Advanced features

Maestro: "Should I use REST or GraphQL for the API?"
You: [Type response and submit]
Maestro: [Continues with your choice]

Maestro: "Please upload the API specification document"
You: [Upload OpenAPI spec]
Maestro: [Implements based on uploaded spec]

Maestro: "Which authentication approach?"
Buttons: [JWT] [OAuth 2.0] [Session-based]
You: [Click OAuth 2.0]
Maestro: [Implements OAuth flow]

** Inefficient:
Multiple asks for simple information
"What's the database host?" → Answer
"What's the port?" → Answer
"What's the username?" → Answer

** Efficient:
"Database credentials: host=localhost, port=5432, user=admin, db=myapp"

"Implement user service. Database: PostgreSQL at localhost:5432. Auth: JWT. Tests: pytest. Follow patterns in existing services."

Fewer clarification asks needed.

/compact → Interactive UI

Choose what to compress:
- Debugging sessions → Compress heavily
- Implementation details → Moderate compression
- Architectural decisions → Preserve fully
- Test results → Compress

Result: Preserve important context, free capacity

Click capacity bar → Detailed breakdown

View:
- Tokens per turn
- Memory by type (user requests, agent replies, tool results)
- File context contribution
- Tool schema overhead

Identify: What's consuming capacity

Keep:
- Specifications and requirements
- Architectural decisions
- Validation results
- Key lessons learned

Remove:
- Failed attempts and dead ends
- Debugging iterations
- Exploratory analysis (after conclusion)
- Redundant explanations

After major milestone:
1. /synopsis → Document session so far
2. /forget → Remove implementation details
3. Keep: Synopsis + current state
4. Continue: With fresh capacity

Team Lead:
- Defines requirements and success criteria
- Reviews Maestro's proposals
- Makes architectural decisions

Developer:
- Monitors implementation progress
- Provides domain expertise when Maestro asks
- Reviews code changes

Both see identical state, can interact simultaneously

Developer A (Day 1):
- Implements core feature
- Uses /synopsis to document state
- Shares session with Developer B

Developer B (Day 2):
- Resumes session
- Reviews synopsis
- Continues implementation
- Full context preserved

"Show me what changed between iteration 5 and 10 of auth.py"

Maestro uses Compute Diffs:
- Generates side-by-side diff
- Highlights additions/deletions
- Explains changes in context

"The authentication in iteration 7 was better. Restore auth.py to iteration 7 while keeping everything else at latest."

Preserves good work while recovering from wrong turn.

"Walk me through the evolution of auth.py showing key changes at iterations 0, 5, 10, 15."

Understanding decision history

"Hide all test files except the ones I just created"

Pattern: **/*test*.py
Exclude: New test files created this session
Result: Old test iterations hidden, new ones visible

"Reorganize code: move all auth-related files from src/ to src/auth/"

Maestro:
- Identifies auth files
- Moves with pattern preservation
- Updates imports across codebase
- Verifies code still works

Create Sandbox(
    sandbox_name="data_processing",
    cpu_count=8,
    memory_gb=32
)

Use for:
- Large dataset processing
- Memory-intensive computations
- Parallel processing

Create Sandbox(
    sandbox_name="ml_training",
    gpu_type="A100",
    gpu_count=2
)

Use for:
- Model training
- Large-scale inference
- GPU-accelerated computing

Create Sandbox(
    sandbox_name="docker_dev",
    privileged=True
)

Use for:
- Docker-based workflows
- Container image building
- Docker Compose orchestration

Create Sandbox(
    sandbox_name="prod_analysis",
    ssh_connection="admin@prod-server.com:22",
    ssh_private_key="credential://PROD_SSH_KEY"
)

Use for:
- Analyzing production systems
- Remote debugging
- Deployment operations
- Infrastructure inspection

Goal: Comprehensive security audit

Tool chain:
1. Search Files → Find authentication code
2. Analyze Files → Extract security patterns
3. Perplexity Search → Research known vulnerabilities
4. Complex Reasoning → Assess overall security posture
5. PROPOSE_EDIT → Implement fixes
6. Execute Command → Run security scanners
7. Generate report

Each tool feeds next step

/tools → Disable design tools if pure backend session

Advantages:
- Reduced token usage
- Faster tool schema loading
- Focused capabilities

For machine learning session:
/tools → Ensure complex reasoning and coding tools enabled

Critical for:
- Algorithm design
- Optimization strategies
- Research synthesis

Session setup:
- Clone repo, branch main (baseline)
- Clone repo, branch feature/auth (WIP)
- Clone repo, branch feature/cache (WIP)

Work on auth:
- View files from feature/auth clone
- Make changes
- Test
- Update PR for feature/auth

Switch to cache:
- View files from feature/cache clone
- Make changes
- Test
- Update PR for feature/cache

No branch checkout needed - different clones

Early implementation stage:
- Create PR with incomplete feature
- Mark as draft
- Request architectural feedback
- Continue implementation

Later:
- Update PR with completion
- Convert from draft to ready
- Request full review

Base: main

Create PR #1 (feature/foundation → main):
- Core infrastructure

Create PR #2 (feature/api → feature/foundation):
- API layer depending on foundation

Create PR #3 (feature/ui → feature/api):
- UI depending on API

Review and merge order: #1, #2, #3
Each PR independently reviewable against its base

/chaos

Maestro starts chaos testing:
- Duration: 5 minutes (default)
- Failure injection ongoing
- Your code runs under stress
- System monitors behavior

Report generated showing:
- Failures injected
- System responses
- Recovery patterns
- Issues discovered

Level 1: Unit tests
"Run unit tests. Verify all pass with >90% coverage."

Level 2: Integration tests
"Run integration tests. Verify service interactions."

Level 3: End-to-end tests
"Run E2E tests in sandbox. Verify full user flows."

Level 4: Performance tests
"Benchmark under realistic load. Show metrics."

Level 5: Security tests
"Run static analysis and dependency scanning. Report issues."

Only after all levels pass: "Feature is complete"

Before optimization:
1. Capture current performance metrics
2. Document baseline behavior
3. Create reproducible test harness

After optimization:
4. Run same tests
5. Compare to baseline
6. Prove improvement with data

Advantage: Objective measurement, no guesswork

Session resumed → Maestro restores state

First request:
"/synopsis to remind me where we left off"

Maestro provides:
- What was accomplished
- Current state
- Next logical steps

Then continue with context

Session 1:
- Implements and validates feature
- Uses /synopsis at end
- Downloads synopsis markdown

Session 2 (days later, new session):
- Upload synopsis from Session 1
- "Read this synopsis and continue the work"

Maestro:
- Understands prior context
- Picks up where Session 1 left off
- Maintains continuity

After implementation burst:
/refresh + /compact → Clean context for validation

Before PR:
/refresh + run all tests → Ensure clean state

Session end:
/synopsis + /download-changed → Document + preserve work

Custom instruction: "For all API implementations:
- Use FastAPI framework
- PostgreSQL for persistence
- Redis for caching
- Pytest with >90% coverage
- Async/await patterns
- Pydantic for validation"

Applies to all requests in session
Consistent patterns automatically

Instead of:
"Please implement the authentication system with JWT tokens, including login and logout endpoints, token refresh functionality, and comprehensive tests for all scenarios."

Try:
"Auth system: JWT, refresh, standard endpoints, full tests"

With established patterns, Maestro infers details