SKILLEMALL.ai

CB wc-chip-timing

Scores the deployment leverage of all five FIFA World Cup Fantasy boosters (Wildcard, 12th Man, Maximum Captain, Clean Sheet Shield, Qualification Booster) across the remaining tournament horizon — computing, for each chip and each candidate round, the EXTRA expected points the chip yields in that round versus an average round, then turning the leverage surface into a fire/hold call per chip this round plus a maintained earmark table (chip -> earmarked round -> trigger -> fallback). This is the math engine behind wc-chip-strategist. Reads the round's player-ev, clean-sheet, fixture/progression, ownership and captain-ladder signals plus tournament-state (chips remaining, phase, surviving nations); never re-derives them. Enforces the golden rule — never reach the semis with chips unused. Use whenever a chip decision is live (the chip-check prompt, the group->KO transition, or any round where a chip is earmarked or leverage spikes). Emits a chip-plan signal.

lyndonkl/claude Agent Skills author: lyndonkl 1 file body ≈ 7 645 tokens Open the sourcegithub.com↗ analyzed 16 h ago

Scores the deployment leverage of all five FIFA World Cup Fantasy boosters (Wildcard, 12th Man, Maximum Captain, Clean Sheet Shield, Qualification Booster)…

As a process B 73/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
C
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
69
Run on models
none yet
Process rating
B
73/100
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

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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

  • warning body-long SKILL.md body ≈ 7645 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 73/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 8 mutating operations with no state check
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 70Execution cost. Instruction body is 7645 tokens
  • 100Tools and files. No external tools needed
  • 100Steps. 32 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 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 969: 120–800 characters recommended
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 32 items
  • +3Output format is stated explicitly
  • +4Has examples (15 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.