APEX - Stop Your AI From Making The Same Mistakes Twice

> APEX gives AI assistants memory, learning, and pattern recognition for 40-55% faster development


``bash


See APEX in action - no installation required!


npx @benredmond/apex start
`

The Problem

Your AI coding assistant is powerful, but it:
- 🔄 Repeats the same mistakes
- 🤷 Doesn't learn from your codebase
- 📋 Lacks memory between sessions
- 🎯 Misses patterns that could save hours

The Solution

APEX transforms your AI assistant into an intelligent development partner that learns and improves:

`
Without APEX: AI suggests generic solution → Often wrong → You fix it → AI forgets
With APEX: AI recalls what worked → Applies proven patterns → Prevents past failures → Gets smarter
`

Why APEX?|

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1. Zero-Runtime Intelligence - No background processes, no performance impact
2. Pattern Evolution - Discovers, validates, and promotes patterns automatically
3. Failure Prevention - Learns from mistakes to prevent repetition

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> "After 50 tasks, APEX prevented every single MongoDB async/await error that used to waste 30 minutes each time. The pattern system is like having a senior developer's knowledge built into my AI." - APEX User

Getting Started

Choose your preferred way to start:

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`bash

Run this in any project - installs nothing globally

npx @benredmond/apex start

That's it! APEX is now active in your AI assistant

`

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`bash

Install once, use everywhere

npm install -g @benredmond/apex
apex start
`

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`bash
apex start # Quick setup (simplified from apex init)
apex patterns list # View available patterns
apex patterns search # Find patterns by text
apex tasks list # View tasks
apex tasks stats # Task metrics
apex doctor # System health check
apex mcp install # Setup MCP integration
`

Your First APEX Workflow

Let's fix a bug using APEX intelligence - this takes less than 5 minutes:

`bash


1. In your project with a failing test

npx @benredmond/apex start

2. Open your AI assistant (Claude Code, Cursor, etc.)

3. Create a task for the bug

/create_task "Fix authentication test timeout error"

4. Let APEX guide the fix

/task T001
`

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`
🧠 ANALYZING... Complexity: 3/10
📚 LOADING... Found 3 similar past fixes
⚡ PATTERN... Applying [FIX:TEST:ASYNC_TIMEOUT] (★★★★★ 98% success)
🛡️ PREVENTING... Warning: This error often caused by missing await
✅ EXECUTING... Test fixed in one try (vs 3 tries typically)
📈 LEARNING... Pattern trust score increased
`

Core Concepts

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Think of APEX as your AI's long-term memory and pattern recognition system:

`
Your Code → APEX Learns → AI Remembers → Better Suggestions → Less Debugging
`

Key Components:
- Pattern Recognition: Tracks what works with trust scores (★★★★★)
- Failure Database: Never repeat the same mistake
- Smart Context: Loads only relevant patterns per task
- Complexity Routing: Simple tasks stay fast, complex tasks get deep analysis

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Watch patterns evolve from discovery to trusted solution:

`
NEW DISCOVERY TESTING VALIDATED TRUSTED
↓ ↓ ↓ ↓
[untracked] ──→ [★★★☆☆ 1 use] ──→ [★★★★☆ 3 uses] ──→ [★★★★★ 47 uses]
CONVENTIONS.pending.md CONVENTIONS.md
`

Real example:
`javascript
[PAT:AUTH:JWT] ★★★★★ (47 uses, 98% success)
// Secure JWT implementation - discovered in T012, now prevents auth vulnerabilities
const token = jwt.sign(payload, process.env.JWT_SECRET, { expiresIn: '24h' });
`

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Every task follows a proven methodology:

`
ARCHITECT → BUILDER → VALIDATOR → REVIEWER → DOCUMENTER
↓ ↓ ↓ ↓ ↓
Research Implement Test Review Learn & Document
`

This isn't just process - it's intelligence-driven:
- ARCHITECT: Loads similar task solutions
- BUILDER: Applies proven patterns
- VALIDATOR: Runs learned test strategies
- REVIEWER: AI + Gemini review (complex tasks)
- DOCUMENTER: Captures new patterns

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Organize work the way you think:

`
📌 Milestone: "User Authentication System"
└── 📅 Sprint: "Core Auth Features"
├── 📋 Task: "Design auth schema" [2h]
├── 📋 Task: "Build login API" [3h]
└── 📋 Task: "Add JWT middleware" [2h]
`

Workflows & Examples

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Scenario: Your test suite has a flaky test that fails intermittently.

