Git plugins

Enforce test-driven development, structured debugging, and code review workflows in Claude Code. Plan features in granular tasks, execute them in isolated worktrees with parallel subagents, and verify completeness with automated checks before merging.

Communicate with Claude Code in an ultra-compressed style that cuts token usage ~75% while preserving technical accuracy, with compressed sub-agents for code review, git commits, file editing, and code exploration.

Run a disciplined engineering workflow: prototype designs, write PRDs, triage issues, refactor code, debug regressions, and publish articles — all with structured agent skills that enforce domain-driven practices and git safety.

Persist and retrieve conversational context across Claude Code sessions using a local filesystem-based memory store, enabling agents to recall past decisions, work patterns, and bugfixes without re-reading source files.

Automate comprehensive PR reviews with specialized agents that analyze code quality, test coverage, error handling, type design, comments, and simplification, producing a categorized issues summary with critical findings, suggestions, strengths, and an action plan.

Equip AI coding agents with production engineering skills to handle full dev lifecycles: refine ideas to specs, implement via TDD slices, run tests/debug, perform multi-axis code reviews, optimize perf/security, automate CI/CD, and execute ship checklists.

Manage feature development with AI-powered code architecture exploration, code review, and CLAUDE.md auditing, plus create and benchmark custom agent skills with evals.

Audit CLAUDE.md files across repositories by discovering them with find, evaluating quality against rubrics, generating reports, and applying targeted improvements after approval. Capture learnings from Claude Code sessions to propose concise updates to CLAUDE.md or .claude.local.md files with user approval.

Run Claude in a continuous iterative loop, feeding the same prompt back after each response for repeated refinement until task completion. Supports cancellation, max iterations, and completion promises.

Automate multi-agent code reviews on GitHub pull requests, auditing CLAUDE.md files, detecting bugs, analyzing git history and prior PRs, reviewing code comments, and scoring issues by confidence level to prioritize fixes.

Orchestrate swarms of specialized AI agents to automate end-to-end software development: plan features, implement code with Rails/Python/TS patterns, conduct multi-perspective reviews for architecture/security/performance, resolve todos/PR feedback in parallel, run browser/iOS tests, sync Figma designs, generate docs/videos, and ship PRs.

Automates end-to-end feature development: explores codebase to map dependencies, patterns, and execution paths; designs architectures with blueprints, data flows, and build sequences; implements code changes; reviews for bugs, security vulnerabilities, and quality issues using high-confidence filtering.

Generate production-ready stateful CLI harnesses for GUI applications from local paths or GitHub repos, implementing Click CLI with REPL/JSON support, pytest unit/E2E tests, and docs. List installed harnesses, refine coverage gaps, run tests to verify functionality, and validate against standards.

Delegates product strategy, legal/licensing, business analysis, project management, UX research, content marketing, customer success, technical sales, technical writing, and WordPress development to specialized AI agents

Run multi-perspective code reviews across architecture, security, performance, and best practices, including git-based PR analysis with specialized agents for vulnerability scanning and architectural integrity.

Delegate expert-level code reviews, security audits, penetration tests, QA automation, accessibility compliance checks, performance optimizations, chaos engineering, and compliance validations to specialized sub-agents across codebases, infrastructure, and systems.

Build and manage cloud infrastructure and deployment pipelines with AWS serverless, Docker, Kubernetes, Terraform, and CI/CD workflows, including environment setup, containerization, GitOps, and production deployment strategies.

Implement a complete QA and testing workflow: set up A/B tests with hard gates, automate browser testing with Playwright/Puppeteer, enforce code review checklists and TDD, debug systematically, and fix failing tests using pytest patterns.

Manage Python projects via structured tracks for features, bugs, refactors: initialize context artifacts like product.md and tech-stack.md, create detailed specs and phased plans, implement tasks with strict TDD workflow using pytest coverage and git commits, monitor status, revert commits, and validate artifacts for consistency.

Enforce code quality and security checks after every change, automate git commits and pushes with conventional messages, generate structured task checklists, guide incremental refactoring, and apply systematic debugging—all within Claude Code.

Delegate complex coding tasks and adversarial code reviews to Codex CLI from within Claude Code, with background job support, git-scoped analysis, and result retrieval.

Automate Git workflows by cleaning up gone remote branches and worktrees, intelligently staging changes with generated commit messages, and creating new feature branches with pushes and GitHub PRs via simple commands.

