SKILLEMALL.ai

BB stakeholder-comms-planner-scott-margetts

Stakeholder mapping, communication plan design, reporting hierarchy, and mid-matter comms updates. Use when setting up a new matter and needing to identify who needs what information, designing the communication rhythm, building reporting structures for multi-jurisdiction programmes, or updating the comms plan when the stakeholder landscape changes. Trigger on: 'stakeholder map', 'who needs to be kept informed', 'communication plan', 'reporting structure', 'who do we report to', 'how often do we meet', 'client wants more updates', 'new contact on the client side', 'build the comms plan', 'reporting hierarchy', 'HQ vs regions', 'who is the decision maker', 'comms rhythm', 'meeting cadence', 'status call schedule'.

lawve-ai/awesome-legal-skills Agent Skills author: lawve-ai 3 files body ≈ 6 093 tokens Open the sourcegithub.com↗ analyzed 88 min ago

Stakeholder mapping, communication plan design, reporting hierarchy, and mid-matter comms updates.

As a process B 66/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

ProcedureData 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
66/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 6093 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 66/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 5 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
  • 70Execution cost. Instruction body is 6093 tokens
  • 85Steps. 47 steps, 1 vague phrases
  • 100Tools and files. No external tools needed
  • 100Consistency. Name and required fields are in place
  • low 11 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 722: enough signal without eating the budget
  • +4Structure: 27 headings
  • +3Step-by-step instructions: 47 items
  • +3Output format is stated explicitly
  • +4Has examples (2 code blocks)
  • +1License stated

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