SKILLEMALL.ai
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AC dream-loop

Build stunning 3D scenes via a concept-art fidelity loop.

NousResearch/hermes-agent Hermes author: NousResearch MIT 2 files body ≈ 3 359 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Build stunning 3D scenes via a concept-art fidelity loop.

As a process C 63/100 · Has gaps — weak spots: when it triggers, running it twice

GeneratorBlenderAI and agentsSoftware 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
63/100
Has gaps
When it triggers w 12
20
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "dependencies"

    Process rating: all ten parameters 63/100

    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 3 mutating operations with no state check
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 60Consistency. The Hermes dialect needs category and tags
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Failures and branches. 5 branches
    • 85Steps. 26 steps, 2 vague phrases
    • 100Execution cost. Instruction body is 3359 tokens
    • 100Progress reporting. Reports progress

    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)
    • +3Description length 57: 120–800 characters recommended
    • +4No input/output examples
    • -2localhost URLs: will not work for another user
    • +2Single-language instructions
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 26 items
    • +3Output format is stated explicitly
    • +1License stated

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