Automates OSS maintenance workflows: changelog generation, conventional commits, PR management, structured documentation, advanced Git operations, and code review handling.

Manage AI-supervised issue tracking directly from the CLI — create, prioritize, block, and close tasks with persistent memory across coding sessions, while an autonomous agent discovers and claims ready work.

Run the full engineering lifecycle from a chat interface: review code, debug issues, design systems, document decisions, manage incidents, and generate standups. Connects to Linear, Jira, Slack, Notion, Datadog, PagerDuty, and Benchling for project management, monitoring, and incident response.

Sync tasks from GitHub, Linear, Monday.com, ClickUp, Asana, Jira, and Notion into a local TASKS.md file, then manage them with Active/Waiting/Someday/Done sections. Decode acronyms, nicknames, and project context into a persistent memory system that keeps your work organized across calendar, email, and chat.

Debug GitHub Actions failures by pinpointing root causes, identifying breaking commits, and scanning for fix PRs. Clone or half-clone Claude conversations to branch experiments or cut token usage. Generate HANDOFF.md summaries for agent handoffs. Fetch Reddit content when WebFetch is blocked. Review conversations to suggest CLAUDE.md improvements.

Index git repositories into a knowledge graph to trace execution flows, analyze blast radius of code changes, and perform augmented code search for debugging, impact analysis, and safe refactoring.

Coordinate multiple AI providers (Claude, Gemini, API-based) to automate code review, TDD, debugging, security audits, design extraction, and documentation generation, with autonomous task decomposition and parallel execution for complex workflows.

Turns any Claude conversation into a persistent, self-organizing Obsidian wiki vault with hybrid retrieval, methodology-based filing (LYT/PARA/Zettelkasten/Generic), automated research loops, canvas management, pre-commit auditing, and health checks — enabling compounding knowledge that survives across sessions and projects.

Manage GitLab projects by accessing repositories, creating and reviewing merge requests, monitoring CI/CD pipelines, handling issues, and updating wikis through remote API integration with a personal access token.

Manage AI-driven development workflows with hierarchical task trees, dependency graphs, automated subtask expansion, PRD-to-task parsing, status tracking, and intelligent task orchestration via natural language commands.

Autonomously optimize code files by measurable metrics through iterative experiments: set up target file, eval command, and loop intervals (10min-monthly); AI edits code, commits to git branches, evaluates with Python, keeps improvements. Resume, run manually, or check dashboard status.

Generate self-contained HTML visual explainers for code diff reviews, implementation plans, slide decks, diagrams, and project recaps — with factual verification against git history and one-click deployment to Vercel.

Install 124 ready-to-use Claude Code skills to automate 50+ third-party services including CRMs (HubSpot, Salesforce), PM tools (Jira, Asana), analytics (GA4, Mixpanel), cloud storage (Google Drive, Dropbox), GitHub/Vercel deploys, doc/PDF/image processing, React artifact building, design generation, and dev productivity tasks via Rube MCP/Composio integrations.

Delegate full-stack development workflows to Claude via 213 specialized agents, commands, and skills: refactor code, generate tests/deployments/Dockerfiles/K8s manifests, audit security/performance, document APIs/onboarding, orchestrate Git ops, and apply patterns across JS/TS/Python/Rust/Go/Java stacks.

Spawn parallel AI subagents in isolated git worktrees to compete on tasks like code optimization, refactoring, test writing, or bug fixing. Evaluate results using pytest metrics or LLM judging on git diffs, rank agents, and merge the top performer into your base branch.

Orchestrates a structured Plan-Work-Review development cycle for solo developers using Claude Code, automating project setup, task tracking, code generation, testing, CI/CD, and cross-session memory while optionally coordinating with Cursor for multi-agent workflows.

Perform AI-powered code reviews on GitHub and GitLab pull requests by connecting to Greptile API. View and resolve review comments directly within Claude Code. Query indexed repositories for code search, codebase Q&A, and context retrieval to accelerate development workflows.

Mark up and refine AI-generated plans interactively in a UI, annotate markdown files, messages, and git changes for review, share for team collaboration, browse plan archives, and automate workflows with plan mode hooks.

Use structured writing methods to deconstruct books, papers, and ideas into digestible formats (library cards, sketchnotes, Q&A pairs, plain-language explanations) and render them as HTML slides or PNG visuals. Also traces research lineages and converts English texts into Chinese with deep annotation.

