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.
Review-first terminal diff viewer that opens changesets in an interactive TUI with multi-file review stream, sidebar navigation, and inline AI/agent annotations. Supports split/stack responsive layouts, watch mode, and Git/Jujutsu pager integration.
Evaluates job postings and produces tailored CVs, cover letters, and interview prep using a Claude Code-driven agent workflow. Distinguishes itself with a drafter–reviewer loop, mandatory PDF compilation and ATS text-layer verification, plus extensible portal scrapers and LaTeX templates.
Hands-on, phase-based curriculum for building end-to-end AI systems from first principles — implement algorithms, run tests, and ship reusable artifacts (prompts, skills, agents, MCP servers) across Python, TypeScript, Rust, and Julia under an MIT license.
Defines 10 design principles and reference implementations for building agent-native, token-efficient CLIs that reduce token and turn costs for AI agents; includes the TOON output format, benchmarks (browser and GitHub), and an AXI catalog of tools.
Scans AI agent skills for security issues—detecting vulnerabilities, malicious patterns, and supply-chain risks before installation. Combines static AST checks (64 patterns across 16 categories) with optional LLM semantic review, OSV live CVE lookups, and JSON/Markdown/SARIF outputs for CI or manual review.