🚀 FRAIM: Framework for Rigor-based AI Management

"FRAIM is a step towards the future of how we work." - Transforming ideas into production code by converting you into an AI manager orchestrating multiple agents with enterprise-grade discipline.

🚀 The Problem with AI Coding Today

Current "vibe coding" frameworks are great at getting from idea to prototype. They fail spectacularly at going from prototype to production or evolving complex codebases.

AI agents are like brilliant but inexperienced developers. They need:
• Clear guardrails to prevent costly mistakes
• Structured workflows to avoid chaos
• Evidence-based validation (not "looks good" claims)
• Learning systems to improve over time
• Balance between determinism and creativity

🎯 Introducing FRAIM: Framework for Rigor-based AI Management

FRAIM transforms you from a solo developer into an AI manager orchestrating multiple agents with enterprise-grade discipline.

The Transformation:
❌ Before: "It's working now. The login button exists."
✅ After: "Implementation complete. 12/12 tests pass, API endpoint validated, UI screenshots provided."

Real Results:
• Dramatic reduction in AI-generated code that needs rework
• Faster delivery through structured workflows
• Higher test coverage through mandatory evidence collection
• Zero agent conflicts through phase-based coordination

The RIGOR Methodology:
R - Reviews: Structured feedback with evidence
I - Isolation: Agents don't interfere unless coordinated
G - GitOps: Git as single source of truth
O - Observability: Complete visibility into AI activities
R - Retrospectives: Continuous learning from experience

🤖 Works with any AI agent (Cursor, Claude, Windsurf) - no vendor lock-in.

The bottom line: FRAIM isn't just about using AI—it's about managing AI teams with the same discipline you'd apply to human developers.

$3

| Human Development | AI Agent Development | FRAIM Solution |
|----------------------|-------------------------|-------------------|
| Code Reviews | Random quality checks | Structured review workflows with evidence requirements |
| Testing Standards | "Looks good" claims | Mandatory test evidence with failure reproduction |
| Team Coordination | Agent conflicts and overlaps | Phase-based isolation with clear handoffs |
| Learning Culture | Repeated mistakes | Retrospective-driven improvement system |
| Process Discipline | Ad-hoc approaches | Proven workflows and deterministic scripts from real projects |
| Quality Gates | Unreliable outcomes | Deterministic validation with rollback capabilities |

🔥 The Problems FRAIM Solves

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- "Looks Good" Syndrome: Agents claim success without evidence
- Quality Lottery: Inconsistent code quality and reliability
- Agent Chaos: Multiple agents stepping on each other's work
- No Learning: Repeated mistakes without improvement
- Ad-hoc Processes: Every project reinvents the wheel
- False Confidence: Broken code marked as "working"
- Hanging Agents: Commands that hang requiring human intervention
- Lost Output: No visibility into long-running task progress

$3

#### 🛡️ Agent Integrity & Test Ethics (.ai-agents/rules/integrity-and-test-ethics.md)
Problem: Agents claim "tests pass" when they actually fail
Solution: Mandatory evidence collection, test immutability rules, and accountability frameworks
``

bash

Before FRAIM: "Tests look good!"

After FRAIM: "Here's the test output proving all 47 tests pass"





#### 🧪 Comprehensive Testing Guidelines (

.ai-agents/rules/agent-testing-guidelines.md

)

Problem: Superficial testing that misses real issues

Solution: Multi-layer validation (database, API, UI, integration) with mandatory evidence

bash

Before: Mock everything, hope it works

After: Test real systems, prove it works, show evidence





#### 🗣️ Clear Communication Standards (

.ai-agents/rules/communication.md

)

Problem: Vague progress reports and unclear accountability

Solution: Structured progress updates with concrete evidence and absolute accountability

bash

Before: "Working on it, almost done"

After: "Fixed API timeout, tests passing, evidence attached, ready for review"





#### 🏗️ Architectural Discipline (

.ai-agents/rules/architecture.md

)

