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BD stable-diffusion-image-generation

State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.

synthetic-sciences/OpenScience Hermes author: synthetic-sciences Apache-2.0 3 files body ≈ 3 100 tokens Open the sourcegithub.com↗ analyzed 5 d ago

State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers.

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorGoogle CloudAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
Runs in: Hermes Agent
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
98
Quality 40%
82
Run on models
none yet
Process rating
D
46/100
Unfinished process
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

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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-high-entropy-token references/advanced-usage.md:201
    High-entropy token-like string (may be an id, hash or a credential)
    from diffusers import StableDiffusionXLPipeline, Stab…ine
  • low Secrets in code secret-high-entropy-token SKILL.md:143
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | `Stab…ine` | Image-to-image |
    table

Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 234 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • note frontmatter-key unknown frontmatter key "dependencies"

Process rating: all ten parameters 46/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (stable-diffusion-image-generation) differs from the folder (stable-diffusion)
  • 100Tools and files. No external tools needed
  • 100Steps. 22 steps
  • 100Execution cost. Instruction body is 3100 tokens
  • low 16 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)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +3Description length 234: enough signal without eating the budget
  • +4Structure: 38 headings
  • +3Step-by-step instructions: 22 items
  • +4Has examples (25 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.