SKILLEMALL.ai

AC cooplens-skill

Analyze Chinese-foreign cooperative undergraduate programs for parents. The skill verifies official sources in real time, estimates admission rank ranges including new/no-history projects, synthesizes anonymous public-discussion concerns without exposing platforms or identities, analyzes CSCSE / 教育部留学服务中心 authentication paths, searches overseas-city living costs when an abroad stage is possible, extracts parent-facing risk questions, produces Markdown with a table of contents, generates colorful mobile-first static HTML with native HTML and CSS, uses task-based artifact filenames, and separates 个性化推荐度评价(学生/家庭适配) from 项目综合实力推荐度评价(项目综合实力角度).

ClawHub Agent Skills author: c-narcissus v1.0.15 MIT-0 18 files body ≈ 7 129 tokens Open the sourceclawhub.ai analyzed 2 d ago

Analyze Chinese-foreign cooperative undergraduate programs for parents.

As a process C 58/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice

AnalyzerData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
C
58/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 17. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 7129 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 58/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 6 mutating operations with no state check
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Execution cost. Instruction body is 7129 tokens
  • 85Steps. 80 steps, 1 vague phrases
  • 100Failures and branches. 5 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • low The response is described with custom markup (12 tags): a typed call is more reliable

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)
  • +2Single-language instructions
  • +3Description length 647: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 80 items
  • +3Output format is stated explicitly
  • +4Has examples (3 code blocks)
  • +4Reference files are cited in the instructions (11 of 11)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

External checks

ClawHub: clean
CoopLens is a coherent education-analysis skill that uses web research and local report generation in ways that match its stated purpose.
LLM: benign (high) · VirusTotal: · 24 Jun 2026