Problem: Agents create architectural chaos and technical debt

Solution: Clean separation of concerns, type safety, and testability patterns

bash

Before: Spaghetti code with mixed responsibilities

After: Clean layers with proper boundaries and validation





#### 🎯 Spike-First Development (

.ai-agents/rules/spike-first-development.md

)

Problem: Agents build complex solutions without validating assumptions

Solution: 5-15 minute proof-of-concepts before major implementation

bash

Before: Build 3-week solution, discover it doesn't work

After: 10-minute spike, validate approach, then build confidently





#### 🔄 Continuous Learning System (

.ai-agents/rules/continuous-learning.md

)

Problem: Same mistakes repeated across projects

Solution: Retrospective-driven knowledge capture and pattern recognition

bash

Before: Every agent learns the same lessons from scratch

After: Knowledge accumulates, patterns emerge, quality improves





#### 🧹 Simplicity Discipline (

.ai-agents/rules/simplicity.md

)

Problem: Over-engineered solutions that are hard to maintain

Solution: "Keep it simple" principles with complexity budgets

bash

Before: 500-line solution to a 10-line problem

After: Minimal, focused solution that actually works





#### 🔧 Git Safety & Timeout Management (

.ai-agents/rules/git-safe-commands.md

)

Problem: Agents hang on interactive Git commands and long-running tasks, requiring human intervention

Solution: Safe Git commands and timeout scripts with output visibility

bash

Before: Agent hangs on "git log" (opens pager) or tests run forever

After: Non-interactive commands with timeouts and log files for visibility

Example: exec-with-timeout.ts runs tests with timeout and saves output to files





#### 🔄 Merge Requirements & Branch Safety (

.ai-agents/rules/merge-requirements.md

)

Problem: Agents accidentally overwrite master branch or create merge conflicts

Solution: Mandatory rebase workflows with conflict resolution patterns

bash

Before: Force pushes that destroy other work

After: Rebase-on-master with force-with-lease for safety





#### 🐛 Systematic Debugging Patterns (

.ai-agents/rules/successful-debugging-patterns.md

)

Problem: Agents struggle with complex debugging and repeat the same mistakes

Solution: Structured debugging methodology with evidence collection and pattern recognition

bash

Before: Random debugging attempts, no learning

After: Systematic approach with documented patterns and regression tests





#### 📋 Package Scripts & Output Visibility (

.ai-agents/scripts/ and sample_package.json

)

Problem: Long-running tasks hang agents and provide no visibility into progress

Solution: Background execution with log files and timeout management

bash

Before: "npm test" hangs agent, no output visibility

After: "npm test" runs in background, saves to test.log, agent can observe progress

Example: exec-with-timeout.ts prevents hangs and provides output visibility





🚀 Proven Benefits from Real Projects



- Dramatic reduction in AI-generated code that needs rework through evidence-based validation

- Faster delivery through structured workflows and clear handoffs

- Higher test coverage through mandatory testing guidelines and evidence collection

- Zero agent conflicts through phase-based isolation and coordination

- Complete accountability - agents fix their own mistakes with evidence



🎬 The FRAIM Experience: From Chaos to Clarity



$3

bash

You: "Add user authentication to the app"

Agent: "I'll add login functionality"

# 

10 minutes later...

Agent: "I've designed the UX to be modern and beautiful. What do you think?"

You: "It's way too complex and does not work with the rest of the product. Just make it simple"

Agent: "You are right. I've made it too complex, let me simplify it right now"

You: "I've told you this 10 times already!!!! "

# 

1 hour later...

Agent: "Implementation complete. Solution looks good."

You: "I get an error on the first screen."

Agent: "What error do you see? Can you write it out for me or give me a screenshot?"

You: "C'mon, do this yourself."

# 

2 hours later...

Agent: 

You: "You've made no progress"

Agent: "You are right. I keep getting stuck. Could you test the implementation for me?"

# 

3 hours later...

Agent: "Ok I'm all done, test cases are passing"

You: "Wait, what... you changed existing tests to make them pass ... everything is broken!!"

