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AC fedora-hyprland-installer

Install, configure, verify, repair, update, and uninstall Hyprland on Fedora Linux with GPU-aware detection (NVIDIA/AMD/Intel).

sickn33/agentic-awesome-skills Hermes author: sickn33 MIT 22 files · 11 scripts body ≈ 1 655 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Install, configure, verify, repair, update, and uninstall Hyprland on Fedora Linux with GPU-aware detection (NVIDIA/AMD/Intel).

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code Hermes Agent
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
83
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: fedora-hyprland-installer (sickn33/agentic-awesome-skills)

How to improve

  1. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Obfuscation obf-base64-blob SKILL.md:14
    Long base64-looking blob (detector / deny-list definition)
    license_source: https://github.com/male…109/hyprfedora/blob/3ec6…319/LICENSE
    detector

Files scanned: 20. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 127 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • note frontmatter-key unknown frontmatter key "risk"
  • note frontmatter-key unknown frontmatter key "source"
  • note frontmatter-key unknown frontmatter key "source_repo"
  • note frontmatter-key unknown frontmatter key "source_type"
  • note frontmatter-key unknown frontmatter key "date_added"
  • note frontmatter-key unknown frontmatter key "tools"
  • note frontmatter-key unknown frontmatter key "license_source"

Process rating: all ten parameters 55/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 50When it triggers. No condition that starts the skill
  • 85Steps. 43 steps, 1 vague phrases
  • 100Tools and files. Tools declared in frontmatter
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1655 tokens
  • 100Running it twice. Mutating operations check current state

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
  • +2Single-language instructions
  • +3Description length 127: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 43 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (8 of 8)
  • +3All 9 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.