SKILLEMALL.ai

AD pixel-art-processing

Pixel art sprite sheet processing tool — video frame extraction, GIF/frames conversion, sprite sheet compose/split, image matting, pixelation, resize, crop, and watermark removal. Use when processing pixel art, game assets, RPG Maker sprites, or any sprite sheet workflow. Triggers on: sprite sheet, GIF拆帧, 序列帧, 像素图片, 抠图, 去水印, 视频转帧, pixel art, sprite, GIF to frames, frames to GIF, background removal, pixelate.

ClawHub Agent Skills author: anlinxi v1.0.0 MIT-0 9 files body ≈ 1 215 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

GeneratorInfrastructureMedia and videotype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
D
49/100
Unfinished process
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
    • 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: 9. 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 49/100

    • 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
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 16 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1215 tokens
    • low 13 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
    • -2localhost URLs: will not work for another user
    • -213 emoji in the instructions: noise for the model
    • -32 of 2 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 411: enough signal without eating the budget
    • +4Structure: 23 headings
    • +3Step-by-step instructions: 16 items
    • +4Has examples (8 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.

    External checks

    ClawHub: suspicious
    This pixel-art tool is mostly coherent, but it asks users to run backend tooling that can execute unreviewed local code and affect unrelated local services.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026