SKILLEMALL.ai

BB matter-plan-builder-scott-margetts

Convert agreed scope into a structured matter plan — phases, workstreams, milestones, dependencies, owner assignments, and matter setup decisions. Use when planning a new matter, running a kickoff, building a workstream plan, structuring phases, setting up task codes, or producing a plan to drive status reporting. Trigger on: 'build a plan', 'matter plan', 'project plan', 'what are the phases', 'workstream plan', 'how do we sequence this', 'who owns what', 'task codes', 'matter setup', 'workstream plan', 'matter plan', 'rolling wave', 'plan the next phase', 'what comes first', 'kickoff agenda'.

lawve-ai/awesome-legal-skills Agent Skills author: lawve-ai 3 files body ≈ 8 220 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Convert agreed scope into a structured matter plan — phases, workstreams, milestones, dependencies, owner assignments, and matter setup decisions.

As a process B 69/100 · Nearly there — weak spots: failures and branches, execution cost, running it twice

GeneratorSoftware developmentData and analyticsOperations and projectstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
Run on models
none yet
Process rating
B
69/100
Nearly there
Failures and branches w 10
0
Running it twice w 4
30
Execution cost w 6
40
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 8220 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 69/100

  • 0Failures and branches. Linear process with no failure handling
  • 30Running it twice. 6 mutating operations with no state check
  • 40Execution cost. Instruction body is 8220 tokens: crowds the task out of the window
  • 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. 36 steps, 1 vague phrases
  • 100Tools and files. No external tools needed
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 10 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)
  • +2Single-language instructions
  • +3Description length 601: enough signal without eating the budget
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 36 items
  • +3Output format is stated explicitly
  • +4Has examples (1 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.