$3

bash

You: "Add user authentication to the app"

Agent: "Starting design phase. Creating RFC with UX mockups, validation rules, and API specs"

# 

10 minutes later...

Agent: "Design complete. UX mockups created, validation rules defined, API endpoints specified. 

        Evidence: 3 mockup images, validation requirements document, API spec ready for review"

You: "Looks good, proceed to implementation"

# 

1 hour later...

Agent: "Implementation complete. Code written, tests created, all tests passing.

        Evidence: Test output showing existing 120/120 tests pass, new 12/12 tests pass, API endpoint tested with curl,

        UI tested with screenshots showing login form and error states"

During PR review...

Reviewer: "Password validation is too strict"

Agent: "Fixed password validation rules, updated tests, evidence provided.

        Evidence: New test output showing updated validation, UI screenshots with new rules"

# 

Result: In best case, hours of frustration, rework saved. In worst case, reputation saved from deploying broken code to production.





🏗️ Enterprise-Grade Framework Structure



FRAIM/

├── 🧠 .ai-agents/rules/          # Guardrails - The "HR Handbook" for AI agents

│   ├── integrity-and-test-ethics.md    # Prevents "fake it till you make it"

│   ├── agent-testing-guidelines.md     # Ensures real validation, not claims

│   ├── communication.md                # Clear accountability and progress

│   ├── architecture.md                 # Prevents technical debt

│   ├── spike-first-development.md      # Validates before building

│   ├── continuous-learning.md          # Learns from mistakes

│   ├── simplicity.md                   # Prevents over-engineering

│   ├── git-safe-commands.md            # Prevents agent hangs on Git commands

│   ├── merge-requirements.md           # Safe branch management and rebasing

│   ├── successful-debugging-patterns.md # Systematic debugging approach

│   ├── local-development.md            # Local development best practices

│   ├── pr-workflow-completeness.md     # PR workflow validation

│   └── software-development-lifecycle.md # Full SDLC integration

│

├── 🔄 .ai-agents/workflows/      # Process - The "Project Management Office"

│   ├── spec.md                   # Specification and requirements phase

│   ├── design.md                 # Structured design phase

│   ├── implement.md              # Implementation with validation

│   ├── test.md                   # Comprehensive testing

│   ├── resolve.md                # Issue resolution workflow

│   └── retrospect.md             # Learning and improvement

│

├── 📋 .ai-agents/templates/      # Human Interfaces - The "Standard Operating Procedures"

│   ├── evidence/                 # Proof of work templates

│   ├── retrospective/            # Learning capture templates

│   └── specs/                    # Specification templates

│

└── 🛠️ Scripts & Automation      # Determinism - The "DevOps Pipeline"

    ├── prep-issue.sh             # Issue preparation

    ├── exec-with-timeout.ts      # Command execution with timeout & output visibility

    └── cleanup-branch.ts         # Branch management





🚀 Get Started in 60 Seconds



$3



Shell Requirements:

- Windows: Must use Git Bash (install from https://git-scm.com/download/win)

- macOS/Linux: Default terminal works fine



Why Git Bash on Windows? All FRAIM scripts use Unix-style paths and Bash commands. Git Bash ensures consistent behavior across platforms.



$3

bash

npm install -g fraim-framework

fraim setup  # Complete setup with IDE configuration





$3



After initial setup, you can use these commands:

bash

Add FRAIM to additional IDEs (after initial setup)

fraim add-ide --ide claude          # Configure specific IDE

fraim add-ide --ide antigravity     # Configure Gemini Antigravity

fraim add-ide --all                 # Configure all detected IDEs

fraim add-ide --list                # List supported IDEs



Project initialization

fraim init-project                  # Initialize FRAIM in current project



Testing and validation

fraim test-mcp                      # Test MCP server connections

fraim doctor                        # Diagnose configuration issues



Sync and maintenance

fraim sync                          # Sync latest workflows and rules





💡 Pro Tip: Use

fraim add-ide

 when you install a new IDE after initial setup. It reuses your existing FRAIM and GitHub keys, making it much faster than running full setup again.



