Agency Agents

A Complete AI Agency with 120+ Specialized Expert Agents

msitarzewski/agency-agents -- 57.8k stars -- MIT License

agent37.club

What Is Agency Agents?

An open-source collection of specialized AI agent personas

  • Each agent is a detailed markdown personality file with identity, mission, workflow, and deliverables
  • Organized into 11 departments mirroring a real company structure
  • Not generic prompts -- agents include real code templates, success metrics, and non-negotiable rules
  • Born from Reddit discussions and months of community iteration

Core Philosophy

Specialization over generalization. Each agent is a domain expert with personality, processes, and proven deliverables -- not a Swiss Army knife.

Multi-Platform

Works with Claude Code, Cursor, Windsurf, Aider, Gemini CLI, and OpenCode via conversion scripts.

Repository Structure

One folder per department, one markdown file per agent

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

Naming Convention

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

Scripts

convert.sh generates integration files for each platform. install.sh copies them to the right location. lint-agents.sh validates agent file quality.

Anatomy of an Agent File

Each .md file is a structured persona with six core sections

SectionPurpose
IdentityName, emoji, color, vibe statement -- the agent's "character sheet"
MissionPrimary responsibilities and areas of expertise
WorkflowStep-by-step process for completing tasks
Critical RulesNon-negotiable constraints the agent must always follow
DeliverablesTemplates and examples of actual output
Success MetricsMeasurable 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%"

The Org Chart: 11 Departments

Engineering

23 agents -- frontend, backend, DevOps, security, AI, mobile, embedded, SRE

Design

8 agents -- UI, UX research, UX architecture, brand, visual storytelling

Marketing

26 agents -- SEO, social (every platform), content, growth, podcasts

Sales

8 agents -- outbound, discovery, deals, proposals, pipeline, coaching

Product

5 agents -- sprint planning, trends, feedback, behavioral nudges, PM

Project Mgmt

6 agents -- studio ops, project shepherd, experiment tracking, Jira

Testing

8 agents -- QA evidence, reality check, performance, API, accessibility

Support

6 agents -- customer support, analytics, finance, infra, legal, exec summary

Spatial Computing

6 agents -- XR interface, visionOS, Metal, immersive dev, cockpit UX

Specialized

22 agents -- orchestrator, blockchain, compliance, MCP builder, ZK, identity

Academic + Strategy

Research-focused and strategic planning agents

Total: 120+

All open-source under MIT license

Engineering Division (23 Agents)

The largest technical division -- covering the full stack and beyond

Frontend Developer
Backend Architect
Mobile App Builder
AI Engineer
DevOps Automator
Rapid Prototyper
Senior Developer
Security Engineer
Software Architect
SRE
Database Optimizer
Code Reviewer
Git Workflow Master
Technical Writer
Data Engineer
Embedded Firmware
Incident Response
Threat Detection
Solidity/Smart Contract
Autonomous Optimization
AI Data Remediation
WeChat Mini Program
Feishu Integration

Notable: includes China-specific agents (WeChat, Feishu) reflecting the global contributor base.

Case Study: Frontend Developer Agent

What a fully-specified agent looks like in practice

Four Mission Areas

  • Editor Integration -- WebSocket/RPC bridges, sub-150ms latency navigation
  • Web App Development -- Responsive apps, component libraries, design systems, API integration
  • Performance Optimization -- Core Web Vitals (LCP <2.5s, FID <100ms, CLS <0.1), code splitting, PWAs
  • Code Quality -- TypeScript, testing suites, error handling, maintainable architecture

Non-Negotiable Rules

# 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

Success Metrics

  • Page loads under 3s on 3G
  • 80%+ component reusability
  • Zero console errors in prod

Marketing Division (26 Agents)

The largest division -- covering every platform and strategy

Western Platforms

Growth Hacker
Content Creator
SEO Specialist
Twitter Engager
Instagram Curator
TikTok Strategist
Reddit Community Builder
LinkedIn Content Creator
Podcast Strategist
AI Citation Strategist

China Market Specialists

Xiaohongshu Specialist
WeChat Official Account
Zhihu Strategist
Baidu SEO Specialist
Bilibili Content Strategist
Douyin Strategist
Kuaishou Strategist
Weibo Strategist
Livestream Commerce Coach
Cross-Border E-Commerce

Design & Paid Media Divisions

Design (8 Agents)

