SKILLEMALL.ai

AC display-quantitative-information

Use this skill when the user needs to design, critique, redesign, audit, generate, code, or explain quantitative graphics: charts, dashboards, tables, maps, scientific/statistical figures, visual evidence, or chart specifications. It helps choose display forms, avoid misleading encodings, compute lie factors, inspect CSV structure, generate simple SVG charts, check color contrast, and produce Tufte-informed but non-formulaic recommendations. Do not use for decorative illustration or general data cleaning unless a quantitative display is involved.

ClawHub Agent Skills author: Tristan Manchester v1.0.0 MIT-0 26 files body ≈ 1 491 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 60/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
C
60/100
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

    For the model run — optional
    • 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: 20. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 60/100

    • 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
    • 30Running it twice. 4 mutating operations with no state check
    • 100Tools and files. No external tools needed
    • 100Steps. 24 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1491 tokens
    • medium 6 test cases, all positive: not one "should refuse" or "should ask first"
    • low No test case covers injection arriving through data

    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
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 552: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 24 items
    • +4Reference files are cited in the instructions (9 of 9)
    • +3All 6 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: clean
    The skill is a coherent data-visualization helper with optional local Python utilities and no evidence of credential access, network activity, persistence, or hidden behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026