ce-agent-native-audit

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• SKILL.md

SKILL.md (first lines)

# Agent-Native Architecture Audit

Conduct a comprehensive review of the codebase against agent-native architecture principles, launching parallel sub-agents for each principle and producing a scored report.

## Core Principles to Audit

1. **Action Parity** - "Whatever the user can do, the agent can do"
2. **Tools as Primitives** - "Tools provide capability, not behavior"
3. **Context Injection** - "System prompt includes dynamic context about app state"
4. **Shared Workspace** - "Agent and user work in the same data space"
5. **CRUD Completeness** - "Every entity has full CRUD (Create, Read, Update, Delete)"
6. **UI Integration** - "Agent actions immediately reflected in UI"
7. **Capability Discovery** - "Users can discover what the agent can do"
8. **Prompt-Native Features** - "Features are prompts defining outcomes, not code"

## Workflow

### Step 1: Load the Agent-Native Skill

First, invoke the agent-native-architecture skill to understand all principles:

```
/ce-agent-native-architecture
```

Select option 7 (action parity) to load the full reference material.

### Step 2: Launch Parallel Sub-Agents

Launch 8 parallel sub-agents using the platform's subagent primitive (`Agent` with `subagent_type: Explore` in Claude Code, `spawn_agent` with `agent_type: "explorer"` in Codex, `subagent` with `agent: "scout"` in Pi via the `pi-subagents` extension), one for each principle. Each agent should:

1. Enumerate ALL instances in the codebase (user actions, tools, contexts, data stores, etc.)
2. Check compliance against the principle
3. Provide a SPECIFIC SCORE like "X out of Y (percentage%)"
4. List specific gaps and recommendations

<sub-agents>

**Agent 1: Action Parity**
```
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
```

**Agent 2: Tools as Primitives**
```
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
```

**Agent 3: Context Injection**
```
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
```

**Agent 4: Shared Workspace**
```
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)

From Vidoxlabs/vitools-public, MIT.

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