SKILLEMALL.ai

AB wc-player-ev

Computes one player's expected FIFA World Cup Fantasy points for a single round (xEV) from a scout signal — folding start probability, the 60-minute appearance cliff, fixture-and-minutes-scaled npxG/xA, clean-sheet and defensive-action floors, the penalty/set-piece premium, and the card/concede downside into one number plus its full decomposition, variance, ceiling, and floor. This is the atomic EV unit the whole system samples from. Use whenever a single player's per-round point distribution is needed — called by wc-scout, every wc-strategist genotype, wc-matchday-tactician, and wc-fitness-eval (which sums these into raw_xEV). Reads the scout signal; never re-scouts. Emits a player-ev signal.

lyndonkl/claude Agent Skills author: lyndonkl 1 file body ≈ 4 577 tokens Open the sourcegithub.com↗ analyzed 16 h ago

Computes one player's expected FIFA World Cup Fantasy points for a single round (xEV) from a scout signal — folding start probability, the 60-minute…

As a process B 76/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

ProcedureData and analyticsSecuritySoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
B
76/100
Nearly there
Progress reporting w 2
0
Inputs and preconditions w 11
30
Running it twice w 4
30
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 76/100

    • 0Progress reporting. Says nothing while it works
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 1 mutating operations with no state check
    • 60Result and completion. Output format stated, no completion criterion
    • 60Failures and branches. 2 branches
    • 70Execution cost. Instruction body is 4577 tokens
    • 100Tools and files. No external tools needed
    • 100Steps. 21 steps
    • 100When it triggers. States when to use and when not to
    • 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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 702: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 21 items
    • +3Output format is stated explicitly
    • +4Has examples (10 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.