SKILLEMALL.ai
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BC asset-gen

Generate game assets, locally and free by default with godogen_assets: images and transparent PNGs (Qwen-Image), textured GLB models (TRELLIS.2), rigged humanoids with generated moves and lip-synced speech, sound effects and voice lines. Paid APIs cover what the local tools can't make — Gemini / xAI Grok images, animated sprites from video, Tripo 3D, Gemini TTS voice acting, Lyria music. Use whenever a game needs generated art or audio.

htdt/godogen Agent Skills author: htdt MIT 10 files · 5 scripts body ≈ 1 296 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Generate game assets, locally and free by default with godogenassets: images and transparent PNGs (Qwen-Image), textured GLB models (TRELLIS.2), rigged…

As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

GeneratorBlenderWriting and documentsMedia and videotype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
C
52/100
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
    • 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: 10. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "display_name"
    • note frontmatter-key unknown frontmatter key "short_description"
    • note frontmatter-key unknown frontmatter key "default_prompt"
    • note frontmatter-key unknown frontmatter key "allow_implicit_invocation"

    Process rating: all ten parameters 52/100

    • 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
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 19 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1296 tokens
    • 100Running it twice. No mutating operations

    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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 440: enough signal without eating the budget
    • +4Structure: 4 headings
    • +3Step-by-step instructions: 19 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.