$3



After installation, customize these files for your specific project:



#### 1. Architecture Rules (

.ai-agents/rules/architecture.md

)

- Purpose: Define your system's architectural patterns and boundaries

- Customize: Replace generic examples with your specific architecture patterns

- Example: Add your database patterns, API structures, service boundaries



#### 2. Branch Cleanup Script (

.ai-agents/scripts/cleanup-branch.ts

)

- Purpose: Project-specific cleanup when branches are deleted

- Customize: Add your cleanup logic in the

insertYourCodeHere()

 method

- Example: Database cleanup, cache invalidation, temporary file removal



#### 3. Package Scripts (

sample_package.json → package.json

)

- Purpose: Testing and build scripts for your project

- Adopt: Copy relevant scripts from

sample_package.json into your main package.json



- Customize: Update test commands, build processes, and framework-specific commands

- Example: Replace

baml-cli generate

 with your framework's generate command



#### 4. Test Utilities (

test-utils.ts

 → your project)

- Purpose: Generic test framework with tagging system

- Adopt: Copy

test-utils.ts

 into your project and import in your test files

- Customize: Extend

BaseTestCase

 interface for your specific test needs

- Example: Create domain-specific test cases using the tagging system



#### 5. Test Structure (

examples/simple-webapp/example-test.ts

)

- Purpose: Example test cases with proper tagging

- Adopt: Use as a template for your project's test structure

- Customize: Replace with your project's test cases and domain-specific tags

- Example: Add your specific test scenarios and validation patterns



🎯 The Transformation: From Developer to AI Manager



$3

bash

npx fraim-framework init

✅ Repository configured with AI management rules

✅ Agents assigned to specific roles (Design, Implementation, Testing)

✅ Workflows and templates installed

$3

bash

gh issue create --title "Add user authentication" --label "ai-agent:cursor"

✅ Git issue is centralized coordination point

✅ Agent assignment handled

✅ Ready for work

$3

bash

.ai-agents/scripts/prep-issue.sh #issue_number

✅ Local clone and new branch created

✅ Coding agent initiated

✅ Tell coding agent which phase it should proceed with





🌟 Why FRAIM is the Future



$3

Every rule, workflow, and pattern has been tested in real projects. This isn't theoretical—it's battle-tested.



$3

The same rigor you'd apply to managing human developers, applied to AI agents.



$3

Built-in learning systems that make your AI agents better over time.



$3

Full visibility into what each agent is doing, with evidence-based validation.



$3

Works with any AI agent (Cursor, Claude, Windsurf, future agents).





🚀 Ready to Transform Your Development?



$3

bash

Watch the magic happen

gh issue create --title "Add API rate limiting" --label "phase:design"

→ Agent: "RFC created, architecture validated, ready for implementation"



gh issue edit 123 --remove-label "phase:design" --add-label "phase:impl"

→ Agent: "Implementation complete, tests passing, evidence provided"



gh issue edit 123 --remove-label "phase:impl" --add-label "phase:tests"

→ Agent: "Performance validated, security checked, ready for production"



Result: Production-ready feature in 2 hours instead of 2 days

$3

- 🌟 GitHub Repository - Star us to follow development
- 🐛 Issue Tracker - Report bugs or request features

---

🎯 The Bottom Line

FRAIM isn't just about using AI—it's about managing AI teams with the same discipline you'd apply to human teams.

Stop fighting with AI agents. Start orchestrating them.

This is the future of how we work.

---

🚀 Ready to become an AI manager? Start with FRAIM today.

![npm version](https://www.npmjs.com/package/fraim-framework)
![GitHub stars](https://github.com/mathursrus/FRAIM/stargazers)
![License: MIT](https://opensource.org/licenses/MIT)

🚀 FRAIM: Framework for Rigor-based AI Management

$3

🔥 The Problems FRAIM Solves

$3

bash

Before FRAIM: "Tests look good!"

