SKILLEMALL.ai

AC commute-carbon-counter

Use when you want to know the real carbon footprint of your daily commute or travel habits, when deciding between commuting modes (car vs transit vs bike vs carpool vs EV), when tracking a personal emissions goal, or when preparing an environmental impact report — logs every trip (mode, distance, passengers), computes kg CO2 per trip/week/month using per-passenger-km emission factors for 14 transport modes, compares against your car baseline, projects your annual trajectory, and shows what changed your footprint most.

ClawHub Agent Skills author: voronindenis5 v1.0.0 MIT-0 6 files body ≈ 1 463 tokens Open the sourceclawhub.ai analyzed 3 d ago

Use when you want to know the real carbon footprint of your daily commute or travel habits, when deciding between commuting modes (car vs transit vs bike vs…

As a process C 56/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
C
56/100
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: 6. 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 56/100

    • 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
    • 30Running it twice. 2 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 100Tools and files. No external tools needed
    • 100Steps. 16 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1463 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
    • +3Output format is not stated: the model decides each time
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 523: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 16 items
    • +4Has examples (5 code blocks)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: clean
    This skill is a local commute-emissions calculator that stores user-entered trip data in a disclosed local JSON file and does not show hidden network, credential, or persistence behavior.
    LLM: benign (high) · VirusTotal: · 8 Sept 2026