SKILLEMALL.ai

AC code-language-analyzer

项目编程语言代码量分析专家。扫描项目目录,统计各编程语言的文件数、代码行数及占比,生成可视化的语言分布报告。当用户需要分析项目使用了哪些语言、各语言代码量占比、技术栈构成、代码行数统计时触发此技能。典型场景包括:分析项目语言占比、统计代码行数、看看这个项目用了哪些语言、分析技术栈、code language analysis 等。

ClawHub Agent Skills author: zmgood v1.0.0 MIT-0 3 files body ≈ 931 tokens Open the sourceclawhub.ai analyzed 2 d ago

项目编程语言代码量分析专家。扫描项目目录,统计各编程语言的文件数、代码行数及占比,生成可视化的语言分布报告。当用户需要分析项目使用了哪些语言、各语言代码量占比、技术栈构成、代码行数统计时触发此技能。典型场景包括:分析项目语言占比、统计代码行数、看看这个项目用了哪些语言、分析技术栈、code language…

As a process C 57/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

AnalyzerSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
C
57/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
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: 3. 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 57/100

  • 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
  • 40Consistency. Frontmatter name (code-language-analyzer) differs from the folder (code-language-analyzer-claw)
  • 60Result and completion. Output format stated, no completion criterion
  • 100Tools and files. No external tools needed
  • 100Steps. 21 steps
  • 100Execution cost. Instruction body is 931 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

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +1No license
  • +2Single-language instructions
  • +3Description length 166: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 21 items
  • +3Output format is stated explicitly
  • +4Has examples (6 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

External checks

ClawHub: clean
This skill is a straightforward local code-language counter that reads a user-chosen project folder and prints aggregate language statistics.
LLM: benign (high) · VirusTotal: · 3 Jul 2026