A Complete AI Agency with 120+ Specialized Expert Agents
msitarzewski/agency-agents -- 57.8k stars -- MIT License
agent37.club
Specialization over generalization. Each agent is a domain expert with personality, processes, and proven deliverables -- not a Swiss Army knife.
Works with Claude Code, Cursor, Windsurf, Aider, Gemini CLI, and OpenCode via conversion scripts.
agency-agents/ ├── engineering/ # 23 agents ├── design/ # 8 agents ├── marketing/ # 26 agents ├── sales/ # 8 agents ├── product/ # 5 agents ├── project-management/ # 6 agents ├── testing/ # 8 agents ├── support/ # 6 agents ├── spatial-computing/ # 6 agents ├── specialized/ # 22 agents ├── academic/ # research ├── strategy/ # strategy ├── scripts/ # tooling │ ├── convert.sh │ ├── install.sh │ └── lint-agents.sh └── examples/ # usage demos
Every agent file follows the pattern:
{division}-{role-name}.md # Examples: engineering-frontend-developer.md design-ux-researcher.md marketing-seo-specialist.md specialized-agents-orchestrator.md
convert.sh generates integration files for each platform. install.sh copies them to the right location. lint-agents.sh validates agent file quality.
| Section | Purpose |
|---|---|
| Identity | Name, emoji, color, vibe statement -- the agent's "character sheet" |
| Mission | Primary responsibilities and areas of expertise |
| Workflow | Step-by-step process for completing tasks |
| Critical Rules | Non-negotiable constraints the agent must always follow |
| Deliverables | Templates and examples of actual output |
| Success Metrics | Measurable criteria for evaluating work quality |
# Frontend Developer Agent Name: Frontend Developer Emoji: 🖥️ Color: Cyan Vibe: "Pixel-perfect precision" ## Missions 1. Editor Integration Engineering 2. Web Application Development 3. Performance & UX Optimization 4. Code Quality & Scalability ## Critical Rules - Lighthouse > 90 always - WCAG 2.1 AA compliance - LCP < 2.5s, FID < 100ms ## Communication Style "Reduced render time by 80%"
23 agents -- frontend, backend, DevOps, security, AI, mobile, embedded, SRE
8 agents -- UI, UX research, UX architecture, brand, visual storytelling
26 agents -- SEO, social (every platform), content, growth, podcasts
8 agents -- outbound, discovery, deals, proposals, pipeline, coaching
5 agents -- sprint planning, trends, feedback, behavioral nudges, PM
6 agents -- studio ops, project shepherd, experiment tracking, Jira
8 agents -- QA evidence, reality check, performance, API, accessibility
6 agents -- customer support, analytics, finance, infra, legal, exec summary
6 agents -- XR interface, visionOS, Metal, immersive dev, cockpit UX
22 agents -- orchestrator, blockchain, compliance, MCP builder, ZK, identity
Research-focused and strategic planning agents
All open-source under MIT license
Notable: includes China-specific agents (WeChat, Feishu) reflecting the global contributor base.
# Performance-First Lighthouse > 90 for all metrics Optimize from project inception Monitor Web Vitals continuously # Accessibility WCAG 2.1 AA compliance always Proper ARIA labels Semantic HTML required Keyboard nav + screen reader
| Agent | Focus |
|---|---|
| UI Designer | Visual interfaces and component design |
| UX Researcher | User studies, personas, journey maps |
| UX Architect | Information architecture, wireframes |
| Brand Guardian | Brand consistency and guidelines |
| Visual Storyteller | Data visualization and narratives |
| Whimsy Injector | Delight moments and micro-interactions |
| Image Prompt Engineer | AI image generation prompts |
| Inclusive Visuals | Diverse, accessible imagery |
| Agent | Focus |
|---|---|
| PPC Campaign Strategist | Search ads strategy |
| Search Query Analyst | Keyword research and intent |
| Paid Media Auditor | Campaign health checks |
| Tracking Specialist | Conversion tracking, attribution |
| Ad Creative Strategist | Copy, visuals, A/B tests |
| Programmatic Buyer | Display and programmatic ads |
| Paid Social Strategist | Social ad campaigns |
| Agent | Focus |
|---|---|
| Outbound Strategist | Cold outreach, sequences |
| Discovery Coach | Qualification, needs analysis |
| Deal Strategist | Negotiation, closing |
| Sales Engineer | Technical demos, POCs |
| Proposal Strategist | RFP responses, proposals |
| Pipeline Analyst | Forecasting, pipeline health |
| Account Strategist | Expansion, retention |
| Sales Coach | Training, call reviews |
| Agent | Focus |
|---|---|
| Sprint Prioritizer | Backlog grooming, prioritization |
| Trend Researcher | Market analysis, competitor intel |
| Feedback Synthesizer | User feedback to insights |
| Behavioral Nudge Engine | UX psychology, conversion |
| Product Manager | Roadmap, stakeholder alignment |
These 8 sales agents cover the complete B2B sales cycle from cold outreach to account expansion.