After FRAIM: "Here's the test output proving all 47 tests pass"





#### 🧪 Comprehensive Testing Guidelines (

.ai-agents/rules/agent-testing-guidelines.md

)

Problem: Superficial testing that misses real issues

Solution: Multi-layer validation (database, API, UI, integration) with mandatory evidence

bash

Before: Mock everything, hope it works

After: Test real systems, prove it works, show evidence





#### 🗣️ Clear Communication Standards (

.ai-agents/rules/communication.md

)

Problem: Vague progress reports and unclear accountability

Solution: Structured progress updates with concrete evidence and absolute accountability

bash

Before: "Working on it, almost done"

After: "Fixed API timeout, tests passing, evidence attached, ready for review"





#### 🏗️ Architectural Discipline (

.ai-agents/rules/architecture.md

)

Problem: Agents create architectural chaos and technical debt

Solution: Clean separation of concerns, type safety, and testability patterns

bash

Before: Spaghetti code with mixed responsibilities

After: Clean layers with proper boundaries and validation





#### 🎯 Spike-First Development (

.ai-agents/rules/spike-first-development.md

)

Problem: Agents build complex solutions without validating assumptions

Solution: 5-15 minute proof-of-concepts before major implementation

bash

Before: Build 3-week solution, discover it doesn't work

After: 10-minute spike, validate approach, then build confidently





#### 🔄 Continuous Learning System (

.ai-agents/rules/continuous-learning.md

)

Problem: Same mistakes repeated across projects

Solution: Retrospective-driven knowledge capture and pattern recognition

bash

Before: Every agent learns the same lessons from scratch

After: Knowledge accumulates, patterns emerge, quality improves





#### 🧹 Simplicity Discipline (

.ai-agents/rules/simplicity.md

)

Problem: Over-engineered solutions that are hard to maintain

Solution: "Keep it simple" principles with complexity budgets

bash

Before: 500-line solution to a 10-line problem

After: Minimal, focused solution that actually works





#### 🔧 Git Safety & Timeout Management (

.ai-agents/rules/git-safe-commands.md

)

Problem: Agents hang on interactive Git commands and long-running tasks, requiring human intervention

Solution: Safe Git commands and timeout scripts with output visibility

bash

Before: Agent hangs on "git log" (opens pager) or tests run forever

After: Non-interactive commands with timeouts and log files for visibility

Example: exec-with-timeout.ts runs tests with timeout and saves output to files





#### 🔄 Merge Requirements & Branch Safety (

.ai-agents/rules/merge-requirements.md

)

Problem: Agents accidentally overwrite master branch or create merge conflicts

Solution: Mandatory rebase workflows with conflict resolution patterns

bash

Before: Force pushes that destroy other work

After: Rebase-on-master with force-with-lease for safety





#### 🐛 Systematic Debugging Patterns (

.ai-agents/rules/successful-debugging-patterns.md

)

Problem: Agents struggle with complex debugging and repeat the same mistakes

Solution: Structured debugging methodology with evidence collection and pattern recognition

bash

Before: Random debugging attempts, no learning

After: Systematic approach with documented patterns and regression tests





#### 📋 Package Scripts & Output Visibility (

.ai-agents/scripts/ and sample_package.json

)

Problem: Long-running tasks hang agents and provide no visibility into progress

Solution: Background execution with log files and timeout management

bash

Before: "npm test" hangs agent, no output visibility

After: "npm test" runs in background, saves to test.log, agent can observe progress

Example: exec-with-timeout.ts prevents hangs and provides output visibility





🚀 Proven Benefits from Real Projects



- Dramatic reduction in AI-generated code that needs rework through evidence-based validation

- Faster delivery through structured workflows and clear handoffs

- Higher test coverage through mandatory testing guidelines and evidence collection

- Zero agent conflicts through phase-based isolation and coordination

- Complete accountability - agents fix their own mistakes with evidence



🎬 The FRAIM Experience: From Chaos to Clarity



$3

bash

You: "Add user authentication to the app"

Agent: "I'll add login functionality"

# 

10 minutes later...

Agent: "I've designed the UX to be modern and beautiful. What do you think?"

