SKILLEMALL.ai

BC wc-ownership-meta

Models the FIFA World Cup Fantasy field — effective ownership, the template set, the differential list, and the rank-protect-vs-gain leverage math — so the system reasons about "what will most managers do?" rather than raw points. Computes effective ownership EO = field_ownership% x (1 + captaincy_share), classifies every owned/missed player into the three regimes (own high-EO haul = hold station, MISS high-EO haul = drop hard, own low-EO haul = leap the field), builds the template set and differential list, flags where the field is over-concentrated (fade ops) and under-reacting (get-there-first), and returns the ownership_leverage contribution under the rank objective θ that wc-fitness-eval folds into fitness. Also computes mini-league rival-relative leverage. Emits an `ownership` signal. Use when called by wc-ownership-analyst, by the strategists weighting picks by leverage, and by wc-fitness-eval scoring the rank term.

lyndonkl/claude Agent Skills author: lyndonkl 1 file body ≈ 4 350 tokens Open the sourcegithub.com↗ analyzed 16 h ago

Models the FIFA World Cup Fantasy field — effective ownership, the template set, the differential list, and the rank-protect-vs-gain leverage math — so the…

As a process C 57/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

Proceduretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
57/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
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: 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 57/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 4 mutating operations with no state check
    • 60Result and completion. Output format stated, no completion criterion
    • 70Execution cost. Instruction body is 4350 tokens
    • 100Tools and files. No external tools needed
    • 100Steps. 23 steps
    • 100Consistency. Name and required fields are in place
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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 936: 120–800 characters recommended
    • +1No license
    • +2Single-language instructions
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 23 items
    • +3Output format is stated explicitly
    • +4Has examples (6 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.