vibecosystem
Your AI software team. Built on Claude Code.
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vibecosystem turns Claude Code into a full AI software team — 138 specialized agents that plan, build, review, test, and learn from every mistake. No configuration needed — just install and code.
v2.0: 13 new agents (sast-scanner, mutation-tester, graph-analyst, mcp-manager, community-manager, benchmark, dependency-auditor, api-designer, incident-responder, data-modeler, test-architect, release-engineer, documentation-architect) + 23 new skills (SAST, compliance, product, marketing, MCP) + 4 new hooks + Agent Monitoring Dashboard + GitHub Actions CI/CD + MCP Auto-Discovery. See UPGRADING.md for details.
v2.1: 7 new skills (minimax-pdf, minimax-docx, minimax-xlsx, pptx-generator, frontend-dev, fullstack-dev, clone-website) + 2 new agents (document-generator, website-cloner). Document generation, pixel-perfect website cloning, and enhanced frontend/fullstack patterns.
v2.1.1: 7 new skills from oh-my-claudecode (smart-model-routing, deep-interview, agent-benchmark, visual-verdict, ai-slop-cleaner, factcheck-guard, notepad-system) + 1 new rule (commit-trailers).
v2.2: 5 features from Claude Code source — Agent Memory (persistent per-agent memory), Magic Docs (auto-updating docs), Dream Consolidation (cross-session memory cleanup), Smart Recall (frontmatter-based memory scoring), Plugin Toggle (hook enable/disable CLI). +7 hooks, skill references for 21 agents.
v2.2.1: Monetization stack — 1 new agent (monetization-expert), 2 new skills (paywall-optimizer, codex-orchestration), 3 updated skills (revenuecat-patterns, paywall-strategy, subscription-pricing). AI-powered paywall optimization with 14-category benchmarks, RevenueCat SDK patterns, Codex + Claude Code orchestration.
v2.3: vibeco CLI tool (stats, doctor, profiles, dashboard), 6 preset profiles for token savings, one-liner install (curl | bash).
v2.4: Terminal HUD (real-time statusline), prompt auto-improver (enriches vague prompts with context), persistent planning system (PLAN.md/PROGRESS.md/CONTEXT.md for 96.7% task completion). Competitive gap closure from analysis of 20+ ecosystem repos.
v3.0: npx vibecosystem init npm installer, plugin.json for official plugin ecosystem, worktree isolation on 60 producer agents, multi-LLM model routing (Haiku/Sonnet/Opus tiers), knowledge graph integration (6-71x token savings), dashboard v2 with token/cost tracking.
The Problem
Claude Code is powerful, but it's one assistant. You prompt, it responds, you review. For complex projects you need a planner, a reviewer, a security auditor, a tester — and you end up being all of them yourself.
The Solution
vibecosystem is a complete Claude Code ecosystem that creates a self-organizing AI team:
- 138 agents — specialized roles from frontend-dev to security-analyst
- 296 skills — reusable knowledge from TDD workflows to Kubernetes patterns
- 74 hooks — TypeScript sensors that observe, filter, and inject context
- 20 rules — behavioral guidelines that shape every agent's output
- Self-learning — every error becomes a rule, automatically
After setup, you say "build a feature" and 20+ agents coordinate across 5 phases.
Quick Start
npm (recommended)
npx vibecosystem init
One-liner
curl -fsSL https://raw.githubusercontent.com/vibeeval/vibecosystem/main/install-remote.sh | bash
Manual
git clone https://github.com/vibeeval/vibecosystem.git
cd vibecosystem
./install.sh
That's it. Use Claude Code normally. The team activates.
