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AC wechat-send

Send a message to a contact or group via macOS WeChat desktop client. Use when the user asks to send a WeChat message, message someone on WeChat, or reply to a WeChat contact/group. v2.0 auto mode tries fast locate + verify first; falls back to Agent-assisted mode only when verification fails. Requires macOS, WeChat desktop logged in, Accessibility permission, and cliclick. NOT for reading messages, sending files/images.

ClawHub Agent Skills author: Lnation v2.0.1 MIT-0 3 files · 1 script body ≈ 783 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerAI and agentsInfrastructuretype 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
55/100
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 · 0

    ✓ No critical or high findings

    Files scanned: 3. 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 55/100

    • 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 (wechat-send) differs from the folder (wechat-send-cn)
    • 100Tools and files. No external tools needed
    • 100Steps. 19 steps
    • 100When it triggers. States when to use and when not to
    • 100Execution cost. Instruction body is 783 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
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 424: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 19 items
    • +4Has examples (6 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: suspicious
    This skill does what it says, but it can send real WeChat messages through a logged-in desktop session automatically and relies on fragile GUI/OCR matching without a final confirmation step.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026