Zhiyong AI Product and Technology Agent

{
  "name": "Zhiyong AI Product and Technology Agent",
  "protocolVersion": "0.3.0",
  "url": "https://kg.zhiyong.dev/a2a",
  "preferredTransport": "JSONRPC",
  "additionalInterfaces": [
    {
      "url": "https://kg.zhiyong.dev/a2a",
      "transport": "JSONRPC"
    }
  ],
  "description": "A focused conversational service agent for Zhiyong's practical tools and public AI knowledge graph. It detects English, Simplified Chinese, and Arabic from each message and reports the response language accurately. It moves conversations toward a concrete workflow, relevant product, evidence-backed technology discovery, or specific feedback, and briefly redirects unrelated tangents. It supports A2A 1.0 and the Taifoon-compatible A2A 0.3 message/send method on the same endpoint. A narrowly identified reachability probe (the documented exact probe, explicit metadata.probe=true, or a TaifoonHarvester version with the taifoon-probe/2 marker) receives a literal READY receipt. This confirms A2A message handling only, not LLM, database, KG, or end-to-end health. It drafts shareable copy only when requested or clearly relevant to the user's stated communication goal, never publishes it. It keeps short-lived follow-up context for up to 12 hours. A private audit copy of completed A2A exchanges is available only to authorized administrators for 30 days; known health probes are excluded, and the context ID is stored only as a one-way hash. It never writes public posts, submits leads, contacts vendors, or changes the KG.",
  "conversationAudit": {
    "enabled": true,
    "purpose": "Quality and intent-handling audit",
    "retentionDays": 30,
    "access": "Authorized administrators only through the analytics dashboard",
    "contextId": "one-way hash only; raw bearer contextId is not stored in the audit index",
    "excludes": [
      "documented health probes",
      "protocol-level requests without a completed reply"
    ]
  },
  "supportedInterfaces": [
    {
      "url": "https://kg.zhiyong.dev/a2a",
      "protocolBinding": "JSONRPC",
      "protocolVersion": "1.0"
    },
    {
      "url": "https://kg.zhiyong.dev/a2a",
      "protocolBinding": "JSONRPC",
      "protocolVersion": "0.3"
    }
  ],
  "provider": {
    "organization": "Zhiyong.dev",
    "url": "https://zhiyong.dev"
  },
  "version": "1.0.0",
  "documentationUrl": "https://kg.zhiyong.dev/for-ai",
  "iconUrl": "https://kg.zhiyong.dev/zhiyong-ai-icon.png",
  "capabilities": {
    "streaming": false,
    "pushNotifications": false,
    "stateTransitionHistory": false,
    "extensions": []
  },
  "supportsAuthenticatedExtendedCard": false,
  "defaultInputModes": [
    "text/plain"
  ],
  "defaultOutputModes": [
    "application/json",
    "text/plain"
  ],
  "skills": [
    {
      "id": "zhiyong-agent-conversation",
      "name": "Zhiyong product and AI conversation",
      "description": "Understand the user's practical goal from natural language and recent A2A context. Answer product and workflow questions from the public product catalogue, in English, Simplified Chinese, or Arabic. Keep follow-ups connected to prior turns, redirect unrelated tangents briefly, and offer one relevant next step. A documented, explicitly marked, or TaifoonHarvester taifoon-probe/2 readiness check receives only a READY receipt and does not test downstream dependencies. Completed A2A exchanges are stored privately for quality audit for 30 days and are visible only to authorized administrators; known probes are excluded. Draft share text only when requested or clearly relevant to the user's stated communication goal; never post or contact anyone on the user's behalf.",
      "tags": [
        "AI",
        "knowledge graph",
        "technology selection",
        "models",
        "tools",
        "deployment"
      ],
      "examples": [
        "What does Zhiyong offer for a small team trying to manage a weekly workflow?",
        "I found two promising AI tools. Help me identify one important follow-up question before I test them."
      ],
      "inputModes": [
        "text/plain"
      ],
      "outputModes": [
        "application/json",
        "text/plain"
      ]
    },
    {
      "id": "ai-technology-discovery",
      "name": "AI technology discovery and evidence gaps",
      "description": "Find and compare public AI model, tool, and workflow records in Zhiyong's knowledge graph using natural-language needs and constraints. Return relevant candidates, public evidence and known unknowns, then suggest a bounded validation step. Candidate listings are not proof of fit, performance, licensing, or compliance. Follow-up questions can use the preceding A2A context; the service does not change KG records or contact vendors.",
      "tags": [
        "AI",
        "knowledge graph",
        "technology selection",
        "models",
        "tools",
        "OCR",
        "RAG",
        "TTS",
        "local deployment",
        "evidence",
        "evaluation"
      ],
      "examples": [
        "We need Chinese OCR for scanned PDFs, but customer data must stay on our servers. What should we evaluate?",
        "Which of these candidates explicitly support offline use, and what evidence is still missing?"
      ],
      "inputModes": [
        "text/plain"
      ],
      "outputModes": [
        "application/json",
        "text/plain"
      ]
    }
  ]
}