SKILLEMALL.ai

BD pixel-art

Make modern, polished pixel-art animated videos (MP4) with code — indie-game look (Celeste / Eastward / Stardew-level scenes, original), 480×270 art upscaled ×4 with nearest-neighbour, limited palettes (day / dusk / night) with Bayer dithering, parallax skies, hills, forests and towns, dithered light and weather, auto-outlined procedural pixel characters with 16 expressions, walk cycles, jumps and held props, emote bubbles, dither / diamond / iris / block transitions and crisp RPG dialogue-box captions (Latin + CJK). Canvas 2D in headless Chrome, encoded with ffmpeg. Use when a reference video or brief calls for pixel art, 8-bit / 16-bit, retro game, JRPG, cozy-game or 像素風 / 像素動畫 / 點陣 looks, or when the video-clone router picks the pixel-art style.

edenfunf/reelmimic Agent Skills author: edenfunf 15 files · 8 scripts body ≈ 1 247 tokens Open the sourcegithub.com↗ analyzed 16 h ago

Make modern, polished pixel-art animated videos (MP4) with code — indie-game look (Celeste / Eastward / Stardew-level scenes, original), 480×270 art upscaled…

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorMedia and videotype and topics are labelled automatically from the skill text
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
98
Quality 40%
87
Run on models
none yet
Process rating
D
43/100
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Secrets in code secret-high-entropy-token template/package-lock.json:84
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…eBL+/1tqG…AR5+magt…Y4Q+w==",
      detector
    • low Secrets in code secret-high-entropy-token template/package-lock.json:115
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…kSb+UKYd…IJ4+lp54i/hhCl…p7i+22QhNU0+/qk8g==",
      detector

    Files scanned: 14. 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 43/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 100Steps. 23 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1247 tokens

    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 758: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 23 items
    • +4Has examples (1 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.