SKILLEMALL.ai

BB without-prejudice-drafter-andrew-bird

Writes a settlement letter on the right footing — and warns you when marking it 'without prejudice' won't actually keep it out of court. A genuine settlement letter is normally protected (the judge can't see it), but only if the substance is a real attempt to settle, and even then there are exceptions. The skill picks the correct footing — without prejudice (WP), without prejudice save as to costs (WPSATC / Calderbank), or open — drafts the finished letter, and surfaces the Unilever v Procter & Gamble exceptions that get WP material admitted despite the label, which a plain template won't flag. Built for juniors and in-house counsel drafting a settlement or Calderbank offer who need the footing right the first time. Use when the user asks to draft a WP letter, a Calderbank, a settlement letter, or wants to know whether material is likely protected from disclosure.

lawve-ai/awesome-legal-skills Claude Code author: lawve-ai 2 files body ≈ 2 351 tokens Open the sourcegithub.com↗ analyzed 90 min ago

Writes a settlement letter on the right footing — and warns you when marking it 'without prejudice' won't actually keep it out of court.

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

GeneratorSales and CRMtype and topics are labelled automatically from the skill text
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
B
73/100
Nearly there
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: 2. 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 73/100

    • 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
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 43 steps
    • 100Result and completion. Output format and completion criterion are stated
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2351 tokens
    • 100Running it twice. No mutating operations

    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 876: 120–800 characters recommended
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 43 items
    • +3Output format is stated explicitly

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.