SKILLEMALL.ai

BB cpr-letter-drafter-andrew-bird

Writes the formal letter you send before suing someone in a civil case in England & Wales — the Letter Before Claim that starts the pre-action clock. The part it gets right is which pre-action protocol applies — debt, professional negligence, housing disrepair, personal injury, or the default Practice Direction on Pre-Action Conduct — each of which has its own rules a generic letter misses (the 30-day debt window, the professional-negligence preliminary-notice step). It also flags the limitation deadline for a solicitor to confirm rather than stating it as fact. Built for litigation juniors, in-house counsel, and small firms without a precedent to copy. Use when the user says 'draft an LBC', 'letter before claim', 'pre-action letter', 'pre-action protocol', or needs to start the pre-action clock before issuing a civil claim. Verify the current protocol before sending.

lawve-ai/awesome-legal-skills Claude Code author: lawve-ai 2 files body ≈ 2 338 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Writes the formal letter you send before suing someone in a civil case in England & Wales — the Letter Before Claim that starts the pre-action clock.

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

GeneratorLegaltype 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
76/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
Failures and branches w 10
55
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 76/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 58 steps, 2 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2338 tokens
    • low 19 top-level sections: this looks like several domains in one skill

    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 880: 120–800 characters recommended
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +4Structure: 26 headings
    • +3Step-by-step instructions: 58 items
    • +3Output format is stated explicitly

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