SKILLEMALL.ai

BD wechat-chat-export-mac

当用户要把微信里某个联系人、某段时间的文件图片视频批量下载到本地时用。指定联系人+日期范围,自动定位会话、批量点开原图、V2三段解密转码、按项目归档并验收缺口为0;实战单次导出1216文件163.9MB。适用微信桌面版4.x,仅macOS。

ClawHub Hermes author: lingyu9495-source v1.0.0 MIT-0 9 files · 5 scripts body ≈ 1 855 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

当用户要把微信里某个联系人、某段时间的文件图片视频批量下载到本地时用。指定联系人+日期范围,自动定位会话、批量点开原图、V2三段解密转码、按项目归档并验收缺口为0;实战单次导出1216文件163.9MB。适用微信桌面版4.x,仅macOS。

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureNotionSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
D
46/100
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. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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

  • warning description-long-hermes description is 120 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"

Process rating: all ten parameters 46/100

  • 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
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 25 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1855 tokens
  • 100Running it twice. No mutating operations
  • low 13 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
  • +2Single-language instructions
  • +3Description length 120: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 25 items
  • +4Has examples (10 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 5 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

External checks

ClawHub: suspicious
This is a powerful WeChat export and decryption workflow that is mostly upfront about its goal, but it needs Review because it handles private chat archives, decryption material, and desktop automation with weak scoping and confirmations.
LLM: suspicious (high) · 5 Oct 2026