关于 ce-agent-native-audit
用于评估代码库是否符合 Agent 原生架构原则的审计技能,面向构建 Agent 功能的开发团队。它并行检查八项原则,并汇总评分、差距和优先级建议。
主要功能
- 01审计八项架构原则
- 02并行运行子 Agent 审查
- 03按原则评估合规度
- 04列出差距与建议
- 05支持单项原则审计
适用场景
- 01评估应用的 Agent 原生设计
- 02查找界面操作与 Agent 工具间的差距
- 03审查上下文注入与共享数据访问
- 04生成带评分的架构报告
安装
复制下面的命令,在终端中运行。
$
npx skills add https:/ / github.com/ everyinc/ compound-engineering-plugin --skill ce-agent-native-audit安装后在 Claude Code 中输入/ce-agent-native-audit确认已加载。
详细介绍
Agent-Native 架构审计
依据 Agent-Native 架构原则全面审查代码库,针对每项原则启动并行子 Agent,并生成评分报告。
核心审计原则
- 操作对等 - “用户能做的事,Agent 都能做”
- 工具即原语 - “工具提供能力,而非行为”
- 上下文注入 - “系统提示词包含有关应用状态的动态上下文”
- 共享工作区 - “Agent 和用户在同一数据空间中工作”
- CRUD 完整性 - “每个实体都具备完整的 CRUD(创建、读取、更新、删除)能力”
- UI 集成 - “Agent 的操作会立即反映在 UI 中”
- 能力发现 - “用户可以发现 Agent 能做什么”
- 提示词原生功能 - “功能是定义结果的提示词,而非代码”
工作流程
步骤 1:加载 Agent-Native 技能
首先,调用 Agent-Native 架构技能以了解所有原则:
/ce-agent-native-architecture
选择选项 7(操作对等)以加载完整参考资料。
步骤 2:启动并行子 Agent
使用平台的子 Agent 原语启动 8 个并行子 Agent(在 Claude Code 中使用 Agent 和 subagent_type: Explore,在 Codex 中使用 spawn_agent 和 agent_type: "explorer",在 Pi 中通过 pi-subagents 扩展使用 subagent 和 agent: "scout"),每项原则各启动一个。每个 Agent 都应:
- 枚举代码库中的所有实例(用户操作、工具、上下文、数据存储等)
- 根据对应原则检查合规情况
- 提供明确的分数,例如“X/Y(百分比)”
- 列出具体差距和建议
Agent 1:操作对等
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:工具即原语
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:上下文注入
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:共享工作区
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 完整性
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 集成
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:能力发现
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:提示词原生功能
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
步骤 3:汇总报告
所有 Agent 完成后,汇总报告,其中包含:
## 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]
成功标准
- 8 个子 Agent 均完成审计
- 每项原则都有明确的数字评分(X/Y 格式)
- 汇总表格显示所有分数和状态指标
- 按影响优先级排列前 10 项建议
- 报告指出优势和差距
可选:单项原则审计
如果 $ARGUMENTS 指定了单项原则(例如“操作对等”),则仅运行对应的子 Agent,并仅提供该原则的详细发现。
有效参数:
action parity或1tools或primitives或2context或injection或3shared或workspace或4crud或5ui或integration或6discovery或7prompt或features或8
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