Strategic BDI reasoning engine for autonomous AI agents — goal decomposition, utility scoring, risk evaluation, and reactive replanning over PuterVision memory and world models.

Multi-agent department framework for long-form complex tasks, fighting AI hallucination, validated on academic research. 共识管线：多智能体部门长线任务解决框架，对抗AI幻觉，以学术研究为验证场景。

One command. Full stack. Zero compromise. — All-in-one Claude Code skill with 33 modes, 6-layer security, 23 hooks, and 75% token savings. Works on Codex, Cursor, Manus, Windsurf.

A self-hosted MCP memory server that gives every MCP-compatible agent a shared, persistent memory layer, so preferences, project conventions, and past decisions carry across sessions and across different agents and vendors. It combines hybrid keyword-plus-vector retrieval with automatic context injection, zero-config sync, and selective injection of memories by agent and priority.

Dev-mode pulse for SolidJS apps: flash what really updates (fine-grained computations, component mounts/remounts, DOM mutations, DOM detach/reattach with focus and scroll loss, fetch/WebSocket/SSE lifecycle, Solid Query observers) with an agent-controllab

Deterministic Unit-Record Memory Engine for AI coding agents. Eliminates context-window amnesia and compaction bloat via zero-turn workspace hydration, byte-exact registers, and live context telemetry.

From web terminals for your coding agents to a self-running agent company: Claude Code and Codex in parallel git worktrees, a job board they pick work from, and one agent that runs the board from a goal.

Enables AI agents and assistants to inspect live system telemetry, check TCP port connectivity, audit environment variables for leaked secrets, analyze and summarize log files, and list Docker containers — all with token-efficient, compressed output.

Enables AI coding agents to retrieve compact structural codebase context through repository architecture summaries, AST symbol extraction, cyclomatic complexity scoring, and TODO/FIXME hotspot search.

Enables AI agents and LLMs to persist facts, preferences, and observations in a local SQLite store and to build and traverse subject-predicate-object knowledge graphs across sessions. It provides fuzzy keyword search and graph queries so assistants retain context and recall prior project history without repeating prompts.

cc-code — Terminal coding agent powered by open-source models — Rust-built, Claude Code compatible

Enables AI agents to drive a real Chromium browser by receiving compact, token-efficient page maps and only the changes after each action. It guards against hidden prompt injections, blocks payments, sends and deletions until the user approves, and lets agents learn site-specific notes over time.

An intelligent Model Context Protocol (MCP) Server that acts as an AI model advisor. Compare LLM and media pricing, features, and quality across 1,000+ models from OpenRouter, fal.ai, Together AI, Replicate, and Fireworks AI. Deeply compatible with Claude

Slides AI Plugin for Claude, OpenClaw, etc that turns any idea into a full presentation — animated HTML or editable PowerPoint — from a single prompt

Enables MCP clients to query and diagnose machine-learning training runs through read-only tools for listing experiments, reading configurations and final metrics, comparing runs, sampling metric curves, and detecting training failures.

Enables AI agents to write, compose, and check assume-guarantee contracts, including refinement, consistency, compatibility, realizability, and LTL checks, and to use returned counterexamples to fix specifications.

A zero-dependency MCP suite exposing computational tools across metabolic, cardio-renal, chronobiology, neuro-recovery, and biomechanics domains. It enables clients to derive longevity and physiology metrics such as autophagy index, glucose-ketone ratio, glycemic variability, peripheral resistance, nocturnal BP dipping, DLMO clock time, melanopic lux, caffeine clearance, EEG fatigue entropy, and grip vitality age from user-supplied measurements.

Exposes MCP tools that compute longevity and metabolic metrics (autophagy index, GKI, MAGE), cardio-renal biomarkers (BUN/creatinine ratio, cardiac output and TPR, nocturnal BP dipping), chronobiology estimates (DLMO, melanopic lux, caffeine clearance), plus neuro-recovery and biomechanics scores. Runs as a zero-dependency Python JSON-RPC 2.0 stdio server so any MCP-compatible client can query these physiological domain calculators.

Gives AI agents read-only access to curated, deduplicated, and well-described data products, complete with source metadata, freshness tracking, and per-agent permissions that preserve the source's access rights. The server can also answer "I don't know" rather than guessing when the approved data doesn't cover a question.

Enables AI agents to remember people, projects, places, and topics across conversations under a strict token budget, returning compact handles with expandable details while preserving changes, provenance, and encrypted self-hosted storage.

Enables agents to run SweetScape 010 Editor Binary Templates and Scripts headlessly against local files. It provides tools to locate the editor binary, execute templates or scripts, and use controlled exit behavior for compile and runtime failures.

Enables an LLM client to perform reverse engineering inside IDA Pro without the GUI, opening databases headlessly in persistent worker processes and exposing tools to decompile and disassemble functions, rename symbols, retype variables and arguments, add comments, and convert numeric bases. It can also run over stdio or HTTP transports and adopt an existing GUI session when desired.

Causal memory layer for AI agents — MCP server that records decision→outcome relationships. Survives compaction.

Enables AI agents to list and compare LLM API prices per million tokens, rank the cheapest models by context and keyword, look up GPU rental rates per hour by provider, and estimate self-hosting cost per million tokens against API pricing. Pricing data comes from OpenRouter's public models endpoint plus a dated GPU price CSV, with source URLs and explicit notes when data is missing.