SKILLEMALL.ai

AC rune-prompt-amplification

Transforms any flat prompt into a structured 8-layer XML prompt using RUNE's semantic engine — delivering ~45% higher quality AI responses. Built on Spinoza's philosophical framework by NeuraByte Labs.

ClawHub Agent Skills author: Mustafa v1.0.0 5 files · 1 script body ≈ 349 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 62/100 · Has gaps — weak spots: when it triggers, failures and branches, progress reporting

GeneratorGitHubAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
C
62/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
When it triggers w 12
20
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.
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: 5. 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")
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 62/100

  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 6 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 349 tokens
  • 100Running it twice. No mutating operations

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)
  • +2Single-language instructions
  • +3Description length 201: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 6 items
  • +3Output format is stated explicitly
  • +4Has examples (2 code blocks)
  • +1License stated

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

External checks

ClawHub: suspicious
This prompt-amplification skill may do what it claims, but it runs unreviewed external code and broadly loads local secrets before processing user prompts.
LLM: suspicious (high) · VirusTotal: suspicious · 28 May 2026