بنقرة واحدة
agent-native-audit
Run comprehensive agent-native architecture review with scored principles
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
Run comprehensive agent-native architecture review with scored principles
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.
Explore requirements and approaches through collaborative dialogue before writing a right-sized requirements document and planning implementation. Use for feature ideas, problem framing, when the user says 'let's brainstorm', or when they want to think through options before deciding what to build. Also use when a user describes a vague or ambitious feature request, asks 'what should we build', 'help me think through X', presents a problem with multiple valid solutions, or seems unsure about scope or direction — even if they don't explicitly ask to brainstorm.
Refresh stale or drifting learnings and pattern docs in docs/solutions/ by reviewing, updating, replacing, or archiving them against the current codebase. Use after refactors, migrations, dependency upgrades, or when a retrieved learning feels outdated or wrong. Also use when reviewing docs/solutions/ for accuracy, when a recently solved problem contradicts an existing learning, or when pattern docs no longer reflect current code.
Document a recently solved problem to compound your team's knowledge
Generate and critically evaluate grounded improvement ideas for the current project. Use when asking what to improve, requesting idea generation, exploring surprising improvements, or wanting the AI to proactively suggest strong project directions before brainstorming one in depth. Triggers on phrases like 'what should I improve', 'give me ideas', 'ideate on this project', 'surprise me with improvements', 'what would you change', or any request for AI-generated project improvement suggestions rather than refining the user's own idea.
Transform feature descriptions or requirements into structured implementation plans grounded in repo patterns and research. Use when the user says 'plan this', 'create a plan', 'write a tech plan', 'plan the implementation', 'how should we build', 'what's the approach for', 'break this down', or when a brainstorm/requirements document is ready for technical planning. Best when requirements are at least roughly defined; for exploratory or ambiguous requests, prefer ce:brainstorm first.
| name | agent-native-audit |
| description | Run comprehensive agent-native architecture review with scored principles |
| argument-hint | [optional: specific principle to audit] |
| disable-model-invocation | true |
Conduct a comprehensive review of the codebase against agent-native architecture principles, launching parallel sub-agents for each principle and producing a scored report.
First, invoke the agent-native-architecture skill to understand all principles:
/compound-engineering:agent-native-architecture
Select option 7 (action parity) to load the full reference material.
Launch 8 parallel sub-agents using the Task tool with subagent_type: Explore, one for each principle. Each agent should:
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)
### Recommendations
Agent 5: CRUD Completeness
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
Agent 6: UI Integration
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
Agent 7: Capability Discovery
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
Agent 8: Prompt-Native Features
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
After all agents complete, compile a summary with:
## 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]
If $ARGUMENTS specifies a single principle (e.g., "action parity"), only run that sub-agent and provide detailed findings for that principle alone.
Valid arguments:
action parity or 1tools or primitives or 2context or injection or 3shared or workspace or 4crud or 5ui or integration or 6discovery or 7prompt or features or 8