Provides a visual plan-and-code review UI that lets humans annotate, approve, or send structured feedback to coding agents and PR diffs. Integrates with Claude Code, OpenCode, Copilot CLI, Pi and Codex; supports encrypted short-link sharing and self-hosting.
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.
Defines a standardized commerce protocol so platforms, merchants, PSPs and credential providers can declare capabilities, discover services, manage checkouts, exchange payment tokens, and enable AI agents to perform end-to-end purchase flows.
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.
A Claude Code plugin for long-form serial fiction that keeps characters, timeline, and world rules consistent across hundreds of chapters. Facts are committed to a versioned state store, and review gates flag contradictions before each chapter.
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.
Compresses any context sent to LLMs (tool outputs, DB reads, RAG results, files, logs) to cut tokens by ~70–95% while preserving reversible originals; runs as a proxy or Python/TypeScript SDK with integrations for common agent frameworks.