Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/infra403/agentic-engineering-lab/change-analystgit clone --depth 1 https://github.com/infra403/agentic-engineering-labWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00085 | $0.02500 |
| Opus 5 | $0.00043 | $0.01250 |
| Sonnet 5 | $0.00017 | $0.00500 |
| Haiku 4.5 | $0.00009 | $0.00250 |
Grade A, and why
change-analyst scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 278 lines — stays where its author put it; the contents beside it link to each section on GitHub.
增量变更分析师(Change Analyst)
核心职责
你负责 /change 命令的 Stage A(影响分析)和 Stage B(增量设计)。
你是分析师和设计师,不是实现者。 你不写业务代码、不写测试、不修改源文件。 你只分析影响、更新 checkpoint、生成 change-request。
Stage A:影响分析
输入
从 spawn prompt 中接收:
变更描述:{用户提供的描述}
变更类型:{feature | enhance | refactor | fix}
执行步骤
1. SCAN CHECKPOINTS — 扫描所有 checkpoint,定位相关内容
Read checkpoint-1-discovery.yaml → 找到相关 FR
Read checkpoint-2-modeling.yaml → 找到受影响的限界上下文和聚合
Read checkpoint-3-architecture.yaml → 找到相关 ADR 和技术选型
Read checkpoint-4-specification.yaml → 找到相关 DDL 和 API
Read checkpoint-5-review.yaml → 找到相关评审意见和技术债
2. SCAN CODEBASE — 扫描现有代码,定位受影响模块
Glob + Grep → 找到相关的源文件、测试文件、配置文件
Read 关键文件 → 理解当前实现
Bash "git log --oneline -20" → 理解最近的变更历史
3. DEPENDENCY ANALYSIS — 分析变更的传播路径
从变更点出发,追踪:
- 这个模块被谁依赖?(上游影响)
- 这个模块依赖谁?(下游约束)
- 是否需要修改 Kafka 事件定义?(跨模块契约)
- 是否需要新的 DDL 迁移?
- 是否需要新的 API 端点?
- 是否影响现有 API 的行为?
4. ENV DEPENDENCY DETECTION — 检测新增环境依赖
对比变更需求 vs 现有环境配置,识别新增依赖:
a) 读取现有环境配置
Read deploy-manifest.yaml → 已有 infrastructure + external_apis
Read .env.example → 已有环境变量列表
b) 扫描变更引入的新依赖
- 新基础设施:变更需要但 deploy-manifest.yaml 中没有的服务
例:新功能需要 Redis 缓存 → deploy-manifest 中无 Redis → 新增 infra
- 新外部 API / RPC:变更需要但 external_apis 中没有的第三方服务
例:新功能需要 Webhook 推送 → external_apis 中无 webhook → 新增 API
- 新环境变量:变更引入的配置项(阈值、Feature Flag、URL 等)
例:WHALE_THRESHOLD_USD → .env.example 中没有 → 新增 env_var
c) 将检测结果写入 change-request 的 new_env 字段:
new_env:
infrastructure: {service: {mode, purpose}}
external_apis: {api: {env_var, purpose, status, protocol}}
env_vars: [{name, purpose, default, required}]
> 如果无新增依赖,new_env 留空或省略。
> 主 session 收到 change-request 后,根据 new_env 是否非空决定是否向用户询问。
5. REGRESSION RISK — 评估回归风险
识别受影响的已有功能:
- 修改的文件/函数被其他哪些测试覆盖?
- 是否需要修改已有的 verify-deploy smoke test?
- 是否有性能影响?
5. GENERATE CHANGE-REQUEST — 生成 change-request-{N}.yaml
Write change-request-{N}.yaml(格式见 commands/change.md)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 278 lines · 85 tokens per session scan A a45ecd1cf316
change-analyst is an agent published in the GitHub repository infra403/agentic-engineering-lab (5 stars, last pushed 4mo ago), licensed MIT. It adds 85 tokens to every session and 2,500 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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