AgentFocus
UI DesignerVisual interfaces and component design
UX ResearcherUser studies, personas, journey maps
UX ArchitectInformation architecture, wireframes
Brand GuardianBrand consistency and guidelines
Visual StorytellerData visualization and narratives
Whimsy InjectorDelight moments and micro-interactions
Image Prompt EngineerAI image generation prompts
Inclusive VisualsDiverse, accessible imagery

Paid Media (7 Agents)

AgentFocus
PPC Campaign StrategistSearch ads strategy
Search Query AnalystKeyword research and intent
Paid Media AuditorCampaign health checks
Tracking SpecialistConversion tracking, attribution
Ad Creative StrategistCopy, visuals, A/B tests
Programmatic BuyerDisplay and programmatic ads
Paid Social StrategistSocial ad campaigns

Sales & Product Divisions

Sales (8 Agents)

AgentFocus
Outbound StrategistCold outreach, sequences
Discovery CoachQualification, needs analysis
Deal StrategistNegotiation, closing
Sales EngineerTechnical demos, POCs
Proposal StrategistRFP responses, proposals
Pipeline AnalystForecasting, pipeline health
Account StrategistExpansion, retention
Sales CoachTraining, call reviews

Product (5 Agents)

AgentFocus
Sprint PrioritizerBacklog grooming, prioritization
Trend ResearcherMarket analysis, competitor intel
Feedback SynthesizerUser feedback to insights
Behavioral Nudge EngineUX psychology, conversion
Product ManagerRoadmap, stakeholder alignment

Full Sales Pipeline

These 8 sales agents cover the complete B2B sales cycle from cold outreach to account expansion.

Testing & Support Divisions

Testing (8 Agents)

  • Evidence Collector -- Screenshot-based QA validation
  • Reality Checker -- Comprehensive integration testing across the full app
  • Test Results Analyzer -- Interprets test output, identifies patterns
  • Performance Benchmarker -- Load testing, bottleneck identification
  • API Tester -- Endpoint validation, contract testing
  • Tool Evaluator -- Assesses third-party tools and libraries
  • Workflow Optimizer -- Process improvement and automation
  • Accessibility Auditor -- WCAG compliance validation

Support (6 Agents)

  • Support Responder -- Customer-facing ticket resolution
  • Analytics Reporter -- Dashboards, KPI tracking
  • Finance Tracker -- Budget monitoring, cost analysis
  • Infrastructure Maintainer -- Server health, uptime
  • Legal Compliance -- Policy checks, GDPR, SOC2
  • Executive Summary Generator -- C-suite reporting

Evidence-Based QA

The Evidence Collector takes actual screenshots and validates against specs -- not just running test suites.

Spatial Computing & Specialized

Spatial Computing (6 Agents)

  • XR Interface Architect -- Cross-platform spatial UI design
  • macOS Spatial/Metal Engineer -- Apple GPU and spatial frameworks
  • XR Immersive Developer -- VR/AR experience development
  • XR Cockpit Interaction -- Dashboard UX for spatial environments
  • visionOS Spatial Engineer -- Apple Vision Pro apps
  • Terminal Integration Specialist -- CLI tools for spatial workflows

Heavy Apple ecosystem focus -- reflects the growing importance of visionOS and Metal.

Specialized (22 Agents) -- Highlights

  • Agents Orchestrator -- The meta-agent that coordinates all others
  • MCP Builder -- Builds Model Context Protocol servers
  • Blockchain Security Auditor -- Smart contract security review
  • ZK Steward -- Zero-knowledge proof systems
  • Identity Graph Operator -- User identity resolution
  • Document Generator -- Automated report creation
  • Compliance Auditor -- Regulatory compliance
  • Healthcare Marketing Compliance -- HIPAA-aware marketing

The Agents Orchestrator

The conductor who runs the entire dev pipeline from spec to ship

Four Sequential Phases

1

Project Analysis

Verify spec exists. Spawn project-manager-senior to generate task list from requirements.

2

Technical Architecture

Spawn ArchitectUX for foundation -- CSS, docs, component structure.

3

Dev-QA Loop

For each task: spawn developer, build, spawn QA, validate. PASS or FAIL (max 3 retries).

4

Final Integration

Spawn testing-reality-checker for comprehensive end-to-end validation.

Quality Gates

# 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()

No Shortcuts

Every task requires QA before advancement. Evidence-based decisions using actual agent outputs.

Orchestrator Communication Patterns

How the conductor talks to the orchestra

Status Reporting

"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"

Agent Spawning

The orchestrator spawns specialist agents for each task, passing them specific context, instructions, and the current project state.

Handoff Protocol

Clear handoffs with complete context. Each agent receives what it needs and returns structured deliverables back to the orchestrator.

