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aeonfun/aeon Agent Skills author: aeonfun 1 file body ≈ 9 697 tokens Open the sourcegithub.com↗ analyzed 72 min ago

One lifecycle poll over everything vuln-scanner produces - PR and advisory status, PVR triage transitions, and pending-disclosure aging, with a stars-secured…

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorGitHubData and analyticsOperations and projectsWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
C
81/100
safety, quality, tests
Safety 60%
97
Quality 40%
57
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
When it triggers w 12
20
Inputs and preconditions w 11
30
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  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 · 3

✓ No critical or high findings

Medium and low: 3

✓ Guard found no suspicious behaviour. 3 matches are attack strings quoted in this security skill's own documentation.

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 9697 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 63, 67): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 56/100

  • 0Result and completion. Does not say what the result is
  • 20When it triggers. No condition that starts the skill
  • 30Inputs and preconditions. Does not say what the process needs to start
  • 40Execution cost. Instruction body is 9697 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 85Steps. 110 steps, 1 vague phrases
  • 100Failures and branches. 7 branches, has a failure section
  • 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 12 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (4 tags): a typed call is more reliable

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)
  • +3Output format is not stated: the model decides each time
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +3Description length 195: enough signal without eating the budget
  • +4Structure: 35 headings
  • +3Step-by-step instructions: 110 items
  • +4Has examples (22 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 57.