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AC build-rigged-game-assets

Create, integrate, or audit production-ready rigged 3D characters and monsters with a main model, skeleton, animation library, sockets, collision contracts, separate character equipment, optimized runtime files, catalog media, and an interactive moveset inspector. Use when generating or importing FBX/GLB game actors; defining character-versus-monster asset requirements; separating swords, shields, helmets, armor, or stowed gear; adding idle, walk, run, attack, hit, dodge, block, cast, or death clips; wiring an asset into gameplay; or adding action and equipment controls plus deterministic tests to a game asset catalog.

MengTo/Skills Agent Skills author: MengTo MIT 7 files · 1 script body ≈ 1 769 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Create, integrate, or audit production-ready rigged 3D characters and monsters with a main model, skeleton, animation library, sockets, collision contracts…

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationInfrastructuretype 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
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
54/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
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: 7. 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
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 9 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 100Steps. 58 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1769 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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 626: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 58 items
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 1 scripts are documented

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