vibeco CLI
After install, the vibeco command is available:
vibeco stats # ecosystem statistics
vibeco list agents --search security # browse components
vibeco profile frontend # switch profile (saves tokens)
vibeco doctor # health check
vibeco dashboard # start monitoring UI
vibeco update # pull latest & reinstall
Profiles
Save tokens by loading only what you need:
| Profile | Agents | Skills | Use case |
|---|
minimal | ~15 | ~40 | Core only (review, test, verify) |
frontend | ~30 | ~60 | React/Next.js/CSS/a11y |
backend | ~44 | ~74 | API/DB/security |
fullstack | ~59 | ~96 | Frontend + Backend |
devops | ~33 | ~61 | CI/CD/K8s/cloud |
smart | 138 | 296 | Everything enabled + token-optimized plugin injection budgets |
all | 138 | 296 | Everything (default) |
Honest note on smart: it does not disable any agent or skill — capability is identical to all. The difference is session-start token cost: smart expects reduced context-injection budgets (via the env section of ~/.claude/settings.json), cutting startup overhead roughly 30-35% against vanilla all.
vibeco profile frontend # switch to frontend profile
vibeco profile all # back to everything
How It Works
YOU SAY SOMETHING VIBECOSYSTEM ACTIVATES RESULT
┌──────────────┐ ┌──────────────────────┐ ┌──────────┐
│ "add a new │──→ Intent ──→ │ Phase 1: scout + │──→ Code │ Feature │
│ feature" │ Classifier │ architect plan │ Written │ built, │
│ │ │ Phase 2: backend-dev │ Tested │ reviewed,│
│ │ │ + frontend-dev │ Reviewed│ tested, │
│ │ │ Phase 3: code-review │ │ merged │
│ │ │ + security-review │ │ │
│ │ │ Phase 4: verifier │ │ │
│ │ │ Phase 5: self-learner│ │ │
└──────────────┘ └──────────────────────┘ └──────────┘
Hooks are sensors — they observe every tool call and inject relevant context:
"fix the bug" → compiler-in-loop + error-broadcast ~2,400 tok
"add api endpoint" → edit-context + signature-helper + arch ~3,100 tok
"explain this code" → (nothing extra) ~800 tok
Agents are muscles — each one specialized for a specific job:
GraphQL API → graphql-expert (backup: backend-dev)
Kubernetes → kubernetes-expert (backup: devops)
DDD modeling → ddd-expert (backup: architect)
Bug reproduction → replay (backup: sleuth)
... 70 more routing rules
Self-Learning Pipeline turns mistakes into permanent knowledge:
Error happens → passive-learner captures pattern (+ project tag)
→ consolidator groups & counts (per-project + global)
→ confidence >= 5 → auto-inject into context
→ 2+ projects, 5+ total → cross-project promotion
→ 10x repeat → permanent .md rule file
No manual intervention. The system writes its own rules — and shares them across projects.
What's New in v2.0
- SAST Security Scanner — static analysis agent + hook for automated vulnerability detection
- Agent Monitoring Dashboard — real-time web UI for agent activity and performance
- MCP Auto-Discovery — automatic MCP server recommendations based on project type
- Changelog Automation — automatic changelog generation at session end
- Compliance Skills — SOC2, GDPR, HIPAA compliance checking
- Product & Marketing Skills — PRD writer, analytics setup, growth playbooks
- GitHub Actions CI/CD — automated PR review + issue fix workflows
- Mutation Testing — test quality measurement via mutation analysis
- Code Knowledge Graph — codebase structure analysis with graph-analyst
Core Features
Agent Swarm
Say "add a new feature" and 20+ agents activate across 5 phases.
Phase 1 (Discovery): scout + architect + project-manager
Phase 2 (Development): backend-dev + frontend-dev + devops + specialists
Phase 3 (Review): code-reviewer + security-reviewer + qa-engineer
Phase 4 (QA Loop): verifier + tdd-guide (max 3 retry → escalate)
Phase 5 (Final): self-learner + technical-writer
Self-Learning Pipeline
Every error becomes a rule. Automatically.
Dev-QA Loop
Every task goes through a quality gate:
Developer implements → code-reviewer + verifier check
→ PASS → next task
→ FAIL → feedback to developer, retry (max 3)
→ 3x FAIL → escalate (reassign / decompose / defer)
Cross-Project Learning
Patterns learned in one project automatically benefit all your projects.
