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BD wechat-miniprogram-toolkit

微信小程序全栈开发 skill，支持项目初始化、云开发（数据库/存储/云函数/聚合查询/事务）、用户登录鉴权、微信支付（JSAPI/统一下单/支付通知/退款）、直播/实时音视频（TRTC）、数据分析/埋点、分享海报/朋友圈分享、TypeScript 泛型封装、云托管（容器化后端）、客服消息、订阅消息、客服自动回复、内容安全（文本/图片/音视频审核）、小程序互跳（APP↔小程序/URL Scheme/扫码）、硬件能力（蓝牙/GPS/NFC/Wi-Fi/扫码）、Skyline 高性能渲染、WXS 脚本、性能优化、CI/CD 流水线、代码分包（每个包小于 2MB）。

ClawHub Agent Skills author: moniq888 v1.0.0 MIT-0 21 files body ≈ 3 529 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 45/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructureSoftware 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
B
89/100
safety, quality, tests
Safety 60%
97
Quality 40%
78
Run on models
none yet
Process rating
D
45/100
Unfinished process
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

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Secrets in code secret-private-key references/ci-cd.md:60
    Private key material (placeholder value)
    下载后，将私钥文件内容（`-----BEGIN PRIVATE KEY----- …
    placeholder
  • low Exfiltration exfil-secret-in-url references/messaging.md:323
    Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (placeholder value)
    url: `https://api.weixin.qq.com/cgi-bin/message/subscribe/send?access_token=…
    placeholder
  • low Exfiltration exfil-secret-in-url references/messaging.md:355
    Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (placeholder value)
    url: `https://api.weixin.qq.com/cgi-bin/token?grant_type=…&appid=…&secret=…
    placeholder

Files scanned: 21. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 45/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 60Tools and files. Uses tools (python, node) that frontmatter does not declare
  • 100Steps. 30 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3529 tokens
  • 100Progress reporting. Reports progress
  • low 14 top-level sections: this looks like several domains in one skill

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
  • -238 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 283: enough signal without eating the budget
  • +4Structure: 37 headings
  • +3Step-by-step instructions: 30 items
  • +4Has examples (14 code blocks)
  • +4Reference files are cited in the instructions (18 of 18)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

External checks

ClawHub: suspicious
The skill is a coherent WeChat mini-program development toolkit, but it needs Review because several examples handle payments, identity, analytics, Wi-Fi credentials, deployment, and skill self-updates without enough safeguards.
LLM: suspicious (high) · VirusTotal: · 17 Aug 2026