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AC unreal-mcp

Automate Unreal Engine editor scenes, actors, and renders.

NousResearch/hermes-agent Hermes author: NousResearch MIT 6 files body ≈ 3 336 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Automate Unreal Engine editor scenes, actors, and renders.

As a process C 64/100 · Has gaps — weak spots: result and completion, running it twice

GeneratorBlenderSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: Hermes Agent
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
Run on models
none yet
Process rating
C
64/100
Has gaps
Result and completion w 14
0
Running it twice w 4
30
Tools and files w 18
60
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

    • note frontmatter-key unknown frontmatter key "requires"

    Process rating: all ten parameters 64/100

    • 0Result and completion. Does not say what the result is
    • 30Running it twice. 5 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 60Consistency. The Hermes dialect needs category and tags
    • 65Failures and branches. 3 branches
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 41 steps, 1 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Execution cost. Instruction body is 3336 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • +3Description length 58: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • -5TODO / placeholder text left in the skill
    • -2localhost URLs: will not work for another user
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 41 items
    • +4Reference files are cited in the instructions (5 of 5)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.