`bash


Create the bug fix task

/create_task "Fix flaky user creation test"

Output: Created task T001

Execute with APEX intelligence

/task T001
`

APEX in Action:
`
🧠 ANALYZING...
- Complexity: 3/10 (single test file)
- Similar issues: Found 5 flaky test fixes
- Pattern match: [FIX:TEST:ASYNC_RACE] likely applies

📚 INTELLIGENCE LOADED:
- TX089: Fixed similar race condition (2h → 15min with pattern)
- Pattern: Always await user.save() before assertions
- Warning: db.clean() must complete before test

🔨 IMPLEMENTING...
Applied [FIX:TEST:ASYNC_RACE] pattern:
- Added await before user.save()
- Wrapped in act() for React updates
- Added afterEach cleanup

✅ VALIDATING...
- Ran test 50x: 0 failures (was failing 30% before)
- All related tests still passing

📝 DOCUMENTING...
- Pattern success rate: 94% → 95%
- Saved to learning database
- Estimated time saved: 1.5 hours
`

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Scenario: Add email notifications to your application.

`bash


Create a sprint for the feature

/sprint M01 "Email Integration"

Output: Created sprint S03

Break down into tasks

/create_task "Design email template system" --sprint S03
/create_task "Integrate SendGrid API" --sprint S03
/create_task "Add email preferences to user model" --sprint S03

Execute first task

/task T001
`

APEX Intelligence Throughout:
`
ARCHITECT PHASE:
🧠 Loading email patterns from 12 previous implementations
📊 Complexity: 6/10 - Will engage Gemini for architecture review
🎯 Suggested approach: Template-based with provider abstraction

BUILDER PHASE:
⚡ Auto-applied patterns:
- [PAT:EMAIL:TEMPLATE_ENGINE] - Handlebars with layouts
- [PAT:API:RETRY_LOGIC] - Exponential backoff for sends
- [PAT:CONFIG:ENV_VALIDATION] - Ensure API keys present

VALIDATOR PHASE:
✅ Tests generated using [PAT:TEST:EMAIL_MOCK] pattern
🛡️ Prevented common issue: Emails sending in test environment

RESULT: Feature complete in 2.5h (vs 5h estimated)
`

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Scenario: Modernize callback-based code to async/await.

`bash


Create refactoring task

/create_task "Refactor payment.js from callbacks to async/await"

Execute with intelligence

/task T001
`

Pattern Discovery in Action:
`
🧠 ANALYZING payment.js...
- 147 callback chains detected
- Error handling inconsistent
- Similar refactor: TX142 (saved 3 hours)

📋 APPLYING PATTERNS:
1. [PAT:REFACTOR:CALLBACK_TO_ASYNC] ★★★★★
- Preserve error handling semantics
- Maintain callback API for backwards compatibility

2. [PAT:REFACTOR:PROGRESSIVE] ★★★★☆
- Refactor in testable chunks
- Keep tests green throughout

🔄 DISCOVERING NEW PATTERN:
Found repeated pattern not in database:
- Payment providers need .catch() → try/catch wrapper
- Adding to CONVENTIONS.pending.md for validation

✅ RESULT:
- 147 callbacks → clean async/await
- All tests passing
- 0 production issues after deploy
- New pattern discovered for future use
`

Command Reference

APEX commands are organized by development phase:

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`bash
/milestone "Project Goal" # Create high-level milestone
/sprint M01 "Sprint Name" # Create sprint in milestone
/create_task "Task" --sprint S01 # Create task in sprint
/plan # View current plan
`

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`bash
/task T001 # Execute task with full intelligence
/task # Continue current task
/yolo # Autonomous multi-task mode
`

