SKILLEMALL.ai

BC web-technique-to-skill

Turn a visual or interaction technique you already built into a reusable web-design skill, by isolating the one mechanism that makes it work while reproducing its approved reference exactly around that focus, and packaging it with a demo that proves both the mechanism and the visual fidelity. Covers finding the mechanism, naming the technique plainly, disclosing the verified runtime and renderer, auditing reference layers, carrying real numbers instead of adjectives, preserving owned staging, keeping expensive gotchas, declaring the boundary against neighbouring skills, and browser-verifying before claiming it works. Use when a page, canvas scene, shader, scroll effect, layout system, or hover interaction turned out well and should become a skill rather than staying in one project.

MengTo/Skills Agent Skills author: MengTo MIT 6 files body ≈ 4 054 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Turn a visual or interaction technique you already built into a reusable web-design skill, by isolating the one mechanism that makes it work while reproducing…

As a process C 54/100 · 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
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
54/100
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

    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

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 54/100

    • 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
    • 30Running it twice. 6 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 70Execution cost. Instruction body is 4054 tokens
    • 100Tools and files. No external tools needed
    • 100Steps. 64 steps
    • 100Consistency. Name and required fields are in place
    • low 14 top-level sections: this looks like several domains in one skill

    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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 792: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 64 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.