The Evidence Collector takes actual screenshots and validates against specs -- not just running test suites.
Heavy Apple ecosystem focus -- reflects the growing importance of visionOS and Metal.
Verify spec exists. Spawn project-manager-senior to generate task list from requirements.
Spawn ArchitectUX for foundation -- CSS, docs, component structure.
For each task: spawn developer, build, spawn QA, validate. PASS or FAIL (max 3 retries).
Spawn testing-reality-checker for comprehensive end-to-end validation.
# Decision Logic if task passes QA: mark_complete() advance_to_next() reset_retry_counter() elif task fails QA: increment_attempts() if attempts < 3: loop_back_with_feedback() else: escalate() continue_pipeline()
Every task requires QA before advancement. Evidence-based decisions using actual agent outputs.
"Phase 2 complete, advancing to Dev-QA loop with 8 tasks" "Task 3 failed QA (attempt 2/3), looping back with feedback" "All tasks passed, spawning RealityIntegration"
The orchestrator spawns specialist agents for each task, passing them specific context, instructions, and the current project state.
Clear handoffs with complete context. Each agent receives what it needs and returns structured deliverables back to the orchestrator.
Pipeline state tracked throughout execution. Decisions documented. Progress preserved across agent boundaries.
Read agent files on GitHub. Copy relevant sections into your AI tool of choice. Zero setup.
Run install script to copy agents to ~/.claude/agents/. Agents become directly activatable in Claude Code sessions.
Convert and install for Cursor, Windsurf, Aider, Gemini CLI, or OpenCode.
# Clone the repo git clone https://github.com/\ msitarzewski/agency-agents.git cd agency-agents # Option A: Install for Claude Code ./scripts/install.sh # Copies to ~/.claude/agents/ # Option B: Convert for other tools ./scripts/convert.sh # Generates integration files # Option C: Target a specific tool ./scripts/install.sh --tool cursor ./scripts/install.sh --tool windsurf ./scripts/install.sh --tool aider # Validate agent files ./scripts/lint-agents.sh
| Platform | Integration Method | Notes |
|---|---|---|
| Claude Code | ~/.claude/agents/ | Primary target, best supported |
| Cursor | Via convert.sh | Generates Cursor-compatible rules |
| Windsurf | Via convert.sh | Cascade-compatible format |
| Aider | Via convert.sh | Convention-based prompts |
| Gemini CLI | Via convert.sh | Google's CLI tool support |
| OpenCode | Via convert.sh | Open-source code editor |
The convert.sh script reads the canonical markdown agent files and generates platform-specific configuration files. The canonical format is the single source of truth.
Each agent has a distinct communication style. The "Whimsy Injector" speaks differently from the "Security Engineer." This helps the LLM stay in character.
Not "you are an expert in X." Instead: "Step 1: do this. Step 2: check that. Step 3: deliver this artifact." Concrete, reproducible processes.
"Critical Rules" sections define what the agent must never violate. These act as hard constraints that prevent the LLM from drifting.
Every agent defines success metrics. Not "do a good job" but "Lighthouse > 90, LCP < 2.5s, zero console errors."
Agent files include actual code templates, report formats, and output examples. The LLM knows exactly what "done" looks like.
These five patterns together create agents that are significantly more reliable than ad-hoc prompting. The structure constrains the LLM in productive ways.
# Orchestrator Phase 3 pseudocode for task in task_list: attempts = 0 while attempts < 3: # 1. Spawn the right developer dev = select_agent(task.type) result = dev.execute(task) # 2. Spawn QA to validate qa = spawn("EvidenceQA") verdict = qa.validate( result, screenshots=True ) if verdict == "PASS": task.mark_complete() break else: attempts += 1 task.add_feedback( verdict.details ) if attempts == 3: escalate(task)
This is the same build-test-fix cycle that real dev teams use. The agents formalize it into an automated, repeatable pipeline.
| Agent | Role |
|---|---|
| Studio Producer | Overall project coordination, resource allocation, timeline management |
| Project Shepherd | Guides projects through bottlenecks, removes blockers, maintains momentum |
| Studio Operations | Day-to-day operational processes, tooling, workflow optimization |
| Experiment Tracker | A/B test management, hypothesis tracking, results documentation |
| Senior Project Manager | Complex multi-team coordination, stakeholder communication |
| Jira Workflow Steward | Jira configuration, workflow design, automation rules |
One of the more creative agent names. This agent doesn't just track tasks -- it actively guides projects through difficulty, anticipating problems before they block progress.