You: "It's way too complex and does not work with the rest of the product. Just make it simple"

Agent: "You are right. I've made it too complex, let me simplify it right now"

You: "I've told you this 10 times already!!!! "

# 

1 hour later...

Agent: "Implementation complete. Solution looks good."

You: "I get an error on the first screen."

Agent: "What error do you see? Can you write it out for me or give me a screenshot?"

You: "C'mon, do this yourself."

# 

2 hours later...

Agent: 

You: "You've made no progress"

Agent: "You are right. I keep getting stuck. Could you test the implementation for me?"

# 

3 hours later...

Agent: "Ok I'm all done, test cases are passing"

You: "Wait, what... you changed existing tests to make them pass ... everything is broken!!"

$3

bash

You: "Add user authentication to the app"

Agent: "Starting design phase. Creating RFC with UX mockups, validation rules, and API specs"

# 

10 minutes later...

Agent: "Design complete. UX mockups created, validation rules defined, API endpoints specified. 

        Evidence: 3 mockup images, validation requirements document, API spec ready for review"

You: "Looks good, proceed to implementation"

# 

1 hour later...

Agent: "Implementation complete. Code written, tests created, all tests passing.

        Evidence: Test output showing existing 120/120 tests pass, new 12/12 tests pass, API endpoint tested with curl,

        UI tested with screenshots showing login form and error states"

During PR review...

Reviewer: "Password validation is too strict"

Agent: "Fixed password validation rules, updated tests, evidence provided.

        Evidence: New test output showing updated validation, UI screenshots with new rules"

# 

Result: In best case, hours of frustration, rework saved. In worst case, reputation saved from deploying broken code to production.





🏗️ Enterprise-Grade Framework Structure



FRAIM/

├── 🧠 .ai-agents/rules/          # Guardrails - The "HR Handbook" for AI agents

│   ├── integrity-and-test-ethics.md    # Prevents "fake it till you make it"

│   ├── agent-testing-guidelines.md     # Ensures real validation, not claims

│   ├── communication.md                # Clear accountability and progress

│   ├── architecture.md                 # Prevents technical debt

│   ├── spike-first-development.md      # Validates before building

│   ├── continuous-learning.md          # Learns from mistakes

│   ├── simplicity.md                   # Prevents over-engineering

│   ├── git-safe-commands.md            # Prevents agent hangs on Git commands

│   ├── merge-requirements.md           # Safe branch management and rebasing

│   ├── successful-debugging-patterns.md # Systematic debugging approach

│   ├── local-development.md            # Local development best practices

│   ├── pr-workflow-completeness.md     # PR workflow validation

│   └── software-development-lifecycle.md # Full SDLC integration

│

├── 🔄 .ai-agents/workflows/      # Process - The "Project Management Office"

│   ├── spec.md                   # Specification and requirements phase

│   ├── design.md                 # Structured design phase

│   ├── implement.md              # Implementation with validation

│   ├── test.md                   # Comprehensive testing

│   ├── resolve.md                # Issue resolution workflow

│   └── retrospect.md             # Learning and improvement

│

├── 📋 .ai-agents/templates/      # Human Interfaces - The "Standard Operating Procedures"

│   ├── evidence/                 # Proof of work templates

│   ├── retrospective/            # Learning capture templates

│   └── specs/                    # Specification templates

│

└── 🛠️ Scripts & Automation      # Determinism - The "DevOps Pipeline"

    ├── prep-issue.sh             # Issue preparation

    ├── exec-with-timeout.ts      # Command execution with timeout & output visibility

    └── cleanup-branch.ts         # Branch management





🚀 Get Started in 60 Seconds



$3



Shell Requirements:

- Windows: Must use Git Bash (install from https://git-scm.com/download/win)

- macOS/Linux: Default terminal works fine



Why Git Bash on Windows? All FRAIM scripts use Unix-style paths and Bash commands. Git Bash ensures consistent behavior across platforms.



