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AC wechat-manager

微信智能管家：自动回复、消息分类、聊天记录分析、联系人管理。Trigger on: 微信, WeChat, 群聊, 朋友圈, 公众号, 聊天记录, 联系人, 表情包, 小程序, 红包.

ClawHub Agent Skills author: clawto v1.0.0 MIT-0 9 files · 1 script body ≈ 341 tokens Open the sourceclawhub.ai↗ analyzed 9 h ago

微信智能管家：自动回复、消息分类、聊天记录分析、联系人管理。Trigger on: 微信, WeChat, 群聊, 朋友圈, 公众号, 聊天记录, 联系人, 表情包, 小程序, 红包.

As a process C 60/100 · Has gaps — weak spots: result and completion, failures and branches, progress reporting

ProcedureSoftware developmenttype 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
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
C
60/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
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 · 0

    ✓ No critical or high findings

    Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 60/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 15 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 341 tokens
    • 100Running it twice. No mutating operations

    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)
    • +3Description length 92: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • -31 of 6 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 15 items
    • +4Has examples (3 code blocks)
    • +1License stated

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

    External checks

    ClawHub: suspicious
    This WeChat skill is purpose-built for chat management, but it needs Review because it handles private messages, contacts, monitoring, and auto-reply rules without clear consent or safety limits.
    LLM: suspicious (high) · VirusTotal: · 28 May 2026