SKILLEMALL.ai

CC panel-review-rationalisation

Panel health assessment, firm exit management, coverage gap analysis, and panel refresh brief for in-house legal ops teams. Produce an annual Panel Health Report synthesising scorecard data, billing compliance, and step-out patterns into per-firm recommendations — Retain, Watch, Improvement Plan, or Exit Review. Produce a firm exit pack: formal exit notice with active matter transition plan, plus an internal exit record. Identify practice area, geographic, or tier gaps and produce a structured Coverage Gap Report with remediation options. Produce a Panel Refresh Brief as a scoped input to the RFP process. Trigger on: 'review our panel', 'annual panel review', 'is our panel working', 'which firms should we exit', 'write the exit notice', 'coverage gap', 'we don't have anyone for restructuring', 'refresh the panel', 'panel rationalisation', 'too many firms', 'step-out problem', 'do we have the right firms'.

lawve-ai/awesome-legal-skills Agent Skills author: lawve-ai 2 files body ≈ 11 080 tokens Open the sourcegithub.com↗ analyzed 89 min ago

Panel health assessment, firm exit management, coverage gap analysis, and panel refresh brief for in-house legal ops teams.

As a process C 62/100 · Has gaps — weak spots: result and completion, failures and branches, consistency

AnalyzerProcurementData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
C
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
71
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 11080 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 62/100

  • 0Result and completion. Does not say what the result is
  • 0Failures and branches. Linear process with no failure handling
  • 30Running it twice. 8 mutating operations with no state check
  • 40Consistency. Frontmatter name (panel-review-rationalisation) differs from the folder (panel-review-rationalisation-scott-margetts)
  • 40Execution cost. Instruction body is 11080 tokens: crowds the task out of the window
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Steps. 72 steps
  • 100When it triggers. States when to use and when not to
  • 100Progress reporting. Reports progress

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 918: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 35 headings
  • +3Step-by-step instructions: 72 items
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.