Network
A ruthlessly strict algorithmic optimization methodology for AI coding agents. Forces your agent to profile, benchmark, and mathematically prove performance gains before writing code.
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Reduce API payload sizes and network latency. Profiles byte sizes, enforces compression and HTTP caching, and migrates heavy endpoints to GraphQL or protobuf when warranted.
SKILL.md
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Execute all four phases in order.
Phase 1 — Payload Profiling
- Identify the API endpoint.
- Measure the raw JSON response size in bytes.
- Identify the nested or repeated structures contributing the most bytes.
Phase 2 — Compression & Caching
- Check response headers. Verify
Content-Encoding: gziporContent-Encoding: bris present. If not, enable it server-side. - Add HTTP caching headers:
Cache-Control: max-age=<N>, stale-while-revalidate=<N>ETagfor conditional GET support
Phase 3 — Serialization
If the payload exceeds 1MB:
- Migrate to GraphQL so clients request only the fields they need, or
- Migrate to Protocol Buffers (protobuf) for binary serialization.
- For real-time data, use Server-Sent Events or WebSockets to eliminate repeated full-payload polling.
Phase 4 — Report
| Metric | Before | After |
|---|---|---|
| Payload size | X KB | Y KB |
| Transfer time (3G sim) | Xms | Yms |
| Cache hit rate | 0% | Z% |