SKILLEMALL.ai

AB Fitness

Plans and progresses workout programs — strength, muscle building, cardio, endurance, and general conditioning — with quantified rules. Use when designing a training plan, routine, or split, choosing sets, reps, weights, or heart-rate zones, when a lift, pace, or the scale has plateaued, when sessions were missed or illness, injury, or travel interrupted training, when planning deloads, training at home or with minimal equipment, or reading training logs and wearable readiness data. Not for meal-level nutrition planning or rep-by-rep set logging.

ClawHub Agent Skills author: Iván v1.0.4 MIT-0 14 files body ≈ 3 871 tokens Open the sourceclawhub.ai analyzed 3 d ago

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

GeneratorInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
B
71/100
Nearly there
Inputs and preconditions w 11
0
Running it twice w 4
30
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: 14. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
    • note frontmatter-key unknown frontmatter key "slug"
    • note frontmatter-key unknown frontmatter key "homepage"
    • note frontmatter-key unknown frontmatter key "changelog"

    Process rating: all ten parameters 71/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 7 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Result and completion. Output format stated, no completion criterion
    • 65Failures and branches. 3 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 49 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3871 tokens
    • 100Progress reporting. Reports progress
    • low 13 top-level sections: this looks like several domains in one skill
    • high The skill tells the model to perform an irreversible action with no human approval

    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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 552: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 49 items
    • +3Output format is stated explicitly

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

    External checks

    ClawHub: clean
    This fitness coaching skill is coherent and disclosed, with limited local persistence for training preferences and logs.
    LLM: benign (high) · VirusTotal: · 26 Jul 2026