SKILLEMALL.ai

BA risk-and-issues-manager-scott-margetts

RAID log methodology with decision extraction from emails and meeting notes. Use when asked to identify risks, log assumptions, track issues, extract decisions from correspondence, create or update a RAID log, escalate a risk to an issue, assess project risks, validate or challenge assumptions, or capture scope-relevant decisions. Also triggers when the user pastes email chains and asks what decisions were made, or needs to find buried decisions and untested assumptions in correspondence. Trigger on: 'risk report', 'RAID log', 'what are the risks', 'what decisions were made', 'update the risk register', 'what assumptions are we making', 'what could go wrong', 'flag any issues', 'extract decisions from these emails'.

lawve-ai/awesome-legal-skills Agent Skills author: lawve-ai 3 files body ≈ 11 796 tokens Open the sourcegithub.com↗ analyzed 89 min ago

RAID log methodology with decision extraction from emails and meeting notes.

As a process A 82/100 · Runs to the end — weak spots: execution cost, running it twice

ProcedureData and analyticsResearchWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
A
82/100
Runs to the end
Running it twice w 4
30
Execution cost w 6
40
Result and completion w 14
60
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 ≈ 11796 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 82/100

  • 30Running it twice. 15 mutating operations with no state check
  • 40Execution cost. Instruction body is 11796 tokens: crowds the task out of the window
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 93 steps, 1 vague phrases
  • 100Tools and files. No external tools needed
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 6 branches, has a failure section
  • 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

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
  • +2Single-language instructions
  • +3Description length 725: enough signal without eating the budget
  • +4Structure: 37 headings
  • +3Step-by-step instructions: 93 items
  • +3Output format is stated explicitly
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.