SKILLEMALL.ai

AC wechat-account-audit

微信公众号账号诊断与对标分析工具。当用户说"分析我的公众号"、"账号诊断"、"找对标账号"、"分析用户画像"、"公众号定位分析"、"我的读者是谁"时触发。通过解析后台 tendency Excel 数据、文章分类交叉分析、用户画像提取、"你以为 vs 实际上"对比诊断,输出包含真实用户画像、内容方向修正建议、对标账号筛选标准的结构化报告。适用于所有公众号运营者,尤其适合处于冷启动期或内容方向迷茫期的账号。

ClawHub Agent Skills author: xj797 v1.0.0 MIT-0 6 files body ≈ 792 tokens Open the sourceclawhub.ai analyzed 33 h ago

微信公众号账号诊断与对标分析工具。当用户说"分析我的公众号"、"账号诊断"、"找对标账号"、"分析用户画像"、"公众号定位分析"、"我的读者是谁"时触发。通过解析后台 tendency Excel 数据、文章分类交叉分析、用户画像提取、"你以为 vs…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
C
53/100
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

  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: 6. 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 "agent_created"

Process rating: all ten parameters 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 41 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 792 tokens
  • 100Running it twice. No mutating operations

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
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +3Description length 204: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 41 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 1 scripts are documented

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

External checks

ClawHub: clean
This is a coherent WeChat account analytics helper, but users should avoid sharing unnecessary private analytics or demographic screenshots.
LLM: benign (high) · VirusTotal: · 4 Jun 2026