SKILLEMALL.ai

BB wc-matchday-timing

Builds the structural support for the in-round levers in FIFA World Cup Fantasy — scores how the squad's players are distributed across the round's match days (a good build spreads them, NOT stacked on one day), orders the 4 bench players by kickoff time (latest highest, so they stay promotable after early results land), and generates conditional manual-substitution triggers ("if early starter scores ≤ T and a later bench player hasn't kicked off, promote them"). Implements BB6 (matchday spread) and MB3/MB4 (bench order + sub triggers), and the manual-substitution rule in league-config.md. Returns a kickoff_spread assessment with over-stack flags, a ranked bench_order (1–4), and a sub_triggers list. Use when laying out a matchday plan or vetting a candidate squad's timing layout — called by wc-matchday-tactician and the strategists, especially A6 Matchday Mechanic.

lyndonkl/claude Agent Skills author: lyndonkl 1 file body ≈ 4 134 tokens Open the sourcegithub.com↗ analyzed 16 h ago

Builds the structural support for the in-round levers in FIFA World Cup Fantasy — scores how the squad's players are distributed across the round's match days…

As a process B 66/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

GeneratorPersonal productivitytype 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
B
66/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
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 66/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 4 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 70Execution cost. Instruction body is 4134 tokens
    • 100Tools and files. No external tools needed
    • 100Steps. 26 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 877: 120–800 characters recommended
    • +1No license
    • +2Single-language instructions
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 26 items
    • +3Output format is stated explicitly
    • +4Has examples (6 code blocks)

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