State Preservation

Pipeline state tracked throughout execution. Decisions documented. Progress preserved across agent boundaries.

Installation & Setup

Three approaches, from simple to full integration

Option 1: Browse & Copy

Read agent files on GitHub. Copy relevant sections into your AI tool of choice. Zero setup.

Option 2: Claude Code

Run install script to copy agents to ~/.claude/agents/. Agents become directly activatable in Claude Code sessions.

Option 3: Multi-Tool

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

Supported Platforms

One agent collection, many integration targets

PlatformIntegration MethodNotes
Claude Code~/.claude/agents/Primary target, best supported
CursorVia convert.shGenerates Cursor-compatible rules
WindsurfVia convert.shCascade-compatible format
AiderVia convert.shConvention-based prompts
Gemini CLIVia convert.shGoogle's CLI tool support
OpenCodeVia convert.shOpen-source code editor

How Conversion Works

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.

Agent Design Patterns

What makes these agents effective vs. generic prompts

Pattern 1: Personality-Driven

Each agent has a distinct communication style. The "Whimsy Injector" speaks differently from the "Security Engineer." This helps the LLM stay in character.

Pattern 2: Workflow-First

Not "you are an expert in X." Instead: "Step 1: do this. Step 2: check that. Step 3: deliver this artifact." Concrete, reproducible processes.

Pattern 3: Guardrails Built In

"Critical Rules" sections define what the agent must never violate. These act as hard constraints that prevent the LLM from drifting.

Pattern 4: Measurable Success

Every agent defines success metrics. Not "do a good job" but "Lighthouse > 90, LCP < 2.5s, zero console errors."

Pattern 5: Real Deliverables

Agent files include actual code templates, report formats, and output examples. The LLM knows exactly what "done" looks like.

Key Insight

These five patterns together create agents that are significantly more reliable than ad-hoc prompting. The structure constrains the LLM in productive ways.

The Dev-QA Loop in Detail

The core execution pattern that ensures quality

# 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)

Key Properties

  • Agent Selection -- Orchestrator picks the right specialist for each task type (frontend, backend, etc.)
  • Evidence-Based QA -- The Evidence Collector takes actual screenshots, not just assertion checks
  • Feedback Loops -- Failed tasks get specific feedback from QA, not just "try again"
  • Bounded Retries -- Max 3 attempts prevents infinite loops on genuinely hard problems
  • Escalation Path -- After 3 failures, the task is flagged but the pipeline continues

Why This Matters

This is the same build-test-fix cycle that real dev teams use. The agents formalize it into an automated, repeatable pipeline.

Project Management Division

Keeping the machine running smoothly

AgentRole
Studio ProducerOverall project coordination, resource allocation, timeline management
Project ShepherdGuides projects through bottlenecks, removes blockers, maintains momentum
Studio OperationsDay-to-day operational processes, tooling, workflow optimization
Experiment TrackerA/B test management, hypothesis tracking, results documentation
Senior Project ManagerComplex multi-team coordination, stakeholder communication
Jira Workflow StewardJira configuration, workflow design, automation rules

The "Project Shepherd"

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.

Experiment Culture

Having a dedicated Experiment Tracker agent reflects a strong bias toward data-driven decisions and rapid iteration.

Notable & Unusual Agents

Some of the more creative specialized roles

Whimsy Injector

Adds "delight moments" and micro-interactions to UIs. Ensures products don't feel clinical and robotic. A rare agent focused entirely on joy.

Behavioral Nudge Engine

Applies behavioral psychology principles to product design. Conversion optimization through UX psychology, not dark patterns.

AI Citation Strategist

A brand-new role: optimizing content so that AI models (ChatGPT, Claude, Perplexity) cite your brand in their responses. The new SEO.

ZK Steward

Zero-knowledge proof specialist. Designs and reviews ZK circuits, ensuring cryptographic privacy properties hold.

MCP Builder

Builds Model Context Protocol servers -- the emerging standard for giving LLMs access to external tools and data sources.

Livestream Commerce Coach

Coaches hosts on live-selling techniques for Chinese e-commerce platforms. A highly specialized, culturally-specific role.