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`bash
/review # AI code review with learning
/test # Run tests with pattern analysis
/debug "error message" # Debug with failure database
/design "component" # Architecture assistance
`

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`bash
/commit # Smart commit with context
/reflect # Extract and save learnings
`

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`bash
apex start # Initialize APEX patterns database (in terminal)
/prime # Load APEX context into AI
/verify # Check APEX health
`

Advanced Usage

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View and manage your pattern library:

`bash


In terminal


npx @benredmond/apex patterns # List all active patterns
npx @benredmond/apex patterns pending # Show patterns being tested
npx @benredmond/apex patterns stats # Pattern usage statistics
`

Share patterns with your team:
`bash


Patterns are stored in version control


git add .apex/CONVENTIONS.md
git commit -m "Share authentication patterns"
`

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For complex tasks (complexity ≥7), APEX automatically engages Gemini for deeper analysis:

`json
// .apex/config.json
{
"apex": {
"geminiApiKey": "your-api-key",
"complexityThreshold": 7, // When to engage Gemini
"geminiModel": "gemini-pro"
}
}
`

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Fine-tune APEX behavior:

`json
{
"apex": {
"patternPromotionThreshold": 3, // Uses before promotion
"trustScoreThreshold": 0.8, // Success rate for promotion
"autoPatternDiscovery": true, // Find patterns automatically
"contextTokenBudget": 30000, // Max context size
"enableFailurePrevention": true // Warn about past failures
}
}
`

Project Structure

APEX creates an intelligent project organization:

`
your-project/
├── .apex/ # APEX Intelligence Hub
│ ├── CONVENTIONS.md # Trusted patterns (★★★★☆+)
│ ├── CONVENTIONS.pending.md # Testing patterns (<3 uses)
│ ├── 09_LEARNING/
│ │ ├── failures.jsonl # What went wrong & how to prevent
│ │ └── TASK_LEARNINGS.md # Successful approaches
│ └── PATTERN_METADATA.json # Pattern statistics & trust scores
│
└── .claude/commands/apex/ # AI command templates
├── 01_plan/ # Planning phase commands
├── 02_execute/ # Execution with intelligence
├── 03_quality/ # Smart testing & review
└── 04_finalize/ # Learning capture
`

Troubleshooting

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"Command not found" in AI assistant
- Run /prime to load APEX commands into context
- Ensure you ran apex start in your project root
- Check that .claude/commands/apex/ exists

Patterns not being applied
- Check pattern trust score - must be ★★★☆☆ or higher
- Verify pattern context matches your use case
- Run apex patterns stats to see pattern health

High complexity score on simple task
- Review task description for trigger words
- Check if task touches multiple systems
- Complexity can be manually overridden in task file

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Q: How does APEX work with my AI assistant?
A: APEX provides markdown-based commands that guide your AI through proven workflows. It's like giving your AI a memory and a methodology.

Q: Is my code/data private?
A: Yes. APEX runs locally and stores all patterns/learnings in your project. Nothing is sent to external servers except optional Gemini API calls for complex tasks.

Q: Can I use APEX with [Cursor/GitHub Copilot/other AI]?
A: Yes! APEX works with any AI that can read markdown files and execute commands. The commands are universal.

Q: How long before I see productivity gains?
A: Immediately for workflow organization. Pattern benefits appear after 5-10 tasks. Full 40-55% gains typically seen after 50+ tasks as the pattern library grows.

Q: Can I share patterns with my team?
A: Yes! Patterns are stored in .apex/CONVENTIONS.md which can be committed to version control and shared.

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Key areas for contribution:
- Domain-specific pattern libraries
- AI assistant integrations
- Workflow improvements
- Documentation examples

License

MIT License - see LICENSE for details

Acknowledgments

APEX was inspired by the need for AI assistants that truly learn and improve. Special thanks to:
- The Claude, Cursor, and Copilot communities
- Early adopters who provided pattern data
- Contributors who shaped the workflow methodology

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Ready to stop repeating mistakes? Run npx @benredmond/apex start` and watch your AI assistant get smarter with every task.

Built with ❤️ and Intelligence by the APEX Community