by vibeeval
AI software team for Claude Code - 138 agents, 295 skills, 73 hooks. Self-learning, multi-agent swarm, autonomous skill evolution.
# Add to your Claude Code skills
git clone https://github.com/vibeeval/vibecosystemLast scanned: 5/16/2026
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"issues": [],
"status": "PASSED",
"scannedAt": "2026-05-16T06:23:34.866Z",
"semgrepRan": false,
"npmAuditRan": true,
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}vibecosystem is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by vibeeval. AI software team for Claude Code - 138 agents, 295 skills, 73 hooks. Self-learning, multi-agent swarm, autonomous skill evolution. It has 506 GitHub stars.
Yes. vibecosystem passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/vibeeval/vibecosystem" and add it to your Claude Code skills directory (see the Installation section above).
vibecosystem is primarily written in C#. It is open-source under vibeeval on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh vibecosystem against similar tools.
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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:
vibecoCLI 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 initnpm installer,plugin.jsonfor 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.
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.
vibecosystem is a complete Claude Code ecosystem that creates a self-organizing AI team:
After setup, you say "build a feature" and 20+ agents coordinate across 5 phases.
npx vibecosystem init
curl -fsSL https://raw.githubusercontent.com/vibeeval/vibecosystem/main/install-remote.sh | bash
git clone https://github.com/vibeeval/vibecosystem.git
cd vibecosystem
./install.sh
That's it. Use Claude Code normally. The team activates.
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
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
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.
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
Every error becom