Project A: add-error-handling (3x) ─┐
├→ 2+ projects, 5+ total → GLOBAL
Project B: add-error-handling (4x) ─┘
↓
Next session in ANY project → "add-error-handling" injected as global pattern
Each project gets its own pattern store. When the same pattern appears in 2+ projects with 5+ total occurrences, it's promoted to a global pattern that benefits every project — even brand new ones.
node ~/.claude/hooks/dist/instinct-cli.mjs portfolio # All projects
node ~/.claude/hooks/dist/instinct-cli.mjs global # Global patterns
node ~/.claude/hooks/dist/instinct-cli.mjs project <name> # Project detail
node ~/.claude/hooks/dist/instinct-cli.mjs stats # Statistics
Canavar Cross-Training
When one agent makes a mistake, the entire team learns from it.
Agent error → error-ledger.jsonl → skill-matrix.json
→ All agents get the lesson at session start
→ Team-wide error prevention
Adaptive Hook Loading
74 hooks exist but they don't all run at once. Intent determines which hooks fire.
Architecture
┌─────────────────────────────────────────────────────────┐
│ Claude Code │
│ │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ Hooks │ │ Agents │ │ Skills │ │
│ │ (74) │→ │ (138) │← │ (296) │ │
│ └────┬─────┘ └────┬─────┘ └──────────┘ │
│ │ │ │
│ ▼ ▼ │
│ ┌──────────┐ ┌──────────┐ │
│ │ Rules │ │ Memory │ │
│ │ (20) │ │ (PgSQL) │ │
│ └──────────┘ └──────────┘ │
│ │
│ ┌──────────────────────────────────────┐ │
│ │ Self-Learning Pipeline │ │
│ │ instincts → consolidate → rules │ │
│ │ + cross-project promotion │ │
│ └──────────────────────────────────────┘ │
│ │
│ ┌──────────────────────────────────────┐ │
│ │ Canavar Cross-Training │ │
│ │ error-ledger → skill-matrix → team │ │
│ └──────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────┘
Agent Categories
| Category | Count | Examples |
|---|
| Core Dev | 14 | frontend-dev, backend-dev, kraken, spark, devops, browser-agent, website-cloner |
| Review & QA | 8 | code-reviewer, security-reviewer, verifier, qa-engineer |
| Domain Experts | 36 | graphql-expert, kubernetes-expert, ddd-expert, redis-expert, paywall-planner |
| Architecture | 8 | architect, planner, clean-arch-expert, cqrs-expert |
| Testing | 6 | tdd-guide, e2e-runner, arbiter, mocksmith |
| DevOps & Cloud | 12 | aws-expert, gcp-expert, azure-expert, terraform-expert |
| Analysis | 11 | scout, sleuth, data-analyst, profiler, strategist, harvest |
| Orchestration | 16 | nexus, sentinel, commander, neuron, vault, nitro |
| Documentation | 6 | technical-writer, doc-updater, copywriter, api-doc-generator, document-generator |
| Learning | 7 | self-learner, canavar, reputation-engine, session-replay-analyzer |
Comparison
| Feature | vibecosystem | Single Claude Code | Cursor | aider |
|---|
| Specialized agents | 138 | 0 | 0 | 0 |
| Self-learning | Yes | No | No | No |
| Agent swarm coordination | Yes | No | No | No |
| Cross-project learning | Yes | No | No | No |
| Cross-agent error training | Yes | No | No | No |
| Dev-QA retry loop | Yes | No | No | No |
| Adaptive hook loading | Yes | No | No | No |
| Assignment matrix routing | Yes | No | No | No |
| Claude Code native | Yes | Yes | No | No |
| Zero config after install | Yes | Yes | No | No |
What's Included
| Component | Count | Description |
|---|
agents/ | 138 | Markdown agent definitions with specialized prompts |
skills/ | 296 | Reusable knowledge — TDD, security, patterns, frameworks |
hooks/src/ | 74 | TypeScript hooks — sensors, learners, validators |
rules/ | 20 | Behavioral guidelines — coding style, safety, QA |
Tech Stack
| Component | Technology |
|---|
| Runtime | Claude Code (Claude Max) |
| Models | Opus 4.6 / Sonnet 4.6 |
| Hook engine | TypeScript → esbuild → .mjs |
| Memory DB | PostgreSQL + pgvector (Docker) |
| Agent format | Markdown + YAML frontmatter |
| Skill format | prompt.md / SKILL.md |
| Cross-training | JSONL ledger + JSON skill matrix |
| Cross-project learning | Per-project instinct stores + global promotion |
Philosophy
hooks are sensors. observe, filter, signal.
