Runs untrusted AI-agent code, commands, and file operations inside isolated sandboxes that scale from local Docker to Kubernetes. One Sandbox Protocol unifies both runtimes, with gVisor, Kata, and Firecracker isolation and SDKs across five languages.
Fifteen reusable agent skills for curating LLM context windows, treating attention decay—not token capacity—as the real constraint. A routing layer benchmarked at 0.92 top-1 accuracy selects the right skill for each task.
Runs LLM-driven coding agents as durable workflows on Vercel, orchestrating isolated sandbox VMs for repo work, shell and file tools, and optional auto-commit/PR flows — designed for cloud-hosted, resumable developer automation.
Provides an agent-native personalized tutoring platform that combines persistent TutorBots, RAG-powered knowledge bases, and a CLI-first workflow. Designed for extensible agent skills, multi-channel deployment, and long-term learner memory.
Collection of self-contained Codex skills that automate recurring engineering tasks (many Apple-platform focused): release-note generation, iOS debugging, SwiftUI audits, multi-agent review and bug-hunt workflows. Best when integrated into Codex-driven developer tooling.
Provides a set of Agent Skills that let LLM agents read, edit, and manipulate Obsidian files (Markdown, Bases, JSON Canvas) and interact with Obsidian via the CLI. Implements the Agent Skills spec for use with Claude Code, Codex CLI, and OpenCode, enabling automated note workflows and RAG over an Obsidian vault.
Registry where OpenClaw agents publish, version, and vector-search text-based skills — each a SKILL.md plus supporting files. Adds a CLI with install pinning and moderation, plus a unified catalog that lists native code and bundle plugins beside skills.
Implements a Manus-style, file-backed planning workflow for AI agents using a three-file Markdown pattern (task_plan.md, findings.md, progress.md) to persist plans, findings and session logs—reducing context drift and enabling session recovery. Adds IDE/CLI hooks to re-read plans and verify completion.
Provides a set of versioned "skills" that codify UI design standards and automated checks for design engineers and AI agents. Includes a CLI to discover, install, and run skills like baseline UI rules, accessibility fixes, motion-performance tuning, and metadata corrections.
Runs an autonomous agent loop that uses AI coding tools (Amp or Claude Code) to implement PRD user stories iteratively, persisting context via git history, progress.txt and prd.json; designed for small, CI-backed tasks.