SKILLEMALL.ai

BA document-approval-tracker-scott-margetts

Approval cascade definition and tracking for multi-stakeholder document workflows. Internal review sequences, client approval workflows, regulatory review, overdue chasing with escalation logic, version control coordination, and cross-jurisdiction document dependencies. Use when defining who reviews a document and in what order, tracking where a document is stuck in the approval chain, chasing an overdue reviewer, preventing reviewers working on superseded drafts, or mapping dependencies between documents across jurisdictions. Trigger on: 'who needs to approve this', 'document approval', 'review sequence', 'stuck in review', 'overdue approval', 'who has the document', 'version control', 'wrong version', 'document dependency', 'NL SPA waiting on German opinion', 'approval cascade', 'review chain', 'document circulation', 'chasing the partner', 'client approval process', 'where is the document'.

lawve-ai/awesome-legal-skills Agent Skills author: lawve-ai 3 files body ≈ 6 855 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Approval cascade definition and tracking for multi-stakeholder document workflows.

As a process A 83/100 · Runs to the end — weak spots: failures and branches

ProcedureWordOperations and projectstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
A
83/100
Runs to the end
Failures and branches w 10
0
Result and completion w 14
60
Execution cost w 6
70
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 ≈ 6855 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 32): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 83/100

  • 0Failures and branches. Linear process with no failure handling
  • 60Result and completion. Output format stated, no completion criterion
  • 70Execution cost. Instruction body is 6855 tokens
  • 100Tools and files. No external tools needed
  • 100Steps. 51 steps
  • 100When it triggers. States when to use and when not to
  • 100Inputs and preconditions. Inputs and preconditions are listed
  • 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
  • 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)
  • +3Description length 906: 120–800 characters recommended
  • +2Single-language instructions
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 51 items
  • +3Output format is stated explicitly
  • +4Has examples (5 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.