[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M73F95GGWB6PBMAZCF6T62AN":3,"skill-same:01M73F95GGWB6PBMAZCF6T62AN":47},{"skillNo":4,"name":5,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":18,"linkOnly":19,"markdownContent":20,"markdownBody":21,"frontMatter":22,"refName":23,"commitSha":24,"skillDirectory":25,"forksCount":26,"repositoryHtmlUrl":27,"repositoryHomepageUrl":28,"ownerHtmlUrl":29,"ownerBlogUrl":30,"ownerDescription":31,"updatedAt":32,"install":33,"resources":46},"01M73F95GGWB6PBMAZCF6T62AN","protobufjs-dynamic-schema",null,"Horonomos-laps\u002FYana-AI","Horonomos-laps","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F355299122?v=4","01M76B3QPXE88ET2SQPDPVATZT"," Horonomos-laps","01M72ZQRJPCYPSSWT4D00VJR2A",13,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fprotobufjs-dynamic-schema\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: protobufjs-dynamic-schema\ndescription: protobuf.js dynamic .proto parsing and code generation for Node.js. Runtime schema loading, static code generation, reflection API, and JSON interop for agent message contracts. Sources: protobufjs\u002Fprotobuf.js (BSD-3-Clause).\norigin: yana-ai — synthesized from protobufjs\u002Fprotobuf.js (BSD-3-Clause)\nlicense: Apache-2.0\nversion: 1.0.0\ncompatibility: yana-ai >= 1.3.50\n---\n\n# \u002Fprotobufjs-dynamic-schema\n\n## When to Use\n\n- Load .proto schemas dynamically at agent startup without build-time codegen\n- Generate TypeScript interfaces from .proto for type-safe message handling\n- Reflection: enumerate all message fields at runtime for generic serializers\n- JSON ↔ Protobuf interop: accept JSON from external APIs, emit Protobuf internally\n\n## Do NOT use for\n\n- Performance-critical paths (static generated code is 2× faster than dynamic loading)\n- gRPC service definitions (use [[grpc-streaming-rpc]] which wraps protobufjs)\n\n---\n\n## Dynamic load and use\n\n```javascript\nimport protobuf from 'protobufjs'\n\n\u002F\u002F Load at startup, cache root — never reload per message\nconst root = await protobuf.load(['agent_message.proto', 'task.proto'])\n\nconst AgentMessage = root.lookupType('yamtam.AgentMessage')\nconst TaskPayload  = root.lookupType('yamtam.TaskPayload')\n\nexport function encodeAgentMessage(data: object): Buffer {\n  const err = AgentMessage.verify(data)\n  if (err) throw new Error(`[protobuf] ${err}`)\n  return Buffer.from(AgentMessage.encode(AgentMessage.create(data)).finish())\n}\n\nexport function decodeAgentMessage(buf: Buffer): object {\n  const msg = AgentMessage.decode(buf)\n  return AgentMessage.toObject(msg, {\n    longs:   String,   \u002F\u002F int64 → string (avoids JS precision loss)\n    enums:   String,   \u002F\u002F enum number → name\n    bytes:   String,   \u002F\u002F bytes → base64 string\n    defaults: true,    \u002F\u002F include fields with default values\n  })\n}\n```\n\n---\n\n## Static codegen (for production)\n\n```bash\n# Generate JS + TypeScript declarations\nnpx pbjs  -t static-module -w es6 -o src\u002Fgenerated\u002Fprotos.js  *.proto\nnpx pbts  -o src\u002Fgenerated\u002Fprotos.d.ts  src\u002Fgenerated\u002Fprotos.js\n\n# Generated usage (2× faster than dynamic, fully typed)\nimport { yamtam } from '.