SKILLEMALL.ai

AC wc-fixture-progression

Rates fixture difficulty (separately for an attacking and a defending fantasy asset, 1-5 each way, from opponent xGA/xG), computes team progression probabilities (group qualification odds from the mini-table; round-by-round knockout survival down the bracket) by reference-class forecasting updated Bayesianly on observed form and group state, detects weak-group/mismatch fixtures (powerhouse-vs-minnow -> captain candidates and clean-sheet locks), and builds a multi-round swing calendar of where each team's difficulty flips so transfers can be planned ahead. Emits the `fixture` signal. Use when called by wc-fixture-analyst, by the strategists in the populate stage, and by wc-fitness-eval for its progression_carry term — and never re-derive these once the signal exists.

lyndonkl/claude Agent Skills author: lyndonkl 1 file body ≈ 4 587 tokens Open the sourcegithub.com↗ analyzed 16 h ago

Rates fixture difficulty (separately for an attacking and a defending fantasy asset, 1-5 each way, from opponent xGA/xG), computes team progression…

As a process C 64/100 · Has gaps — weak spots: failures and branches, progress reporting

ProcedurePersonal productivityData and analyticstype 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
C
64/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
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: 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 64/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (node) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4587 tokens
    • 100Steps. 28 steps
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. Mutating operations check current state
    • 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 776: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 28 items
    • +3Output format is stated explicitly
    • +4Has examples (4 code blocks)

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