Learn coding skills interactively with personalized tutorials and spaced repetition quizzes drawn from your own codebase. Use /teach-me for lessons, /quiz-me for practice with feedback, track progress, and sync tutorial data to a private GitHub repo.

Generate complete AI-powered wiki sites from git repositories as dark-mode VitePress static sites with Mermaid diagrams, source citations, hierarchical catalogues, audience-tailored onboarding guides, changelogs, deep research reports, and codebase Q&A. Export to Azure DevOps Wiki or deploy via GitHub Actions to Pages.

Semi-automated research assistant for academic ML/AI research and software development, enabling literature review with Zotero integration, paper writing with Nature/NeurIPS templates, experiment analysis with statistical validation, and Obsidian-based project knowledge management, plus code quality enforcement and CI workflows.

Implement Trail of Bits handbook security testing workflows: fuzz Rust, Python, C/C++, Ruby code with AFL++, libFuzzer, cargo-fuzz, Atheris; instrument AddressSanitizer; run static analysis via Semgrep, CodeQL; generate coverage reports, dictionaries, and bypass obstacles for vulnerability detection.

Provides 20 Chinese-language skills for Claude Code and compatible AI coding tools, enabling structured brainstorming, TDD, debugging, code review, git workflows, MCP server development, and multi-agent task execution with Chinese-language conventions for commits, documentation, and team communication.

Orchestrate structured, spec-driven development workflows for AI coding agents — from project initialization and phased planning through autonomous execution, code review, testing, and milestone tracking. Includes context engineering, knowledge graphs, and multi-agent debugging.

Apply Maoist dialectical reasoning and strategic frameworks to software development: prioritize tasks via contradiction analysis, resolve trade-offs, investigate unknowns, self-criticize completed work, and bootstrap projects from zero resources using phased warfare tactics.

Run a complete AI-assisted coding workflow with self-correcting memory, persistent FTS5-indexed research wikis, auto-research loops, multi-LLM council deliberation, and 8 specialized agents that coordinate parallel sessions, enforce quality gates, audit context costs, and capture learnings across every session.

Crystallize vague project requirements into executable Seed specifications through Socratic interviews, then run, evaluate, and iteratively refine them with three-stage verification, drift detection, and evolutionary loops.

Build multi-language code graphs to map call graphs, attack surfaces, blast radius, taint propagation, privilege boundaries, and complexity hotspots for security audits. Visualize architecture with Mermaid diagrams, compare snapshots across git commits for evolution analysis, triage mutation testing survivors, generate crypto test vectors, diagram protocols, and project SARIF findings onto graphs.

Quickly pack local or remote GitHub repositories into AI-optimized formats (XML, Markdown, JSON, plain) with compression, file filters, git diffs/logs, and clipboard copy using simple slash commands.

Analyze local and remote GitHub repositories using Repomix CLI to explore code structure, search for patterns, and answer questions about components, architecture, and content.

Build and orchestrate advanced Claude Code agentic workflows by creating meta-prompts, subagents, hooks, MCP servers, slash commands, and skills; execute hierarchical plans, run autonomous coding loops, apply expert debugging and productivity frameworks like 5 Whys or Eisenhower Matrix, and audit components for compliance and quality.

Orchestrate autonomous multi-agent sprints to develop full features from specs.md: agents handle architecture, parallel implementation of Next.js frontends and Python/FastAPI backends, CI/CD setup, automated testing, UI QA, reviews, and iterative convergence with structured reports and git safety.

Generate 35 structured engineering documents — including incident postmortems, architecture decision records, code review checklists, PR descriptions, changelogs, runbooks, test strategies, threat models, SLO definitions, and migration plans — directly from rough notes, logs, or git history within Claude Code.

Build interactive web UIs for MCP servers and Claude Desktop apps using guided Claude Code skills. Add UIs to existing servers via Apps SDK, convert web apps to hybrid MCP format with shared code and tool registration, create new apps from React/Vue/Svelte templates with Vite bundling, or migrate OpenAI Apps SDK projects.

Migrate React Native apps to newer versions by applying incremental diffs, updating iOS/Android configs, resolving CocoaPods and Gradle changes, and handling breaking API updates

Streamline end-to-end Obsidian plugin development and vault management: scaffold projects with TypeScript setups, implement UI views/events/data handling, optimize performance/security, establish local dev loops/CI/CD/release pipelines, migrate content, and troubleshoot errors using 24 specialized skills.

