Sn Image Imitate
This is a three-stage pipeline that extracts a long caption from a reference image, rewrites it to match your new content request while locking down the layout, then generates the result. It's built on top of SenseNova's base image tools and enforces layout consistency through a post-generation review step with bounded retries. Use it when you want "make me a new slide deck page that looks like this one but talks about Q4 results instead of Q3," not when you need pixel-perfect style transfer or local edits. The two-tier agent architecture keeps the worker focused on the fixed pipeline while the main agent handles user interaction. Default is three attempts at 0.75 layout similarity threshold.
- skills.sh installs
- 133
- GitHub stars
- 6.1K
- last updated
- Jun 11, 2026
Install
$ npx -y skills add opensensenova/sensenova-skills --skill sn-image-imitate --agent claude-codeInstalls into .claude/skills of the current project.
Files
sn-image-imitate
Image style imitation scene skill (tier 1), relying on the sn-image-recognize, sn-text-optimize, and sn-image-generate tools provided by sn-image-base (tier 0).
Features:
- Extracts high-fidelity long caption from a reference image
- Rewrites caption according to user requested content change while preserving style and layout
- Enforces layout-lock constraints during caption rewrite
- Performs post-generation layout consistency review and bounded retries
- Returns structured process artifacts for debugging and reproducibility
Non-goals
- Pure neural style transfer without content change (use dedicated style-transfer tools instead)
- Local editing / inpainting of specific regions within the reference image
- Processing video or animation input (only single static images are supported)
- Batch generation from multiple reference images in one invocation
- Guaranteeing pixel-level fidelity to the reference; the skill targets layout and style consistency, not exact reproduction
Input Specification
Sponsors
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- Repository
- github.com/opensensenova/sensenova-skills
- License
- MIT
- First seen
- Jun 11, 2026
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