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BB live-dashboard

Build self-updating dashboards from live sources.

NousResearch/hermes-agent Hermes author: NousResearch MIT 1 file body ≈ 1 866 tokens Open the sourcegithub.com↗ analyzed 13 h ago

Build self-updating dashboards from live sources.

As a process B 71/100 · Nearly there — weak spots: result and completion, running it twice

GeneratorLogistics and warehouseData and analyticstype and topics are labelled automatically from the skill text
Runs in: Hermes Agent
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
86
Run on models
none yet
Process rating
B
71/100
Nearly there
Running it twice w 4
30
Result and completion w 14
40
When it triggers w 12
50
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: 1. 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 71/100

    • 30Running it twice. 4 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Consistency. The Hermes dialect needs category and tags
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 25 steps
    • 100Execution cost. Instruction body is 1866 tokens
    • 100Progress reporting. Reports progress
    • low The response is described with custom markup (5 tags): a typed call is more reliable

    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 49: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 25 items
    • +4Has examples (1 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.