Configure, deploy, optimize, troubleshoot, and integrate CodeRabbit AI code reviews across GitHub and GitLab repositories. Automate CI merge gates, cost tuning, security policies, local dev loops, performance monitoring, migrations from other tools, and webhook handling using 24 targeted skills.

Automatically discover and hierarchically load AGENTS.md files across project directories into Claude's agent context, merging instructions with conflict detection and caching for specialized behaviors without manual setup. Sync all agent contexts into CLAUDE.md under organized sections with backups and summaries.

Build and manage PortalJS data portals: recommend an architecture, scaffold a portal, add datasets (CSV/TSV/JSON/GeoJSON), create charts (recharts) and interactive maps (Leaflet), connect a CKAN backend, harvest datasets from open-data platforms, infer data schemas, audit data quality, and deploy to PortalJS Arc hosting.

Run AI-powered code reviews on uncommitted changes, branch diffs, or specific commits using external LLM CLIs (OpenAI Codex, Google Gemini). Bundles a local MCP server for direct tool access via the 'codex' command.

Automate spec-driven development workflows with AI agents that handle GitHub issue fixing, PR review, deep research, image generation, and session analysis. Includes custom slash commands, skill creation, and browser automation via Chrome DevTools Protocol.

Scaffold production-grade Claude Code plugins with marketplace integration, validate structure and schemas, audit for security vulnerabilities and best practices, and automate semantic version bumps across manifests and catalogs using auto-invoked skills and interactive commands.

Automate overnight software development by configuring Git hooks for TDD enforcement with tests and lints, then run Claude autonomously for 6-8 hours to build features that pass all checks by morning.

Generate multi-stage CI/CD pipelines in YAML for GitHub Actions, GitLab CI, Jenkins, and CircleCI. Automate workflows covering linting, testing, Docker image builds/pushes, security scans, and gated deployments to staging/production on Kubernetes.

Accelerate Atomic Agents app development through a guided 7-phase workflow: delegate schema design, agent and tool creation, architecture planning, codebase analysis, and code review to specialized AI sub-agents for scalable multi-agent LLM systems.

Integrate OpenEvidence medical AI for clinical decision support in healthcare SaaS: run evidence-based queries, drug interactions, DeepConsult syntheses; automate auth setup, rate limiting, caching, RBAC, monitoring, CI/CD pipelines, Docker deploys, and production checklists in TypeScript/Node.js/Python projects.

Combine multiple Git repositories into unified archives for AI-powered codebase analysis, with built-in security scanning and file search capabilities.

Persist conversational memory across Claude Code sessions by saving architectural decisions, bug fixes, and design patterns to a project memory store, then search past sessions for prior work, preferences, and implementation details to maintain context continuity.

Manage environment configurations and secrets across dev/staging/prod deployments using .env files, Kubernetes ConfigMaps/Secrets, and AWS SSM. Audit values, encrypt secrets with sops, validate schemas, detect drift, and run promotion workflows. Generate secure, scalable DevOps setup code for Docker, Kubernetes, Terraform, AWS, and GCP infrastructure.

Generate test reports by parsing JUnit XML, Jest JSON, pytest results, and coverage data into Markdown/HTML formats with metrics, failures, slowest tests, trends, and CI annotations. Aggregate results across frameworks for summaries and exports in HTML, PDF, or JSON.

Generate production-ready GitOps workflows for Kubernetes using ArgoCD or Flux, creating manifests, sync policies, multi-environment promotions, RBAC configurations, notifications, and CI/CD integrations for secure, scalable continuous deployments.

Track regression tests across code releases by mapping git commits to pytest or Jest tests, tagging markers for suites, flagging coverage gaps, generating pass/fail reports with flaky detection, viewing history, and enforcing runs in CI/CD pipelines.

Use AI to generate conventional commit messages from staged Git changes. Analyzes code diffs to classify updates as feat, fix, refactor, chore, or docs, then crafts standardized messages with proper prefixes for consistent Git history, changelogs, and automation compatibility.

Orchestrate multi-agent coding workflows with context-aware task decomposition, parallel subtask execution, automated code review, and TDD test generation.

Perform security reviews of pull requests, commits, or code diffs using git history for context, blast radius estimation, test coverage checks, and markdown report generation.

