SKILLEMALL.ai

CC matter-intake-scoping-scott-margetts

Matter scoping across the full pre-execution arc — organise client data into a structured brief, capture the agreed baseline, or reconstruct scope mid-flight. Use when making sense of client information before a proposal, scoping a new matter, running a kickoff, defining scope, mapping stakeholders, or inheriting a matter mid-flight. Trigger on: 'make sense of this', 'structure this for the proposal', 'scope this matter', 'new matter', 'kickoff', 'what are we doing', 'who are the stakeholders', 'what does success look like', 'matter setup', 'intake', 'I've inherited this matter', 'organise this client data'.

lawve-ai/awesome-legal-skills Agent Skills author: lawve-ai 5 files body ≈ 6 903 tokens Open the sourcegithub.com↗ analyzed 89 min ago

Matter scoping across the full pre-execution arc — organise client data into a structured brief, capture the agreed baseline, or reconstruct scope mid-flight.

As a process C 62/100 · Has gaps — References files that are not bundled: references/matter-type-profiles/[type].md

AnalyzerWordOperations and projectsInfrastructuretype 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
References files that are not bundled: references/matter-type-profiles/[type].md
Tools and files w 18
0
Inputs and preconditions w 11
0
Failures and branches w 10
55
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.
  2. The text references files that are not there: add them or drop the references.
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: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 6903 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: references/matter-type-profiles/[type].md

Process rating: all ten parameters 62/100

Will not run. References files that are not bundled: references/matter-type-profiles/[type].md
  • 0Tools and files. 1 referenced file(s) missing: references/matter-type-profiles/[type].md
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 55Failures and branches. 1 branches
  • 70Execution cost. Instruction body is 6903 tokens
  • 85Steps. 54 steps, 3 vague phrases
  • 100Result and completion. Output format and completion criterion are stated
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • 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)
  • -5TODO / placeholder text left in the skill
  • +2Single-language instructions
  • +3Description length 615: enough signal without eating the budget
  • +4Structure: 22 headings
  • +3Step-by-step instructions: 54 items
  • +3Output format is stated explicitly
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +1License stated

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