sn-image-imitate
Generates a new image that imitates the style of a reference image while updating content based on user intent. Uses a three-stage pipeline: image annotation (long caption), caption rewriting, and image generation. Use when user asks to "imitate style", "保持这个风格重画", "按这张图风格生成", or "style transfer with new content".
npx skills add OpenSenseNova/SenseNova-Skills --skill sn-image-imitate --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 - `reference_image` (string, required): local path or URL of the style reference image - `target_content` (string, required): new content user wants in the gen
- Non-goals
- Input Specification
- Environment Variable
- API Configuration
- Architecture: Main Agent + Worker Agent
- Workflow
- Main Agent Workflow
- Worker Agent Workflow
- Return Contract
- Normal Flow
- Error Flow
- Output Format
- friendly mode (default)
- verbose mode
echo "$TARGET_CONTENT" > "$TEMP_DIR/target-content.txt" python "$SN_IMAGE_BASE/scripts/sn_agent_runner.py" sn-image-recognize \ echo "$SHORT_CAPTION" > "$TEMP_DIR/reference-short-caption.txt" echo "$LONG_CAPTION" > "$TEMP_DIR/reference-long-caption.txt" echo "$LAYOUT_BLUEPRINT_JSON" > "$TEMP_DIR/layout-blueprint.json" python "$SN_IMAGE_BASE/scripts/sn_agent_runner.py" sn-text-optimize \ echo "$NEW_LONG_CAPTION" > "$TEMP_DIR/new-long-caption.txt" python "$SN_IMAGE_BASE/scripts/sn_agent_runner.py" sn-image-generate \
What does the sn-image-imitate skill do?
Generates a new image that imitates the style of a reference image while updating content based on user intent. Uses a three-stage pipeline: image annotation (long caption), caption rewriting, and image generation. Use when user asks to "imitate style", "保持这个风格重画", "按这张图风格生成", or "style transfer with new content".
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill sn-image-imitate --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From OpenSenseNova/SenseNova-Skills, a repository with 4,855 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.