BF local-image-generation
本地文生图、AI画图、生成图像、画一张图、帮我画、生成图片、创作图像、制作一幅图、 图像生成、文字生成图片、AI绘画、画个XX、我想要一张XX的图、本地生图、离线生图。 generate an image, create a picture, draw something, make an image of, text to image, paint a picture, illustrate, visualize, local image generation, AI art, image synthesis. 使用 Z-Image-Turbo 模型在本机 Windows 离线运行,支持中英双语提示词, 自动优先 Intel iGPU 推理,无需联网,不调用任何云端 API。
本地文生图、AI画图、生成图像、画一张图、帮我画、生成图片、创作图像、制作一幅图、 图像生成、文字生成图片、AI绘画、画个XX、我想要一张XX的图、本地生图、离线生图。 generate an image, create a picture, draw something, make an image of…
As a process F 26/100 · Will not run — References files that are not bundled: .*?
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The text references files that are not there: add them or drop the references.
- 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: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
missing-refreference to a missing file: .*?
Process rating: all ten parameters 26/100
- 0Tools and files. 1 referenced file(s) missing: .*?
- 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. 7 mutating operations with no state check
- 40Consistency. Frontmatter name (local-image-generation) differs from the folder (ov-local-image-gen)
- 70Execution cost. Instruction body is 4526 tokens
- 100Steps. 5 steps
- low 11 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
- -4Absolute local paths (C:\Users, /home/…): not portable
- -239 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 343: enough signal without eating the budget
- +4Structure: 17 headings
- +3Step-by-step instructions: 5 items
- +4Has examples (27 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.