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By mgd34msu
Orchestrate multi-agent workflows to automate full-stack development: delegate planning, coding across React/Next.js backends/databases, security audits, testing, integrations like Stripe/AI services, performance optimization, code reviews, and deployments to Vercel/AWS.
npx claudepluginhub mgd34msu/goodvibes-plugin --plugin goodvibesQuick access to GoodVibes analytics-engine features. Tracks token usage, cost, cache performance, file operations, command execution, and agent activity across your Claude session.
Full codebase review with parallel goodvibes agent remediation. Analyzes 10 quality dimensions, generates master report, creates prioritized remediation plan, executes fixes with max 6 parallel goodvibes background agents (one task per agent, fresh context).
Load a skill's full content into context
GoodVibes plugin management commands (update, status, config)
Toggle precision-engine path sandboxing (allow/restrict external paths)
Meta-agent that creates specialized Claude Code subagents. Use when you need to build a new agent for a specific domain. Researches thoroughly, applies SDK patterns, and generates production-ready agent files. For skills, delegates to skill-factory.
Architecture and planning specialist. Use PROACTIVELY when designing system architecture, planning implementation strategies, breaking down complex tasks, identifying dependencies and risks, or making architectural decisions.
Deployment and DevOps specialist. Use PROACTIVELY when deploying applications, configuring CI/CD pipelines, setting up Docker/containerization, deploying to cloud platforms (Vercel, AWS, Railway, Fly.io), configuring environment variables, or setting up monitoring and error tracking. Triggers on: deploy, deployment, hosting, CI/CD, pipeline, Docker, container, Kubernetes, production, staging, environment variables, secrets, monitoring, Sentry, infrastructure.
Unified full-stack engineer for backend and frontend implementation. Use PROACTIVELY when user mentions: API, REST, GraphQL, tRPC, endpoint, route, database, SQL, Prisma, Drizzle, PostgreSQL, MongoDB, Redis, authentication, auth, login, JWT, OAuth, middleware, server, backend, validation, schema, migration, CRUD, component, React, Vue, Svelte, Next.js, Nuxt, Remix, Astro, frontend, page, layout, navigation, modal, form, button, card, table, responsive, CSS, Tailwind, styling, theme, dark mode, shadcn, Radix, animation, Framer Motion, accessibility, SSR, SSG, hydration. Also trigger on: "build an API", "create component", "add authentication", "implement feature", "fix bug", "add page", "build form", "connect database", "style this", "make responsive".
AI and LLM integration specialist for chat interfaces, streaming responses, RAG pipelines, embeddings, vector search, and tool/function calling. Use PROACTIVELY when user mentions: AI, LLM, ChatGPT, Claude, OpenAI, Anthropic, Vercel AI SDK, streaming, chat, completions, embeddings, RAG, vector, Pinecone, Weaviate, pgvector, function calling, tool use, AI agents. Triggers on: "integrate AI", "add chat", "LLM feature", "streaming responses", "RAG pipeline", "vector search", "embeddings", "AI chat interface", "function calling", "tool use".
Load PROACTIVELY when task involves building a complete feature across multiple layers. Use when user says "build a feature", "add user profiles", "create a dashboard", or any request spanning database, API, UI, and tests. Orchestrates multi-agent work sequentially: schema and migrations, API endpoints, UI components, tests, and review. The runtime engine handles WRFC chains automatically via <gv> directives. Handles dependency ordering and cross-layer type sharing.
Load PROACTIVELY when decomposing a user request into parallel agent work. Use when user says "build this", "implement this feature", or any request requiring multiple agents working concurrently. Guides task decomposition into parallelizable units, agent assignment with skill matching, dependency graph construction, and result aggregation. The runtime engine handles WRFC chain coordination automatically via <gv> directives.
Load PROACTIVELY when task involves AI, LLM, or machine learning features. Use when user says "add AI chat", "implement streaming responses", "build a RAG pipeline", "add embeddings", or "integrate OpenAI". Covers chat interfaces, streaming with Vercel AI SDK, retrieval-augmented generation, vector search, embeddings pipelines, tool/function calling, and provider abstraction for OpenAI, Anthropic, and local models.
Load PROACTIVELY when task involves building or modifying API endpoints. Use when user says "build an API", "add an endpoint", "create a REST route", "set up GraphQL", or "add tRPC procedures". Covers route design and file organization, request validation with Zod, response formatting, error handling patterns, middleware composition, authentication guards, rate limiting, pagination, and API documentation generation.
