protobufjs-dynamic-schema
暂无简介
- License 许可
- Apache-2.0
- 最近更新
- 2026-11-12
- Stars
- ★ 13
- Forks
- 2
03 SKILL.md 正文
/protobufjs-dynamic-schema
When to Use
- Load .proto schemas dynamically at agent startup without build-time codegen
- Generate TypeScript interfaces from .proto for type-safe message handling
- Reflection: enumerate all message fields at runtime for generic serializers
- JSON ↔ Protobuf interop: accept JSON from external APIs, emit Protobuf internally
Do NOT use for
- Performance-critical paths (static generated code is 2× faster than dynamic loading)
- gRPC service definitions (use [[grpc-streaming-rpc]] which wraps protobufjs)
Dynamic load and use
import protobuf from 'protobufjs'
// Load at startup, cache root — never reload per message
const root = await protobuf.load(['agent_message.proto', 'task.proto'])
const AgentMessage = root.lookupType('yamtam.AgentMessage')
const TaskPayload = root.lookupType('yamtam.TaskPayload')
export function encodeAgentMessage(data: object): Buffer {
const err = AgentMessage.verify(data)
if (err) throw new Error(`[protobuf] ${err}`)
return Buffer.from(AgentMessage.encode(AgentMessage.create(data)).finish())
}
export function decodeAgentMessage(buf: Buffer): object {
const msg = AgentMessage.decode(buf)
return AgentMessage.toObject(msg, {
longs: String, // int64 → string (avoids JS precision loss)
enums: String, // enum number → name
bytes: String, // bytes → base64 string
defaults: true, // include fields with default values
})
}
Static codegen (for production)
# Generate JS + TypeScript declarations
npx pbjs -t static-module -w es6 -o src/generated/protos.js *.proto
npx pbts -o src/generated/protos.d.ts src/generated/protos.js
# Generated usage (2× faster than dynamic, fully typed)
import { yamtam } from './generated/protos'
const msg = yamtam.AgentMessage.create({ agentId: 'x', seq: 1 })
const buf = yamtam.AgentMessage.encode(msg).finish()
Reflection API (enumerate message fields)
const AgentMessage = root.lookupType('yamtam.AgentMessage')
// List all fields
for (const field of AgentMessage.fieldsArray) {
console.log(` ${field.id}: ${field.name} (${field.type})`)
}
// Check if a field exists by name
const hasPayload = !!AgentMessage.fields['payload']
// Nested message types
const nested = AgentMessage.resolvedFields
.filter(f => f.resolvedType instanceof protobuf.Type)
.map(f => f.resolvedType.name)
JSON ↔ Protobuf conversion
// JSON from external API → Protobuf binary
function jsonToProto(jsonObj: object): Buffer {
const msg = AgentMessage.fromObject(jsonObj) // lenient: coerces types
const err = AgentMessage.verify(msg)
if (err) throw new Error(`[protobuf] ${err}`)
return Buffer.from(AgentMessage.encode(msg).finish())
}
// Protobuf binary → JSON (for logging/debugging)
function protoToJson(buf: Buffer): string {
const msg = AgentMessage.decode(buf)
return JSON.stringify(AgentMessage.toObject(msg, { longs: String, enums: String }))
}
Anti-Fake-Pass Checklist
❌ protobuf.load() called per message → filesystem + parse overhead on every encode
❌ AgentMessage.create() skipped → encode() accepts plain objects but skips field defaults
❌ longs: Number in toObject → int64 precision loss above Number.MAX_SAFE_INTEGER
❌ Dynamic schema in production hot path → use static codegen (pbjs) for 2× throughput
❌ lookupType() throws if package prefix wrong — use full name: 'yamtam.AgentMessage' not 'AgentMessage'
❌ Missing .proto file at load path → protobuf.load() throws, no fallback
04 同仓库更多 Skills
openai--sentry--sentry
Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.
ito-basket-compare
Compare Itô prediction-market baskets against a user's knowledge base, portfolio notes, financial context, watchlist, or research thesis. Use for read-only basket comparison and gap analysis without investment advice or live trading.
implementing-network-traffic-baselining
Build network traffic baselines from NetFlow/IPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly/daily traffic pattern profiling
detecting-evasion-techniques-in-endpoint-logs
Detects defense evasion techniques used by adversaries in endpoint logs including log tampering, timestomping, process injection, and security tool disabling. Use when investigating suspicious endpoint behavior, building detection rules for evasion tactics, or conducting threat hunting for stealthy adversary activity. Activates for requests involving evasion detection, defense evasion analysis, log tampering detection, or MITRE ATT&CK TA0005.
implementing-microsegmentation-with-guardicore
Implementing microsegmentation using Akamai Guardicore Segmentation to map application dependencies, create granular network policies, visualize east-west traffic flows, and enforce least-privilege communication between workloads across data centers and cloud.
ramdisk-tmpfs-patterns
暂无简介