A curated collection of Codex plugin examples demonstrating plugin manifests, companion surfaces (skills, hooks, assets), and sample integrations. Highlights richer, opinionated examples for Figma, Notion, iOS/macOS/web builds and MCP-backed bundles — useful for prototyping assistant plugins.
Aggregates 60+ real-time OSINT feeds into a self-hosted geospatial dashboard and exposes an HMAC-signed agentic AI command channel so LLM-driven agents can query and act on live telemetry; privacy is experimental.
Lets an LLM autonomously propose, edit, run, and evaluate short single‑GPU LLM training experiments — fixed 5‑minute runs (~12 experiments/hour). Agent edits a single train.py; humans supply goals via program.md. Single‑GPU, val_bpb metric.
A self-hostable workspace where humans and AI agents collaborate in shared rooms, using a Nostr-based signed event log to unify chat, workflows and git events. Agents act as members with their own keys and audit trails, enabling scoped agent actions without shared secrets.
Runs locally to learn your tastes and proactively discover content across Bilibili, Xiaohongshu, Douyin, YouTube, X, Zhihu, Reddit and the open web. Local-first agent storing data in a local SQLite, with a browser extension, optional desktop backend bundling embeddings (bge-m3/Ollama), and conversational feedback to refine recommendations.
Generates production-ready App Store and Google Play screenshots from app metadata and style preferences using AI. Scaffolds a Next.js project, composes ad-style slides with localized/RTL support, and exports PNGs at all required Apple and Google resolutions.
Automatically converts codebases into structured, JSON-first CLI harnesses so LLMs and AI agents can reliably control desktop and server software; includes a CLI-Hub registry, demo harnesses, and agent plugins for one‑command generation and installation.
Automatically evolves Hermes Agent skills, prompts, tool descriptions and code using DSPy + GEPA — mutating text via API calls, evaluating trace-based failures, and selecting variants that pass tests and human PR review. No GPU training required; runs cost roughly $2–$10 per optimization.
Author HTML-based video compositions and render deterministic, frame-accurate MP4s with agent-friendly tooling — preview in the browser, drive generation via AI agent skills, and use adapter runtimes (GSAP, Lottie, Three.js).
Lightweight, Markdown-only skill pack that lets LLM agents autonomously run ML research workflows—literature survey, idea discovery, cross-model review loops, experiment automation and paper writing—designed for Claude Code, Codex CLI, Cursor and local model setups.
Provides a single persistent database and open protocol so multiple AI tools share the same memory — built-in vector search, an AI gateway, and capture/skill extensions. Best for teams and power users who want a unified, self-hosted agent memory instead of siloed notes or per-tool caches.
A 23-skill Claude Code toolkit that composes an LLM-driven virtual engineering team (CEO, designer, eng manager, QA, security, release) into slash-command workflows — includes real-browser QA, a persistent GBrain memory, multi-agent integrations, and team auto-update semantics.