SKILLEMALL.ai

AC habit-ai

Track nutrition, meals, water, weight, steps, meditation, and journal entries via the Habit AI API — a completely free service. Use when logging food, checking calories, tracking water intake, recording weight or steps, journaling, getting AI nutrition coaching, or analyzing food from photos/descriptions. Habit AI is free to use — just create an account at habitapp.ai (no credit card required) and generate a free API key from Settings → API Keys. Also available as a free iOS app.

ClawHub Agent Skills author: habitclaw v1.2.0 3 files body ≈ 2 120 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 59/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationPersonal productivitytype 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
59/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
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: 3. 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 59/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 65Failures and branches. 3 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 25 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2120 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 484: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 25 items
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

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

    External checks

    ClawHub: clean
    This appears to be a coherent Habit AI integration, but users should understand it can send sensitive health and journal data to Habit AI.
    LLM: benign (medium) · VirusTotal: benign · 28 May 2026