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BC 小智AI-Xiaozhi Mcp Openclaw Official

按小智官方 MCP 接入方式，把小智 AI 设备通过 MCP 接到 OpenClaw / OpenAI-compatible 后端。适用于已经有小智 MCP 接入点（wss://api.xiaozhi.me/mcp/?token=...）的场景。提供一个 `openclaw_query(message)` MCP 工具，让小智在需要外部能力、复杂推理、联网查询或外部智能辅助时调用。 Official XiaoZhi MCP bridge for OpenClaw / OpenAI-compatible backends. Use when you already have a XiaoZhi MCP endpoint and want XiaoZhi to call an external assistant tool such as `openclaw_query(message)`.

ClawHub Agent Skills author: joe12801 v1.0.2 MIT-0 7 files body ≈ 464 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

IntegrationAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
96
Quality 40%
79
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 4

    ✓ No critical or high findings

    Medium and low: 4
    • low Exfiltration read-dotenv README.md:32
      Reads a .env file
      cp .env.example .env
    • low Exfiltration read-dotenv README.md:82
      Reads a .env file
      cp .env.example .env
    • low Exfiltration read-dotenv SKILL.md:40
      Reads a .env file
      cp .env.example .env
    • low Exfiltration read-dotenv SKILL.md:92
      Reads a .env file
      cp .env.example .env

    Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)

    Process rating: all ten parameters 52/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (小智AI-Xiaozhi Mcp Openclaw Official) differs from the folder (xiaozhi-mcp-openclaw-official)
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 34 steps
    • 100Execution cost. Instruction body is 464 tokens

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 399: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 34 items
    • +4Has examples (8 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

    External checks

    ClawHub: suspicious
    This is a real XiaoZhi-to-OpenAI bridge, but it needs review because it can forward voice/tool traffic and secrets through external services with broad scope and limited privacy controls.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026