FOREST is a research agent for long-horizon science, where evidence gets lost, errors compound, and goals drift. It explores ideas, reads literature, forms hypotheses, runs real experiments, analyzes results, revises paths, and builds a full paper—all in an editable graph you can inspect, branch, redirect, with claims and figures linked to evidence
Perceptive behavioral foundation models for terrain-adaptive humanoid control: motion tracking, goal reaching, reward inference and teleoperation.
Turn long-horizon goals into verifiable results. A local-first control plane and research workbench for agents and teams, across sessions and runtimes.
Graph-Orchestrated Agent Loop — a production-grade framework on LangGraph. Combine workflow graphs and agent loops, transpile Dify DSL to runnable code, swap wire protocols (Dify/OpenAI).
A goal-driven AI book production house for Amazon KDP: research niches, create books, publish, manage ads and deliver client services.
A proxy tool to bypass GFW.
CloudGoat is Rhino Security Labs' "Vulnerable by Design" AWS deployment tool
local-first semantic code search engine
Python library to handle Gene Ontology (GO) terms
A benchmark for offline goal-conditioned RL and offline RL
Evolutionary multi-agent runtime that breeds, evaluates, and improves autonomous agents across reproducible epochs to converge on optimization of a goal.
INTACT: Isomorphic Intent-to-Action Learning for Search-Free World Models.
A canvas for red team infrastructure and cyber ranges. Compose a topology, export runnable Terraform and Ansible, and deploy it yourself. Your cloud credentials never leave your machine.
Turn vague product or feature ideas into clear, checkable requirements before planning or coding—mapping rough descriptions to professional terms, exposing ambiguities, and aligning goals, flows, and boundaries. 这是一个需求澄清 Skill:把说不清的想法整理成一份清晰、可核对的需求清单,在执行任务前对齐目标、流程与边界等。
Three lightweight Codex workflow skills for goal clarity, evidence-based review, and safe public research.
Generate and review MiniMax H3 video prompts from user goals and multimodal references.
—
Human-in-the-loop planning preset for DeepSeek Harness (DSH), adapted from prime-plans: researched Markdown plans in ./dsh-plans, reviewer/criticizer subagent refinement, per-workspace language & model settings, goal-driven execution with ponytail + MVP tests.
CLI for your personal muse.ai AI agent: chat, side chats, feed, goals, ideas, and sessions from the terminal. No browser needed.
pec-goal-development 是一个面向 Codex编码智能体的 Skill,作为复杂开发任务的默认编排层。它解决的是开发者最头疼的三件事:AI 答应得好好的却改出一堆 bug、需求越聊越偏、做到一半不清楚进度。用户无需主动输入 SPEC、Goal 等术语——只要像平时一样描述需求,Skill 会自动判断复杂度并进入相应执行路径,在写代码前先与你确认授权。
Crow — a local AI agent, not a chat box: 30 tools, MCP, memory, skills, goals, subagents, vision, and image generation and editing with Qwen-Image. Runs on crow-nest or llama.cpp on one GPU, or any model via OpenRouter. Windows and Linux.
A local Markdown knowledge-base template for shared use with Codex and WorkBuddy.
An open-source agent runtime inspired by OpenAI’s Dots: persistent AI agents that pursue goals, react to events, and keep work moving 24×7.
Your coding agent says "done". Orvant asks for proof. A skill for Claude Code, Codex and other file-capable agents, plus a Python engine that clarifies the goal, prepares an independent acceptance oracle and accepts work only through an independent gate. Engine workers currently run on Codex CLI. Open beta.
WorldReward & WorldReward-Bench
DYB Pro hands the protein design pipeline to Devin: state a goal, add sequences, and an autonomous in-silico cycle returns a ranked, order-ready wet-lab shortlist at a fraction of the cost. Devin orchestrates specialist agents whose results commit to a version graph the next cycle reads.
WorldGuide: Goal-Directed Video World Model for Procedural Task Execution.
The authentication glue you need.
Bring any model to Codex, assign them any role, use them in /goal or any workflow.
A local-first AI desktop planning assistant that turns goals into executable time.