Automate an entire PRP workflow loop — plan, implement, debug, review, commit, and PR — using specialized agents that execute plans, investigate issues, run multi-aspect code reviews, and enforce a review gate on session stop.

Master Cursor IDE AI workflows using 30 guided skills: install and authenticate, configure custom models and rules, optimize indexing and performance, automate Composer for multi-file refactoring and scaffolding, troubleshoot errors, manage teams with SSO, and audit compliance.

Generate read-only Markdown discrepancy reports validating messaging consistency—including tone, terminology, versions, and structure—across HTML-based websites (WordPress, Hugo, Next.js, React, Vue, etc.), GitHub repositories, and local documentation, with severity levels and fix suggestions.

Use Claude to manage Granola AI meeting notes workflows end-to-end: automate installations and upgrades, integrate with GitHub/Linear/Slack via Zapier for action items, optimize costs/performance/security, export data, troubleshoot issues, and deploy enterprise setups with RBAC/observability.

Orchestrate a full BMAD agile workflow with role-based AI agents (PO, Architect, SM, Dev, QA) to build projects from descriptions. Handles repo scanning, interactive requirements and architecture design, sprint planning, automated coding with tests, QA validation, code reviews, and user approval gates for production-ready delivery.

Execute 175 slash commands to automate git workflows like branching/PR creation/issue syncing with Linear, code quality reviews/refactors/fixes, test generation/setup/coverage, CI/CD pipelines, security/performance audits, documentation generation, project scaffolding/setup, and deployments across JS/TS/Python/Go/Rust/Svelte stacks.

Automate Lucidchart diagram creation and management in Node.js/TypeScript apps via API: import shapes/lines from .lucid/JSON/CSV, link data dynamically, export PNGs, handle auth/errors/rate limits/webhooks, with CI/CD setup, Docker deployment, local dev loops, debugging bundles, security checklists, and prod optimizations.

Analyze local Git branches and worktrees to categorize them as merged, squash-merged, superseded, or active work; group related branches; review and safely delete unnecessary ones with user approval before any changes.

Orchestrate AI coding agents through a repeatable engineering workflow covering requirements, design, implementation, testing, code review, and security — with persistent memory and evidence-based verification.

Automate development workflows by walking through code files line-by-line in VSCode or Vim, logging timestamped work sessions with file changes in daily Markdown, generating detailed issue specs staged in Git, engaging in adaptive Socratic quizzes for learning, and delegating UI validation tasks to a browser agent using Chrome DevTools.

Research any topic via web search, analyze findings, and automatically create structured GitHub issues with titles, summaries, key points, recommendations, source links, labels, and assignees. Turn investigations into trackable tickets for security vulnerabilities, APIs, features, or technical explorations using skills or CLI commands.

Equip Windsurf AI IDE with 30 Cascade skills to automate code generation, debugging, testing, multi-file refactoring, CI/CD workflows, Docker setups, Git integrations, security configurations, and enterprise onboarding, streamlining full dev lifecycles.

Master Windsurf AI IDE with 30 skills to automate Cascade multi-file coding workflows, troubleshoot IDE issues, optimize performance and costs, configure enterprise RBAC/security/CI gates, deploy to Netlify/Vercel, and scale for large teams/monorepos.

Scan your codebase and Git history for exposed secrets like API keys, passwords, tokens, and credentials using pattern matching and entropy analysis. Receive detailed reports pinpointing file locations, secret types, severity ratings, and step-by-step remediation guidance to secure your project fast.

Orchestrate complex test workflows across Jest, Vitest, pytest, Playwright, and Cypress with parallel execution, test sharding, dependency management, flakey retries, affected test selection, and result aggregation in GitHub Actions or GitLab CI. Generate optimized configs for CI/CD pipelines.

Orchestrate multi-agent AI workflows in Claude Code: track work via convoys and beads, deploy polecat/crew agents, merge via refinery, install/monitor with gt/bd CLI for AI-powered software factories.

Orchestrate multi-stage deployment pipelines across dev, staging, and prod environments using Kubernetes and CI/CD platforms like GitHub Actions and Jenkins. Apply strategies such as blue-green, canary, and rolling updates. Generate production-ready pipeline configurations, setup code, and documentation tailored to Docker, Terraform, and AWS infrastructure.

Generate AI-powered conventional commit messages from staged Git changes: auto-classifies feat/fix/docs types, detects scopes/breaking changes, matches project commit history style. Preview the message, confirm, and auto-commit in one workflow.