\u002Fgenerated\u002Fprotos'\n\nconst msg = yamtam.AgentMessage.create({ agentId: 'x', seq: 1 })\nconst buf = yamtam.AgentMessage.encode(msg).finish()\n```\n\n---\n\n## Reflection API (enumerate message fields)\n\n```javascript\nconst AgentMessage = root.lookupType('yamtam.AgentMessage')\n\n\u002F\u002F List all fields\nfor (const field of AgentMessage.fieldsArray) {\n  console.log(`  ${field.id}: ${field.name} (${field.type})`)\n}\n\n\u002F\u002F Check if a field exists by name\nconst hasPayload = !!AgentMessage.fields['payload']\n\n\u002F\u002F Nested message types\nconst nested = AgentMessage.resolvedFields\n  .filter(f => f.resolvedType instanceof protobuf.Type)\n  .map(f => f.resolvedType.name)\n```\n\n---\n\n## JSON ↔ Protobuf conversion\n\n```javascript\n\u002F\u002F JSON from external API → Protobuf binary\nfunction jsonToProto(jsonObj: object): Buffer {\n  const msg = AgentMessage.fromObject(jsonObj)   \u002F\u002F lenient: coerces types\n  const err = AgentMessage.verify(msg)\n  if (err) throw new Error(`[protobuf] ${err}`)\n  return Buffer.from(AgentMessage.encode(msg).finish())\n}\n\n\u002F\u002F Protobuf binary → JSON (for logging\u002Fdebugging)\nfunction protoToJson(buf: Buffer): string {\n  const msg = AgentMessage.decode(buf)\n  return JSON.stringify(AgentMessage.toObject(msg, { longs: String, enums: String }))\n}\n```\n\n---\n\n## Anti-Fake-Pass Checklist\n\n```\n❌ protobuf.load() called per message → filesystem + parse overhead on every encode\n❌ AgentMessage.create() skipped → encode() accepts plain objects but skips field defaults\n❌ longs: Number in toObject → int64 precision loss above Number.MAX_SAFE_INTEGER\n❌ Dynamic schema in production hot path → use static codegen (pbjs) for 2× throughput\n❌ lookupType() throws if package prefix wrong — use full name: 'yamtam.AgentMessage' not 'AgentMessage'\n❌ Missing .proto file at load path → protobuf.load() throws, no fallback\n```\n","# \u002Fprotobufjs-dynamic-schema\n\n## When to Use\n\n- Load .proto schemas dynamically at agent startup without build-time codegen\n- Generate TypeScript interfaces from .proto for type-safe message handling\n- Reflection: enumerate all message fields at runtime for generic serializers\n- JSON ↔ Protobuf interop: accept JSON from external APIs, emit Protobuf internally\n\n## Do NOT use for\n\n- Performance-critical paths (static generated code is 2× faster than dynamic loading)\n- gRPC service definitions (use [[grpc-streaming-rpc]] which wraps protobufjs)\n\n---\n\n## Dynamic load and use\n\n```javascript\nimport protobuf from 'protobufjs'\n\n\u002F\u002F Load at startup, cache root — never reload per message\nconst root = await protobuf.load(['agent_message.proto', 'task.proto'])\n\nconst AgentMessage = root.lookupType('yamtam.AgentMessage')\nconst TaskPayload  = root.lookupType('yamtam.TaskPayload')\n\nexport function encodeAgentMessage(data: object): Buffer {\n  const err = AgentMessage.verify(data)\n  if (err) throw new Error(`[protobuf] ${err}`)\n  return Buffer.from(AgentMessage.encode(AgentMessage.create(data)).finish())\n}\n\nexport function decodeAgentMessage(buf: Buffer): object {\n  const msg = AgentMessage.decode(buf)\n  return AgentMessage.toObject(msg, {\n    longs:   String,   \u002F\u002F int64 → string (avoids JS precision loss)\n    enums:   String,   \u002F\u002F enum number → name\n    bytes:   String,   \u002F\u002F bytes → base64 string\n    defaults: true,    \u002F\u002F include fields with default values\n  })\n}\n```\n\n---\n\n## Static codegen (for production)\n\n```bash\n# Generate JS + TypeScript declarations\nnpx pbjs  -t static-module -w es6 -o src\u002Fgenerated\u002Fprotos.js  *.proto\nnpx pbts  -o src\u002Fgenerated\u002Fprotos.d.ts  src\u002Fgenerated\u002Fprotos.js\n\n# Generated usage (2× faster than dynamic, fully typed)\nimport { yamtam } from '.