China Market Coverage

Unusually deep China-specific agent roster

Marketing Agents (China)

AgentPlatform / Focus
Xiaohongshu SpecialistChina's Instagram + Pinterest hybrid
WeChat Official AccountWeChat content ecosystem
Zhihu StrategistChina's Quora equivalent
Baidu SEO SpecialistChinese search engine optimization
Bilibili ContentChina's YouTube (Gen Z focus)
Douyin StrategistChinese TikTok (separate app)
Kuaishou StrategistShort-video platform (rural focus)
Weibo StrategistChina's Twitter equivalent

Engineering Agents (China)

  • WeChat Mini Program Developer -- Builds apps within the WeChat super-app ecosystem
  • Feishu Integration Developer -- Integrates with ByteDance's enterprise collaboration tool (China's Slack)

Commerce Agents

  • China E-Commerce Operator -- Tmall, JD.com, Pinduoduo store management
  • Cross-Border E-Commerce -- International selling into China
  • Livestream Commerce Coach -- Live-selling on Douyin/Taobao Live
  • Private Domain Operator -- WeChat group and CRM strategies

Agent File Format: Deep Dive

The markdown structure that makes agents reproducible

# {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

How It Compares

Agency Agents vs. other agent frameworks

AspectAgency AgentsCrewAI / AutoGenCustom Prompts
FormatMarkdown persona filesPython code + configAd-hoc text
IntegrationMulti-platform (6+ tools)Python runtime onlyCopy-paste
OrchestrationOrchestrator agent patternBuilt-in frameworkManual
Depth120+ agents, real deliverablesFramework, bring your ownVaries wildly
QABuilt-in dev-QA loopsConfigurable validationNone
PortabilityTool-agnostic markdownPython-lockedUniversal but fragile
Learning curveRead markdown, use itLearn the framework APINone

Different Category

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.

Strengths & Limitations

Strengths

  • Incredible breadth -- 120+ agents covering nearly every business function
  • Battle-tested -- Born from Reddit discussions and real-world usage
  • Platform-agnostic -- Markdown files work with any AI tool
  • Quality over vibes -- Real code, real metrics, real workflows
  • Community-driven -- MIT license, active contributions
  • Global perspective -- Deep China market coverage is rare

Limitations

  • No runtime -- These are prompts, not executable code. You need an AI tool to run them.
  • Orchestration is conceptual -- The orchestrator agent describes a pattern, but doesn't enforce it in code.
  • Agent quality varies -- Community contributions mean some agents are more polished than others.
  • Context window pressure -- Loading a full agent file plus your task can consume significant context.
  • No memory -- Agents don't persist state between sessions unless the platform provides it.

Practical Tips for Using Agency Agents

How to get the most value from the collection

Do

  • Start with one agent -- Pick the role closest to your current task and use it for a week
  • Customize the persona -- Remove missions that don't apply. Add your tech stack to the rules.
  • Use the workflow section -- The step-by-step processes are often more valuable than the identity
  • Combine 2-3 agents max -- Orchestrate a frontend dev + QA agent for a focused dev loop
  • Check success metrics -- They're great checklists even without AI

Don't

  • Load all 120+ agents -- Your context window will explode and quality will plummet
  • Use them verbatim -- Adapt to your specific project, tech stack, and constraints
  • Expect autonomous orchestration -- The orchestrator pattern requires manual setup per platform
  • Skip the critical rules -- They're the most valuable part for preventing LLM drift
  • Ignore the linter -- Run lint-agents.sh if you modify or create agents

Building Your Own Agent

Follow the established pattern for consistency

# 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

Checklist

  • File named {division}-{role}.md
  • Identity section with emoji, color, and vibe
  • At least 2 primary missions
  • At least 2 non-negotiable critical rules
  • Step-by-step workflow (not just "be an expert")
  • At least one deliverable template with real code
  • Quantifiable success metrics

Contributing

The repo accepts PRs. Run lint-agents.sh before submitting. Follow the naming convention. Include real deliverables, not just descriptions.

Key Takeaways

1. Specialization Works

120+ focused agents outperform one generic super-prompt. Each agent brings domain expertise, constraints, and deliverables that keep the LLM on track.

2. Structure Beats Vibes

Identity + Mission + Workflow + Rules + Deliverables + Metrics. This six-part structure is what makes agents reproducible and reliable.

3. QA Loops Are Essential

The orchestrator's dev-QA loop with bounded retries mirrors real engineering practice. Automated quality gates prevent broken work from progressing.

4. Platform-Agnostic Is the Right Call

Markdown files with conversion scripts mean the collection isn't locked into any single AI tool. The canonical format survives platform churn.

5. Community Scales

57.8k stars and growing. China market agents, spatial computing, ZK proofs -- breadth that no single author could achieve alone.

Agency Agents

120+ specialized AI agents. 11 departments. One open-source collection.

"Specialization over generalization.
Deliverables over descriptions."

github.com/msitarzewski/agency-agents

agent37.club