Load PROACTIVELY when task involves user identity, login, or access control. Use when user says "add authentication", "set up login", "add OAuth", "protect these routes", "implement RBAC", or "add sign-up". Covers session management, JWT tokens, OAuth2 flows, password reset, email verification, protected route middleware, role-based access control, and security hardening (CSRF, rate limiting, token rotation).
Matches all tools
Hooks run on every tool call, not just specific ones
Admin access level
Server config contains admin-level keywords
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Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Personal Claude Code + Codex dev stack: security hooks, AI-first code conventions, /security-review, /repo-map, /stack-check, portable statusline. Designed to complement other skills-based plugins, not replace them.
Automatic context engineering — observes your coding sessions and generates rules, suggestions, skills, and hooks so Claude gets smarter on your codebase over time
Self-evolving Claude Code system that learns from corrections, manages context, and improves every session
Curated skills for Claude Code and Codex power users - tool selection, workflow optimization, and productivity
Commands for loading context and priming Claude for specific tasks
(Alpha) Persistent memory, architectural decisions, and safety guardrails for Claude Code. Your agent starts every session with full project context — stack, decisions, patterns, safety rules, and a handoff from the previous session.
Executes bash commands
Hook triggers when Bash tool is used
Executes bash commands
Hook triggers when Bash tool is used
Modifies files
Hook triggers on file write and edit operations
Modifies files
Hook triggers on file write and edit operations
Uses power tools
Uses Bash, Write, or Edit tools
Uses power tools
Uses Bash, Write, or Edit tools
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Plug in. Receive good vibes.
A Claude Code plugin that replaces native tools with token-efficient precision equivalents, adds 73 MCP tools across 6 engines, and orchestrates 11 specialized agents with persistent cross-session memory.
Prerequisites: Node.js (v18+) and tmux must be installed.
Supported platforms: Linux (x64, ARM64), macOS (x64, Apple Silicon), Windows (x64, ARM64, ia32). Native AST binaries are included for all platforms.
claude plugin marketplace add mgd34msu/goodvibes-plugin
claude plugin install goodvibes@goodvibes-market
After installation, run the Setup hook to pre-write CLAUDE.md chain files:
claude --init-only
This ensures all GoodVibes instruction files are in place before your first session. On each session start, the SessionStart hook:
Set your output style:
/output-style goodvibes:vibecoding # Interactive mode
/output-style goodvibes:justvibes # Autonomous mode
| Component | Count | What You Get |
|---|---|---|
| Agents | 11 | Specialized roles (Opus/Sonnet) for engineering, review, testing, architecture, deployment, integration, planning |
| Skills | 25 | Tiered knowledge modules: protocol, orchestration, outcome, quality |
| MCP Tools | 73 | Token-efficient tools across 6 specialized engines |
| Hooks | 11 | Lifecycle automation (tool redirection, context injection, error recovery, setup) |
| Output Styles | 2 | Interactive (vibecoding) or fully autonomous (justvibes) |
| Templates | 3 | Production scaffolds |
Token consumption in AI coding sessions follows a layered pattern: individual operations add tokens, round trips resend conversation context, sessions accumulate state, and knowledge either persists or gets rediscovered. GoodVibes optimizes all seven layers.
Native tools return maximum output regardless of need. Precision tools let you request exactly the detail level required.
Note: Token estimates below are for typical small-to-medium files (~50-100 lines). Savings scale linearly with file size (e.g., a 500-line file would be ~5,000 tokens native vs. the same low precision overhead).
| Operation | Native Tool | Precision Tool | Savings |
|---|---|---|---|
| Check if a file exists | Read returns full content (~500+ tokens) | precision_read with count_only (~15 tokens) | ~97% |
| Count files matching a pattern | Glob returns all paths (~200+ tokens) | precision_glob with count_only (~15 tokens) | ~92% |
| Check if a pattern exists in code | Grep returns all matches with context (~300+ tokens) | precision_grep with count_only (~15 tokens) | ~95% |
| Re-read an unchanged file | Read returns full content again (~500+ tokens) | precision_read returns cache hit (~20 tokens) | ~96% |
| Get function signatures from a file | Read returns entire file (~500+ tokens) | precision_read with symbols extract (~50 tokens) | ~90% |
Mechanisms:
count_only, files_only, minimal, standard, verbose. Tools default to minimal output automatically — precision_edit defaults to minimal, precision_grep to files_only, precision_glob to paths_only. Savings are automatic even without explicit requests.precision_read): content, outline, symbols, ast, lines. Get function signatures (~50 tokens) instead of full file content (~500+ tokens). 75-95% savings.precision_edit): More precise than regex, fewer false positives, fewer failed edits requiring retry.Every API call resends the entire conversation (system prompt + tool definitions + all messages). Fewer calls = less overhead.