SKILLEMALL.ai

BD 微信公众号文章深度分析

微信公众号文章深度分析工具。 当用户发送微信公众号文章链接时,可以读取文章内容并进行深度分析。 功能:自动提取文章标题和正文、提取时间线、识别关键人物/公司、提取核心事实、进行主题分析、生成报告。 支持输出格式:Markdown 报告、OpenCLI 适配器、JSON 数据。

ClawHub Agent Skills author: teenyboy v1.0.1 MIT-0 5 files body ≈ 398 tokens Open the sourceclawhub.ai analyzed 3 d ago

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

ReferenceInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
69
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: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "triggers"

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 (微信公众号文章深度分析) differs from the folder (wechat-article-analyzer)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 23 steps
  • 100Execution cost. Instruction body is 398 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
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 138: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 23 items
  • +4Has examples (3 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: clean
This appears to be a WeChat article analysis helper with expected network fetching and report output, but it has dependency and trigger-scope hygiene issues to review.
LLM: benign (medium) · VirusTotal: · 29 May 2026