all-the-vibes/atv-starterkit/agent-native-audit

/compound-engineering-agent-native-architecture

Audit for ACTION PARITY - "Whatever the user can do, the agent can do."
 
Tasks:
1. Enumerate ALL user actions in frontend (API calls, button clicks, form submissions)
   - Search for API service files, fetch calls, form handlers
   - Check routes and components for user interactions
2. Check which have corresponding agent tools
   - Search for agent tool definitions
   - Map user actions to agent capabilities
3. Score: "Agent can do X out of Y user actions"
 
Format:
## Action Parity Audit
### User Actions Found
| Action | Location | Agent Tool | Status |
### Score: X/Y (percentage%)
### Missing Agent Tools
### Recommendations

Audit for TOOLS AS PRIMITIVES - "Tools provide capability, not behavior."
 
Tasks:
1. Find and read ALL agent tool files
2. Classify each as:
   - PRIMITIVE (good): read, write, store, list - enables capability without business logic
   - WORKFLOW (bad): encodes business logic, makes decisions, orchestrates steps
3. Score: "X out of Y tools are proper primitives"
 
Format:
## Tools as Primitives Audit
### Tool Analysis
| Tool | File | Type | Reasoning |
### Score: X/Y (percentage%)
### Problematic Tools (workflows that should be primitives)
### Recommendations

Audit for CONTEXT INJECTION - "System prompt includes dynamic context about app state"
 
Tasks:
1. Find context injection code (search for "context", "system prompt", "inject")
2. Read agent prompts and system messages
3. Enumerate what IS injected vs what SHOULD be:
   - Available resources (files, drafts, documents)
   - User preferences/settings
   - Recent activity
   - Available capabilities listed
   - Session history
   - Workspace state
 
Format:
## Context Injection Audit
### Context Types Analysis
| Context Type | Injected? | Location | Notes |
### Score: X/Y (percentage%)
### Missing Context
### Recommendations

Audit for SHARED WORKSPACE - "Agent and user work in the same data space"
 
Tasks:
1. Identify all data stores/tables/models
2. Check if agents read/write to SAME tables or separate ones
3. Look for sandbox isolation anti-pattern (agent has separate data space)
 
Format:
## Shared Workspace Audit
### Data Store Analysis
| Data Store | User Access | Agent Access | Shared? |
### Score: X/Y (percentage%)
### Isolated Data (anti-pattern)
### Recommendations

Audit for CRUD COMPLETENESS - "Every entity has full CRUD"
 
Tasks:
1. Identify all entities/models in the codebase
2. For each entity, check if agent tools exist for:
   - Create
   - Read
   - Update
   - Delete
3. Score per entity and overall
 
Format:
## CRUD Completeness Audit
### Entity CRUD Analysis
| Entity | Create | Read | Update | Delete | Score |
### Overall Score: X/Y entities with full CRUD (percentage%)
### Incomplete Entities (list missing operations)
### Recommendations

Audit for UI INTEGRATION - "Agent actions immediately reflected in UI"
 
Tasks:
1. Check how agent writes/changes propagate to frontend
2. Look for:
   - Streaming updates (SSE, WebSocket)
   - Polling mechanisms
   - Shared state/services
   - Event buses
   - File watching
3. Identify "silent actions" anti-pattern (agent changes state but UI doesn't update)
 
Format:
## UI Integration Audit
### Agent Action → UI Update Analysis
| Agent Action | UI Mechanism | Immediate? | Notes |
### Score: X/Y (percentage%)
### Silent Actions (anti-pattern)
### Recommendations

Audit for CAPABILITY DISCOVERY - "Users can discover what the agent can do"
 
Tasks:
1. Check for these 7 discovery mechanisms:
   - Onboarding flow showing agent capabilities
   - Help documentation
   - Capability hints in UI
   - Agent self-describes in responses
   - Suggested prompts/actions
   - Empty state guidance
   - Slash commands (/help, /tools)
2. Score against 7 mechanisms
 
Format:
## Capability Discovery Audit
### Discovery Mechanism Analysis
| Mechanism | Exists? | Location | Quality |
### Score: X/7 (percentage%)
### Missing Discovery
### Recommendations

Audit for PROMPT-NATIVE FEATURES - "Features are prompts defining outcomes, not code"
 
Tasks:
1. Read all agent prompts
2. Classify each feature/behavior as defined in:
   - PROMPT (good): outcomes defined in natural language
   - CODE (bad): business logic hardcoded
3. Check if behavior changes require prompt edit vs code change
 
Format:
## Prompt-Native Features Audit
### Feature Definition Analysis
| Feature | Defined In | Type | Notes |
### Score: X/Y (percentage%)
### Code-Defined Features (anti-pattern)
### Recommendations

## Agent-Native Architecture Review: [Project Name]
 
### Overall Score Summary
 
| Core Principle | Score | Percentage | Status |
|----------------|-------|------------|--------|
| Action Parity | X/Y | Z% | ✅/⚠️/❌ |
| Tools as Primitives | X/Y | Z% | ✅/⚠️/❌ |
| Context Injection | X/Y | Z% | ✅/⚠️/❌ |
| Shared Workspace | X/Y | Z% | ✅/⚠️/❌ |
| CRUD Completeness | X/Y | Z% | ✅/⚠️/❌ |
| UI Integration | X/Y | Z% | ✅/⚠️/❌ |
| Capability Discovery | X/Y | Z% | ✅/⚠️/❌ |
| Prompt-Native Features | X/Y | Z% | ✅/⚠️/❌ |
 
**Overall Agent-Native Score: X%**
 
### Status Legend
-✅ Excellent (80%+)
-⚠️ Partial (50-79%)
-❌ Needs Work (<50%)
 
### Top 10 Recommendations by Impact
 
| Priority | Action | Principle | Effort |
|----------|--------|-----------|--------|
 
### What's Working Excellently
 
[List top 5 strengths]