SKILLEMALL.ai

BD wechat-local-reader

微信本地管家:在桌面电脑(Windows/macOS/Linux)本地读取你本人本机登录的微信加密数据库,自动脱敏(手机号/身份证/银行卡/邮箱)并生成每日工作简报,推送至邮箱/企业微信机器人/任意 HTTP 端口。全程零上传,隐私不出本机。当用户想"让 AI 自动梳理微信里的消息和工作群""每天总结微信内容并推送""把微信聊天记录接入自动化并脱敏"时使用。注意:仅限本人本机微信数据;不支持手机/平板/鸿蒙等移动端。

ClawHub Agent Skills author: GP198922 v1.0.1 MIT-0 39 files · 2 scripts body ≈ 2 719 tokens Open the sourceclawhub.ai analyzed 2 d ago

微信本地管家:在桌面电脑(Windows/macOS/Linux)本地读取你本人本机登录的微信加密数据库,自动脱敏(手机号/身份证/银行卡/邮箱)并生成每日工作简报,推送至邮箱/企业微信机器人/任意 HTTP 端口。全程零上传,隐私不出本机。当用户想"让 AI…

As a process D 39/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
Run on models
none yet
Process rating
D
39/100
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 · 0

✓ No critical or high findings

Files scanned: 39. 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")
  • note frontmatter-key unknown frontmatter key "disable"

Process rating: all ten parameters 39/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (wechat-local-reader) differs from the folder (wechat-local-reader-publish)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 39 steps
  • 100Execution cost. Instruction body is 2719 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • +2Single-language instructions
  • +5Description quotes 3 example trigger phrases
  • +3Description length 210: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 39 items
  • +4Has examples (10 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.

External checks

ClawHub: suspicious
This skill is a local WeChat reader, but it uses highly sensitive key extraction, plaintext caches, broad chat export, and persistent privilege changes that need careful review before installation.
LLM: suspicious (high) · 9 Sept 2026