Gives the pi terminal AI agent an autonomous experiment loop: propose code changes, run benchmarks, record metrics, auto-commit improvements and revert regressions. Ships a live widget/dashboard, MAD-based confidence scoring, hooks and backpressure checks — made for iterating on speed, bundle size, training loss and build times inside a terminal workflow.
Turns any topic or document into an interactive, multi-agent classroom that generates slides, quizzes, interactive simulations and project-based learning activities. Includes real-time AI teachers/classmates, whiteboard drawing, TTS/ASR, PPTX/HTML export and chat-app integration via OpenClaw.
Multi-agent workbench for long- and short-form fiction, scriptwriting, interactive narrative games and multilingual translation. Offers Studio/CLI/TUI tools that plan, draft, audit and revise chapters while maintaining structured story state, branching forecasts, cover generation and multi-model routing. Suited for authors and teams automating long-horizon narrative workflows.
Framework for running agents inside real applications — it exposes shared actions, SQL-backed state, tools, skills, jobs and UI surfaces so agents can act on app state instead of just chatting. Backend-agnostic TypeScript stack with cloneable app templates and visual planning/recap features.
Reverse-engineers live websites into production-ready Next.js codebases: an AI-driven /clone-website skill extracts design tokens, assets, and exact component specs, then dispatches parallel builder agents to reconstruct pages. Recommends Claude Code (Opus 4.7) but supports many agents.
Turns a single research idea into runnable experiments and a conference-ready paper by orchestrating an LLM-driven end-to-end workflow (literature → design → code → sandboxed runs → analysis → writing). Provides human-in-the-loop checkpoints, domain-specialist executors, and multi-layer citation verification.
Provides a command-line interface for AI agents to create, read, render, and modify Word/Excel/PowerPoint files headlessly. Includes a built-in high-fidelity HTML/PNG renderer, deterministic JSON APIs, resident mode and an MCP server for direct agent integration—suited for CI, containers, and automated document pipelines.
Turns any codebase, docs, or wiki into an interactive knowledge graph for exploration, semantic search, and Q&A. Uses a Tree-sitter + multi-agent LLM pipeline to auto-generate node summaries, guided tours, and diff impact analysis; CLI and dashboard integrations.
Provides modular, AI-optimized SKILL.md instructions that ground LLMs in Android development workflows and can be installed into agent environments via the Android CLI; focuses on tasks where LLMs underperform.
Provides modular “skills” that help designers and engineers audit, find, and improve UI animations and interaction decisions — optimized for use by AI agents or human-in-the-loop workflows. Distills domain-expert rules into actionable SKILL.md modules (review, improve, find opportunities, vocabulary).
Transforms articulated 3D asset creation into a programmatic, LLM-driven code-generation workflow that produces objects with semantic parts, robust geometry, and physical joints. Includes CLI generation, a local viewer, and pipelines for large-scale dataset contribution.
Orchestrates parallel CLI-based AI agents in isolated git worktrees so you can run multiple coding agents side-by-side, review AI-generated diffs, and link PRs/CI to each worktree. Desktop client with a mobile companion and BYO model subscriptions.