| name | agent-gap-analyzer |
| description | Analyze project needs against existing Agent capabilities to identify coverage gaps. Reads project-profile.yaml and Agent capabilities tags, outputs deterministic coverage report. Use when evaluating which Agents are missing for a project. |
Agent 覆盖度分析器
对比项目需求与现有 Agent 能力,识别覆盖缺口。
使用场景
- "这个项目还缺什么 Agent?"
- "现有 Agent 能覆盖多少?"
- project-analyzer 之后的第二步
前置条件
.aria/project-profile.yaml 存在 (由 project-analyzer 生成)- Agent frontmatter 含
capabilities 字段 (US-010 STCO + US-011 T0)
流程
1. 加载输入
- 读取
.aria/project-profile.yaml
- 读取所有 Agent 的 capabilities:
- 插件级:
${CLAUDE_PLUGIN_ROOT}/agents/*.md frontmatter
- 项目级:
.aria/agents/*.md frontmatter (如存在)
- 读取
${CLAUDE_PLUGIN_ROOT}/references/capabilities-taxonomy.yaml 做标签规范化
2. 推导需求场景
从 project-profile 的 tech_stack + patterns 映射到能力标签需求:
tech_stack_mapping:
orm: "Prisma" → 需求: [orm-migration, query-optimization, database-schema]
framework: "Express" → 需求: [api-design, performance-optimization]
testing: "Jest" → 需求: [test-strategy]
ci_cd: "GitHub Actions" → 需求: [ci-cd-pipeline]
deployment: "Docker" → 需求: [infrastructure]
3. 标签规范化
使用 capabilities-taxonomy.yaml 的同义词映射:
- Agent capability
database-schema 匹配需求 db-design (同义词)
- 规范化后再做匹配,避免假缺口
4. 匹配计算
对每个需求场景:
- 遍历所有 Agent capabilities
- match_rate = 命中标签数 / 需求标签数
- covered: match_rate >= 0.5
- gap: match_rate < 0.5
5. 输出
.aria/coverage-report.yaml (schema_version: "1"):
schema_version: "1"
project: "kairos"
timestamp: "2026-04-11T..."
covered:
- scenario: "API design"
matched_agent: "backend-architect"
matched_capabilities: ["api-design"]
match_rate: 1.0
gaps:
- scenario: "ORM migration"
required_capabilities: ["orm-migration", "query-optimization"]
best_partial_match:
agent: "backend-architect"
matched: ["database-schema"]
match_rate: 0.33
suggested_agent:
name: "database-specialist"
capabilities: ["orm-migration", "query-optimization", "database-schema"]
summary:
total_scenarios: 8
covered: 5
gaps: 3
coverage_rate: 62.5%
重要
- 匹配基于 capabilities 标签 (确定性), 不使用 LLM 解析 description
- match_rate 是标签重合率, 非 AI 评分
- 场景列表来自规则映射, 非每次 LLM 推断
