Browse plugins

Hookify creates custom hooks to prevent unwanted behaviors by analyzing conversation patterns or explicit instructions, using simple markdown configuration files with regex pattern matching. It supports four hook events (PreToolUse, PostToolUse, UserPromptSubmit, Stop) and provides commands to generate rules, list configurations, and interactively manage hook behavior without requiring restarts.

Pro Workflow is a complete AI coding system with self-correcting memory, persistent SQLite-indexed research wikis, and an auto-research loop that learns from corrections across sessions. It provides 41 skills, 8 agents, 23 commands, and intelligent hooks that inject relevant knowledge and enforce quality gates without requiring API keys or external services.

Claude-mem is a memory compression system that persists context across Claude Code sessions using 18 skills, 6 hooks, and an MCP server integration. It intercepts session and tool-use events to manage and compress conversation history, enabling continuity between separate coding sessions.

Ars Contexta generates a personalized knowledge management system through conversational setup, creating markdown vaults with wiki links, processing pipelines, automation hooks, and domain-native documentation—all derived from how you describe your thinking and work. The plugin provides 10 skills for extracting insights, finding connections, and maintaining your vault, plus a 6-phase pipeline that mirrors Cornell note-taking with a meta-cognitive layer for continuous system evolution.

Ponytail injects a minimalist decision ladder into Claude Code sessions, forcing the agent to question whether code is needed at all before building solutions. It works by hooking session start and prompt submission to apply YAGNI principles, prefer stdlib, and avoid unrequested abstractions.

This plugin provides five core skills for ecosystem navigation, skill creation, research pattern analysis, session reflection, and plugin development, with a UserPromptSubmit hook that forces skill evaluation on user input. It integrates via a shell-based activation script to enable automated skill assessment workflows.

memsearch automatically captures and indexes your Claude Code session history as semantic memories, allowing Claude to recall past work across sessions without manual saving. It runs entirely locally using ONNX embeddings and shell hooks, integrating with Claude Code's native skill and hook system.

Hivemind provides cloud-backed persistent memory using Deeplake, allowing you to read, write, and share memory across Claude Code sessions and agents through hooks that capture session events and tool usage. The plugin integrates with your workflow via Node.js commands and skills, enabling collaborative memory management across multiple Claude instances.

Ralph Wiggum implements continuous self-referential AI loops that repeat the same prompt through a Stop hook until task completion, allowing iterative refinement within a single Claude Code session. The technique runs Claude in a while-true pattern with persistent file state, letting the model read its own past work and fix failures autonomously.

This plugin adds audio feedback to Claude Code on Windows by playing system sounds when Claude finishes responding or when notifications occur using PowerShell. It provides non-blocking background playback and is configurable via environment variables to select different Windows system sounds or disable the feature entirely.

This skill provides expert Git workflow patterns including branching strategies (Git Flow, GitHub Flow, trunk-based), commit conventions, pull request processes, and CI/CD integration through a portable Agent Skill. It includes a `/pr-finish` command to automate pull request merge workflows and pre-commit hooks for validation.

This agent skill automatically detects missing CLI tools (77+ supported) and installs them via optimal package managers, while also providing proactive environment auditing and batch tool updates. It reacts to "command not found" errors and user requests to maintain development environments across multiple platforms.

Engram is a learning system that turns your AI assistant into an evidence-based tutor using first-principles Socratic questioning, blind-graded recall verification, and FSRS-based spaced repetition scheduling to ensure you retain knowledge. The plugin stores your learning state locally as plain JSON, works across six agentic platforms, and requires only Python 3 (no external APIs or accounts).