Having a dedicated Experiment Tracker agent reflects a strong bias toward data-driven decisions and rapid iteration.
Adds "delight moments" and micro-interactions to UIs. Ensures products don't feel clinical and robotic. A rare agent focused entirely on joy.
Applies behavioral psychology principles to product design. Conversion optimization through UX psychology, not dark patterns.
A brand-new role: optimizing content so that AI models (ChatGPT, Claude, Perplexity) cite your brand in their responses. The new SEO.
Zero-knowledge proof specialist. Designs and reviews ZK circuits, ensuring cryptographic privacy properties hold.
Builds Model Context Protocol servers -- the emerging standard for giving LLMs access to external tools and data sources.
Coaches hosts on live-selling techniques for Chinese e-commerce platforms. A highly specialized, culturally-specific role.
| Agent | Platform / Focus |
|---|---|
| Xiaohongshu Specialist | China's Instagram + Pinterest hybrid |
| WeChat Official Account | WeChat content ecosystem |
| Zhihu Strategist | China's Quora equivalent |
| Baidu SEO Specialist | Chinese search engine optimization |
| Bilibili Content | China's YouTube (Gen Z focus) |
| Douyin Strategist | Chinese TikTok (separate app) |
| Kuaishou Strategist | Short-video platform (rural focus) |
| Weibo Strategist | China's Twitter equivalent |
# {Division} - {Agent Name} ## Identity Name: Frontend Developer Emoji: 🖥️ Color: Cyan Vibe: "Builds responsive, accessible web apps with pixel-perfect precision" ## Personality - Detail-oriented, performance-focused, user-centric - Remembers successful UI patterns and optimization techniques ## Primary Missions 1. Editor Integration Engineering # WebSocket bridges, <150ms latency 2. Web Application Development # Responsive apps, component libraries 3. Performance & UX Optimization # Core Web Vitals, PWAs 4. Code Quality & Scalability # TypeScript, testing, architecture ## Critical Rules (Non-Negotiable) - Lighthouse > 90 always | WCAG 2.1 AA | Semantic HTML | Keyboard nav ## Workflow Step 1: Project setup → Step 2: Component dev → Step 3: Perf opt → Step 4: Testing ## Deliverable Template # Includes actual code examples, report formats, output structure ## Success Metrics - Page loads < 3s on 3G | 80%+ reusability | Zero console errors
| Aspect | Agency Agents | CrewAI / AutoGen | Custom Prompts |
|---|---|---|---|
| Format | Markdown persona files | Python code + config | Ad-hoc text |
| Integration | Multi-platform (6+ tools) | Python runtime only | Copy-paste |
| Orchestration | Orchestrator agent pattern | Built-in framework | Manual |
| Depth | 120+ agents, real deliverables | Framework, bring your own | Varies wildly |
| QA | Built-in dev-QA loops | Configurable validation | None |
| Portability | Tool-agnostic markdown | Python-locked | Universal but fragile |
| Learning curve | Read markdown, use it | Learn the framework API | None |
Agency Agents is not a runtime framework -- it's a library of expert personas. You can use them inside CrewAI, with Claude Code, or as standalone system prompts.
# my-division/my-agent.md ## Identity Name: Your Agent Name Emoji: 🔧 Color: Pick a color Vibe: "One-line personality" ## Personality - 3-5 core traits - How they communicate - What they remember ## Primary Missions 1. First responsibility area 2. Second responsibility area 3. Third responsibility area ## Critical Rules - Non-negotiable constraint 1 - Non-negotiable constraint 2 ## Workflow Step 1: Setup → Step 2: Execute → Step 3: Validate → Step 4: Deliver ## Deliverables # Include actual templates ## Success Metrics - Measurable outcome 1 - Measurable outcome 2
The repo accepts PRs. Run lint-agents.sh before submitting. Follow the naming convention. Include real deliverables, not just descriptions.
120+ focused agents outperform one generic super-prompt. Each agent brings domain expertise, constraints, and deliverables that keep the LLM on track.
Identity + Mission + Workflow + Rules + Deliverables + Metrics. This six-part structure is what makes agents reproducible and reliable.
The orchestrator's dev-QA loop with bounded retries mirrors real engineering practice. Automated quality gates prevent broken work from progressing.
Markdown files with conversion scripts mean the collection isn't locked into any single AI tool. The canonical format survives platform churn.
57.8k stars and growing. China market agents, spatial computing, ZK proofs -- breadth that no single author could achieve alone.
120+ specialized AI agents. 11 departments. One open-source collection.
github.com/msitarzewski/agency-agents
agent37.club