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CA runtime-admissibility-review

Determines whether a specific AI-agent action, output, recommendation, or proposed commitment remains admissible for execution or institutional reliance under current authority, delegated scope, evidence, facts, policy, risk, escalation, and revocation conditions. Use this Skill before an enterprise or regulated AI agent executes, updates records, triggers workflows, communicates externally, or before an institution relies on an agentic output in a way that creates consequence.

lawve-ai/awesome-legal-skills Hermes author: lawve-ai 8 files body ≈ 9 895 tokens Open the sourcegithub.com↗ analyzed 71 min ago

Determines whether a specific AI-agent action, output, recommendation, or proposed commitment remains admissible for execution or institutional reliance under…

As a process A 80/100 · Runs to the end — weak spots: consistency, execution cost

AnalyzerAI and agentsLegaltype and topics are labelled automatically from the skill text
Runs in: Hermes Agent
JSON
Technical rating
C
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
71
Run on models
none yet
Process rating
A
80/100
Runs to the end
Consistency w 8
40
Execution cost w 6
40
Result and completion w 14
60
the three weakest of ten parameters · all ten

How to improve

  1. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
  2. 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: 8. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 482 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • warning body-long SKILL.md body ≈ 9895 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 80/100

  • 40Consistency. Frontmatter name (runtime-admissibility-review) differs from the folder (runtime-admissibility-review-arkadiy-miteiko)
  • 40Execution cost. Instruction body is 9895 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
  • 70Failures and branches. 4 branches
  • 100Tools and files. No external tools needed
  • 100Steps. 519 steps
  • 100When it triggers. States when to use and when not to
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • low 37 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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 482: enough signal without eating the budget
  • +4Structure: 106 headings
  • +3Step-by-step instructions: 519 items
  • +3Output format is stated explicitly
  • +4Has examples (0 code blocks)

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