agents are muscles. build, produce, fix.
the bridge between them: context injection.
no direct RPC. no message passing. by design.
implicit coordination through context.
Data & Privacy
- All data stays on your machine (
~/.claude/)
- No network requests, no telemetry, no cloud sync
- Self-learned rules go to
~/.claude/rules/
- Hooks run locally via Claude Code's native hook system
Multi-CLI Support
vibecosystem works with multiple AI coding tools:
| CLI | Installer | Instructions File | What You Get |
|---|
| Claude Code | ./install.sh | rules/*.md | Full support (agents + skills + hooks + rules) |
| Cursor IDE | ./install-cursor.sh | AGENTS.md + .cursor/rules/ | 6 MDC rules + AGENTS.md + skills |
| Codex CLI (OpenAI) | ./install-codex.sh | AGENTS.md | Skills only (296 skills) |
| OpenCode | Manual | AGENTS.md | Skills only |
# For Cursor IDE users:
./install-cursor.sh /path/to/your/project
# For Codex CLI users:
./install-codex.sh
See docs/codex-setup.md for Codex CLI setup, or copy .cursor/rules/ into any Cursor project.
Inspired By
vibecosystem stands on the shoulders of great open-source projects:
- Shannon by KeygraphHQ — Result<T,E> pattern, pentest pipeline, comment philosophy
- UI UX Pro Max by nextlevelbuilder — Named UX rules, UI style catalog, design token architecture
- Game Studios by Donchitos — Context resilience, incremental writing, gate-check system
- Skill Gateway by buraksu42 — Invisible skill routing, external catalog, one-question rule
- Pyxel by kitao — Retro game engine patterns, pixel art constraints, MML audio
- copilot-orchestra by ShepAlderson -- Phase-gated commits, plan documentation trail, 90% confidence threshold
- RevenueCat -- Subscription infrastructure, category benchmarks, paywall patterns
- Trail of Bits Skills by trailofbits -- Security audit patterns, variant analysis, false positive verification, sharp edges detection
Contributing
Contributions welcome! Areas where help is needed:
- More agent definitions — specialized roles for your domain
- More skill patterns — framework-specific knowledge (Rails, Flutter, etc.)
- Better hooks — new sensors, smarter context injection
- Documentation — tutorials, guides, examples
- Translations — improve existing or add new languages
Türkçe
Nedir?
vibecosystem, Claude Code'u tam donanımlı bir yapay zeka yazılım ekibine dönüştürür. Sadece tek bir asistan değil — planlayan, geliştiren, kod incelemesi (review) yapan, test eden ve yaptığı her hatadan öğrenen 138 uzman ajandan (agent) oluşan bir ekip.
Özel bir model yok. Özel bir API yok. Sadece Claude Code'un hook + agent + rules sistemi sonuna kadar kullanılmış durumda.
Hızlı Başlangıç
git clone https://github.com/vibeeval/vibecosystem.git
cd vibecosystem
./install.sh
Nasıl Çalışır?
- Hook'lar sensördür — gözlemler, filtreler ve işaret eder.
- Agent'lar kas gibidir — çalışır, üretir ve düzeltir.
- Aralarındaki köprü: Context (bağlam) enjeksiyonudur.
- Doğrudan RPC yoktur — bu kasıtlı bir mimari tercihtir.
- Context üzerinden örtük (implicit) koordinasyon sağlanır.
Felsefe
Kullanıcının hiçbir şey hatırlamasına gerek yoktur.
Her şey otomatiktir.
License
MIT
Built by @vibeeval
No custom model. No custom API. Just good engineering.
