SKILLEMALL.ai

AD illustration-halftone-line

Draws hand-authored SVG spot illustrations in a halftone line style for design and coding scenes. Identifying traits are a confident black line with a slight hand wobble and contours left open, grey tones made only of fine halftone dots clipped to shapes, a few solid black masses (hair, leaves, a bag, a black tee), warm off-white paper and no colour at all. Covers the two-ink palette, line weights, the open-contour and stipple techniques, a seeded compiler that turns tone tags into dot fills, rubbery long-limbed characters and expressive hands, doodles, floor pools and a render-and-critique loop. Use when someone asks for halftone, stipple, dotted, risograph-like, newsprint, black-and-white, monochrome line, editorial line or zine-style illustrations, or for empty states, onboarding, 404 pages, blog headers, feature spots and marketing cards in that look.

MengTo/Skills Agent Skills author: MengTo MIT 8 files · 3 scripts body ≈ 4 467 tokens Open the sourcegithub.com↗ analyzed 4 d ago

Draws hand-authored SVG spot illustrations in a halftone line style for design and coding scenes.

As a process D 47/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureDesignSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
D
47/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    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: 8. 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 47/100

    • 0Result and completion. Does not say what the result is
    • 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. 3 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web, node) that frontmatter does not declare
    • 70Execution cost. Instruction body is 4467 tokens
    • 100Steps. 70 steps
    • 100Consistency. Name and required fields are in place
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 11 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (11 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)
    • +3Description length 867: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 70 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 3 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.