\u002Fgenerated\u002Fprotos'\n\nconst msg = yamtam.AgentMessage.create({ agentId: 'x', seq: 1 })\nconst buf = yamtam.AgentMessage.encode(msg).finish()\n```\n\n---\n\n## Reflection API (enumerate message fields)\n\n```javascript\nconst AgentMessage = root.lookupType('yamtam.AgentMessage')\n\n\u002F\u002F List all fields\nfor (const field of AgentMessage.fieldsArray) {\n  console.log(`  ${field.id}: ${field.name} (${field.type})`)\n}\n\n\u002F\u002F Check if a field exists by name\nconst hasPayload = !!AgentMessage.fields['payload']\n\n\u002F\u002F Nested message types\nconst nested = AgentMessage.resolvedFields\n  .filter(f => f.resolvedType instanceof protobuf.Type)\n  .map(f => f.resolvedType.name)\n```\n\n---\n\n## JSON ↔ Protobuf conversion\n\n```javascript\n\u002F\u002F JSON from external API → Protobuf binary\nfunction jsonToProto(jsonObj: object): Buffer {\n  const msg = AgentMessage.fromObject(jsonObj)   \u002F\u002F lenient: coerces types\n  const err = AgentMessage.verify(msg)\n  if (err) throw new Error(`[protobuf] ${err}`)\n  return Buffer.from(AgentMessage.encode(msg).finish())\n}\n\n\u002F\u002F Protobuf binary → JSON (for logging\u002Fdebugging)\nfunction protoToJson(buf: Buffer): string {\n  const msg = AgentMessage.decode(buf)\n  return JSON.stringify(AgentMessage.toObject(msg, { longs: String, enums: String }))\n}\n```\n\n---\n\n## Anti-Fake-Pass Checklist\n\n```\n❌ protobuf.load() called per message → filesystem + parse overhead on every encode\n❌ AgentMessage.create() skipped → encode() accepts plain objects but skips field defaults\n❌ longs: Number in toObject → int64 precision loss above Number.MAX_SAFE_INTEGER\n❌ Dynamic schema in production hot path → use static codegen (pbjs) for 2× throughput\n❌ lookupType() throws if package prefix wrong — use full name: 'yamtam.AgentMessage' not 'AgentMessage'\n❌ Missing .proto file at load path → protobuf.load() throws, no 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Use for read-only basket comparison and gap analysis without investment advice or live trading.","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fito-basket-compare\u002FSKILL.md",[],[],{"skillNo":64,"name":65,"nameZh":6,"description":66,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":67,"sourceHtmlUrl":68,"categories":69,"keywords":70,"publishedAt":18},"01M734CYEMZFDR3KH6SW0JP8TD","implementing-network-traffic-baselining","Build network traffic baselines from NetFlow\u002FIPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly\u002Fdaily traffic pattern profiling","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FSKILL.md",[],[],{"skillNo":72,"name":73,"nameZh":6,"description":74,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":75,"sourceHtmlUrl":76,"categories":77,"keywords":78,"publishedAt":18},"01M73ASZ6NJRJM44Z2P84NGQ3Q","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.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FSKILL.md",[],[],{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":83,"sourceHtmlUrl":84,"categories":85,"keywords":86,"publishedAt":18},"01M734AFNXD06Z78QRK0VKGT7Q","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.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FSKILL.md",[],[],{"skillNo":88,"name":89,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":90,"categories":91,"keywords":92,"publishedAt":18},"01M736TK27QMCXK0DVCH25SJN5","ramdisk-tmpfs-patterns","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Framdisk-tmpfs-patterns\u002FSKILL.md",[],[]]