-extensible nodes. Build, debug, and scale LLM workflows with 13+ model providers, 8+ vector databases, and agent orchestration, all from your IDE. Includes VS Code extension, TypeScript/Python SDKs, and Docker deployment.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
A skill to stop your coding agent from burying the answer. ADHD-friendly output.
极致轻量|阻止论文的防御性写作 | Anti-defensive writing for academic papers — 学术写作原则 Skill + prompt pack (CN/EN)
Zero-modification Human-in-the-Loop adapter for OpenOPC's agentic DAG runtime.
Open-source Python library for AI browser agents, plus Browser Use Cloud: stealth browsers and a hosted agent API.
Reverse engineer any website into a typed API client. Works with Claude Code, Codex, Cursor and OpenCode.
A high-throughput and memory-efficient inference and serving engine for LLMs
The fastest, litest AI Gateway. Rust core with Python SDK. Call 100+ LLM APIs in OpenAI (or native) format with cost tracking, guardrails, load balancing, and logging [Bedrock, Azure, OpenAI, Anthropic, OpenAI, VertexAI, vLLM, Nvidia NIM]
HarnessRouter Community Edition: the self-hosted, Apache-2.0 edition of the unified interface for agent harnesses. Run Codex, Claude Code, Hermes, PI, DSH, and more through one API, with sessions, streaming, files, cancellation, and failure handling. Implements the Unified Harness Protocol (UHP), an open standard. Your keys, your infrastructure.
ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent.
Non-autoregressive System 1 decision engine. Typed choice, score and yes/no decisions over any text in a single forward pass, in 100+ languages, with a router that picks the right checkpoint per request.
Official Crawl4AI Cloud: a hosted web scraping, extraction, and search API for AI agents. Free API key, 1,000 credits a month, no card. Fully open source: this repo is the Apache-2.0 crawler that powers it, and you can self-host it.
Run 35B MoE models in ~2.5 GB of RAM. Weights stream from SSD; a trained router predicts loads a step ahead. Mac, iPhone, Android, Windows supported
Breakthrough Method for Agile Ai Driven Development
🔍大模型应用开发实战一:RAG 技术全栈指南,在线阅读地址:https://datawhalechina.github.io/all-in-rag/
A lightweight Python SDK and CLI for evaluating recursive self-improvement (RSI) methods across agent benchmarks.
📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
A straightforward method for training your LLM, from downloading data to generating text.
"RAG-Anything: All-in-One RAG Framework"
Persistent memory for Claude Code, Codex and 6 other AI agents, stored as plain markdown in your Obsidian vault. New: Crew, one shared logbook so Claude Code, Codex, Cursor and Grok Bot know what each other did. 48 commands: semantic search, self-rewriting notes, key-less research, scheduled agents.
When Claude keeps giving you bad answers, make 100 versions of it fight to the death. Same task, 100 different strategies, a bracket, one answer left. Free Claude Code skill.
Skill training with a corruption and reconstruction loop: train an agent's skill library, not its weights.
SGLang is a high-performance serving framework for large language models and multimodal models.
[EMNLP2025] LightRAG: Simple and Fast Retrieval-Augmented Generation
Serve large Qwen models fast on the GPUs you actually own. Qwen3.8-27B on a single 24 GB card with vLLM: 127 tok/s single-user (381 when the answer quotes the prompt), ~1,035 tok/s at 64 concurrent, 150k-262k context. vLLM patches, requant pipeline, benchmarks.
斑海豹 — Phocinae-Largha-150M-v1: a 144.3M bilingual decision model. Tiny model. Big decisions. 小海豹,大决断。
Infrastructure for the next generation of voice agents, designed to provide universal memory. It is divided into a left brain and a right brain, storing information and emotions respectively, while a fully streaming architecture eliminates latency at the fundamental level.
Official Inference Code of EAGLE-4
Find the ghost tokens. Fix them. Survive compaction. Avoid context quality decay.
[SIGMOD'27] Easy Data Preparation with latest LLMs-based Operators and Pipelines.
Convert PDF to markdown + JSON quickly with high accuracy
A framework for continual learning and recursive self-improvement through harness adapter evolution.
零基础入门大模型(LLM)的基础教程:从零手写 GPT-2,一行行看懂大模型是怎么工作的 | Build GPT-2 from scratch in PyTorch, step by step
TokenSpeed is a speed-of-light LLM inference engine.
Open decision models from 0.8B to 27B: a calibrated probability for every option in one pass, and a short thought only when unsure. Engine for vLLM, llama.cpp and MLX.
Offline voice-agent application layer: session core with byte-deterministic replay, spoken-LLM adapters (scripted/replay/loopback-mock), injection-safe function-call grammar, slot grounding, workflow engine with compensation and resume, permissioned tool sandbox, repair-dialog policies, task-success and latency evaluation. NumPy core, torch extra.
Our lightning-fast inference engine.
On-device Vision Model - a fully offline, free vision pipeline that gives text-only models (e.g. DeepSeek) image understanding and multilingual OCR through an OpenAI-compatible local API. Only tokens are needed: no API key, no subscription, no cloud cost. Recommended for native speakers of Chinese, English, Japanese and Korean.
Refusal removal as a loadable KV-cache graft — zero weight modification, no refusal-direction projection, fully reversible.