I spent 3.8 million tokens finding which service to change

说明与用途

在大型单体仓库中，识别受新规范影响的微服务最初需要大量令牌，因为图形文件庞大。通过结合轻量级 Markdown 描述和 Graphify 的深度分析，令牌消耗降至 61k，使流程变得可行。

问题

Determining which microservices in a large monorepo are affected by a new spec is costly and inefficient when relying on a single tool.

结果

The two-layer approach cuts token usage from millions to tens of thousands, enabling practical agent-driven routing in large monorepos.

工作流

• 1. Run Graphify on a single service to generate a structural graph (graph.json).
• 2. Generate a lightweight markdown descriptor per service containing typed frontmatter and links to neighbors.
• 3. For a spec, first query the markdown descriptors to identify candidate services (≈676 tokens per service).
• 4. For each candidate, run Graphify locally on that service to compute precise impact radius (few hundred tokens).
• 5. Aggregate results to determine affected services and detailed changes.
• 6. Use the catalogified data for agent-driven routing and impact analysis.

证据

Graphify is superb at the second one. graphify affected "UpdateApiUsageCount" --depth 2 gives you a precise impact radius in a few hundred tokens.

来源

One service entry 2.6 KB, 676 tokens. Ninety of those is 61,000 tokens.

来源