$3

bash

npm install -g fraim-framework

fraim setup  # Complete setup with IDE configuration





$3



After initial setup, you can use these commands:

bash

Add FRAIM to additional IDEs (after initial setup)

fraim add-ide --ide claude          # Configure specific IDE

fraim add-ide --ide antigravity     # Configure Gemini Antigravity

fraim add-ide --all                 # Configure all detected IDEs

fraim add-ide --list                # List supported IDEs



Project initialization

fraim init-project                  # Initialize FRAIM in current project



Testing and validation

fraim test-mcp                      # Test MCP server connections

fraim doctor                        # Diagnose configuration issues



Sync and maintenance

fraim sync                          # Sync latest workflows and rules





💡 Pro Tip: Use

fraim add-ide

 when you install a new IDE after initial setup. It reuses your existing FRAIM and GitHub keys, making it much faster than running full setup again.



$3



After installation, customize these files for your specific project:



#### 1. Architecture Rules (

.ai-agents/rules/architecture.md

)

- Purpose: Define your system's architectural patterns and boundaries

- Customize: Replace generic examples with your specific architecture patterns

- Example: Add your database patterns, API structures, service boundaries



#### 2. Branch Cleanup Script (

.ai-agents/scripts/cleanup-branch.ts

)

- Purpose: Project-specific cleanup when branches are deleted

- Customize: Add your cleanup logic in the

insertYourCodeHere()

 method

- Example: Database cleanup, cache invalidation, temporary file removal



#### 3. Package Scripts (

sample_package.json → package.json

)

- Purpose: Testing and build scripts for your project

- Adopt: Copy relevant scripts from

sample_package.json into your main package.json



- Customize: Update test commands, build processes, and framework-specific commands

- Example: Replace

baml-cli generate

 with your framework's generate command



#### 4. Test Utilities (

test-utils.ts

 → your project)

- Purpose: Generic test framework with tagging system

- Adopt: Copy

test-utils.ts

 into your project and import in your test files

- Customize: Extend

BaseTestCase

 interface for your specific test needs

- Example: Create domain-specific test cases using the tagging system



#### 5. Test Structure (

examples/simple-webapp/example-test.ts

)

- Purpose: Example test cases with proper tagging

- Adopt: Use as a template for your project's test structure

- Customize: Replace with your project's test cases and domain-specific tags

- Example: Add your specific test scenarios and validation patterns



🎯 The Transformation: From Developer to AI Manager



$3

bash

npx fraim-framework init

✅ Repository configured with AI management rules

✅ Agents assigned to specific roles (Design, Implementation, Testing)

✅ Workflows and templates installed

$3

bash

gh issue create --title "Add user authentication" --label "ai-agent:cursor"

✅ Git issue is centralized coordination point

✅ Agent assignment handled

✅ Ready for work

$3

bash

.ai-agents/scripts/prep-issue.sh #issue_number

✅ Local clone and new branch created

✅ Coding agent initiated

✅ Tell coding agent which phase it should proceed with





🌟 Why FRAIM is the Future



$3

Every rule, workflow, and pattern has been tested in real projects. This isn't theoretical—it's battle-tested.



$3

The same rigor you'd apply to managing human developers, applied to AI agents.



$3

Built-in learning systems that make your AI agents better over time.



$3

Full visibility into what each agent is doing, with evidence-based validation.



$3

Works with any AI agent (Cursor, Claude, Windsurf, future agents).





🚀 Ready to Transform Your Development?



$3

bash

Watch the magic happen

gh issue create --title "Add API rate limiting" --label "phase:design"

→ Agent: "RFC created, architecture validated, ready for implementation"



gh issue edit 123 --remove-label "phase:design" --add-label "phase:impl"

→ Agent: "Implementation complete, tests passing, evidence provided"



gh issue edit 123 --remove-label "phase:impl" --add-label "phase:tests"

→ Agent: "Performance validated, security checked, ready for production"



Result: Production-ready feature in 2 hours instead of 2 days

$3

- 🌟 GitHub Repository - Star us to follow development
- 🐛 Issue Tracker - Report bugs or request features

---