SKILLEMALL.ai

AB policy-renewal-review

Run a pre-renewal review of an insurance programme: scan coverage gaps against current operations, test limit adequacy against inflation and exposure growth, read the claims experience into pricing expectations, frame market alternatives, and arm the broker negotiation. Use when asked to prepare for a policy renewal, review cover before renewal, check if limits are still adequate, or build renewal negotiation points. Produces a structured renewal review with gap findings, limit assessment, pricing outlook, and negotiation points.

mohitagw15856/pm-claude-skills Agent Skills author: mohitagw15856 MIT 1 file body ≈ 1 241 tokens Open the sourcegithub.com analyzed 2 d ago

Run a pre-renewal review of an insurance programme: scan coverage gaps against current operations, test limit adequacy against inflation and exposure growth…

As a process B 72/100 · Nearly there — weak spots: when it triggers, running it twice, progress reporting

AnalyzerOutlookSales and CRMtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
B
72/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: policy-renewal-review (mohitagw15856/pm-claude-skills)

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 72/100

    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 3 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 20 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1241 tokens

    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)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 535: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 20 items
    • +3Output format is stated explicitly

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.