---

name: sn-image-imitate description: | 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". metadata: project: SenseNova-Skills tier: 1 category: scene priority: 8 user_visible: true triggers:

• "style imitation"
• "style transfer"
• "imitate this image style"
• "use this style with new content"
• "reference style image"
• "风格模仿"
• "风格迁移"
• "模仿这张图风格"
• "按参考图风格生成"

---

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 generated image
• output_mode (string, default friendly): output mode, friendly or verbose
• aspect_ratio (string, default 16:9): output aspect ratio for generation
• image_size (string, default 2k): output image size preset
• max_attempts (int, default 3): maximum generation attempts for meeting layout consistency
• layout_threshold (float, default 0.75): minimum layout similarity score to accept result

Environment Variable

Dependency installation and API key configuration are for sn-image-base skill.

The minimum environment variables to configure sn-image-base skill running with SenseNova Token Plan:

SN_BASE_URL="https://token.sensenova.cn/v1"
SN_API_KEY="your-api-key"

Fallback priority is dedicated variable > domain shared variable > global variable. Text calls use SN_TEXT_API_KEY -> SN_CHAT_API_KEY -> SN_API_KEY; vision calls use SN_VISION_API_KEY -> SN_CHAT_API_KEY -> SN_API_KEY; image generation uses SN_IMAGE_GEN_API_KEY -> SN_API_KEY.

Please refer to the Python dependencies and API keys section in sn-image-generate_en.md for more configurations.

API Configuration

All API calls in this skill are executed through the sn_agent_runner.py of the sn-image-base skill, please refer to the sn-image-base skill (README.md) for more details.

• VLM call: sn-image-recognize (Step 1 & 3)
• LLM call: sn-text-optimize (Step 2)
• Image generation call: sn-image-generate (Step 3)

When encountering MissingApiKeyError or needing explicit model control: pass model and auth params explicitly via CLI arguments. See $SN_IMAGE_BASE/references/api_spec.md.

$SN_IMAGE_BASE path explanation: $SN_IMAGE_BASE is the installation directory of the sn-image-base skill (SKILL.md exists). The agent can locate this path by skill name sn-image-base.

Architecture: Main Agent + Worker Agent

This skill uses a two-tier agent architecture:

• Main Agent: receives user request, normalizes parameters, sends preflight, invokes Worker Agent, and sends final text/image to user
• Worker Agent: executes fixed 3-step pipeline and returns structured JSON

Responsibility Boundaries:

• Worker Agent does not send any user-visible message directly
• Main Agent sends all user-facing responses
• Worker Agent last message must be and only be the JSON string defined in Return Contract
• Worker Agent executes VLM/LLM/image calls directly; no nested subagent for these low-level calls

Workflow

Main Agent Workflow

1. Extract reference_image, target_content, output_mode (default friendly), aspect_ratio (default 16:9), image_size (default 2k), max_attempts (default 3), and layout_threshold (default 0.75)
2. Validate required inputs:
   • reference_image is provided and resolvable
   • target_content is non-empty
3. Send preflight message: "Using sn-image-imitate skill to generate a style-consistent image, please wait..."
4. Start Worker Agent with full normalized parameters and working directory
5. On Worker result:
   • status=ok: send final summary and generated image
   • status=error: report the actual error

Worker Agent Workflow

Worker Agent receives reference_image, target_content, output_mode, aspect_ratio, image_size, max_attempts, layout_threshold, and the working directory of this skill ($SKILL_DIR).

Error Handling Strategy:

All sn_agent_runner.py calls share the same error handling rules:

• If the subprocess exits with non-zero code, crashes, or times out: do not fallback, return status=error with the actual error message from stderr or the system error string
• If the subprocess returns invalid JSON or the JSON lacks an expected result field: return status=error, do not silently continue with empty or default values
• If the VLM review call fails during Step 3, treat the attempt as incomplete: do not record a score, and either retry the review once or skip to the next attempt depending on remaining budget

Step 0 — Initialization

1. Generate task_id with format YYYYMMDD_HHMMSS
2. Create temp directory: /tmp/openclaw/sn-image-imitate/<task_id>/ as TEMP_DIR
3. Resolve and normalize REFERENCE_IMAGE
4. Persist user request:

echo "$TARGET_CONTENT" > "$TEMP_DIR/target-content.txt"

Step 1 — Image Annotation (long caption + layout blueprint)

Use prompts/image_annotate.md as system prompt and call sn-image-recognize on reference image.

python "$SN_IMAGE_BASE/scripts/sn_agent_runner.py" sn-image-recognize \
  --system-prompt-path "$SKILL_DIR/prompts/image_annotate.md" \
  --user-prompt "Please annotate this reference image and follow the required output format." \
  --images "$REFERENCE_IMAGE" \
  --output-format json

Parse JSON result, then parse three blocks:

• SHORT_CAPTION: ...
• LONG_CAPTION: ...
• LAYOUT_BLUEPRINT_JSON: { ... }

If parsing fails, LONG_CAPTION is empty, or LAYOUT_BLUEPRINT_JSON is invalid JSON, return status=error.

Persist outputs:

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"

Step 2 — New long caption generation (content rewrite with layout lock)

Goal: preserve style/layout/visual language from reference long caption while replacing core content by target_content.

Hard constraints to preserve (guided by layout-blueprint.json):

• visual hierarchy (title/subtitle/body emphasis order)
• region topology (number of major blocks and their relative positions)
• reading flow (left-to-right / top-to-bottom / radial / timeline direction)
• chart type and data encoding form (if present)
• spacing rhythm and alignment pattern
• major region bounding boxes and topological relations from blueprint

Preferred system prompt: prompts/caption_rewrite.md (recommended to add). If missing, use inline fallback system prompt:

Rewrite the long caption by preserving style and layout constraints while replacing semantic content according to user target. Do not change block topology, reading order, or visual hierarchy. Keep the caption detailed and directly usable for image generation.

Call sn-text-optimize:

python "$SN_IMAGE_BASE/scripts/sn_agent_runner.py" sn-text-optimize \
  --system-prompt-path "$SKILL_DIR/prompts/caption_rewrite.md" \
  --user-prompt "Reference long caption:\n$LONG_CAPTION\n\nLayout blueprint JSON:\n$LAYOUT_BLUEPRINT_JSON\n\nTarget content:\n$TARGET_CONTENT\n\nReturn only the rewritten long caption." \
  --output-format json

Parse JSON result as NEW_LONG_CAPTION. If empty, return status=error.

Persist output:

echo "$NEW_LONG_CAPTION" > "$TEMP_DIR/new-long-caption.txt"

Step 3 — Image Generation and Layout Review Loop

Execute attempt from 1 to max_attempts sequentially:

Generate Image (using sn-image-base's sn-image-generate tool):

python "$SN_IMAGE_BASE/scripts/sn_agent_runner.py" sn-image-generate \
  --prompt "$CURRENT_PROMPT" \
  --aspect-ratio "$ASPECT_RATIO" \
  --image-size "$IMAGE_SIZE" \
  --save-path "$TEMP_DIR/attempt_<N>.png" \
  --output-format json

VLM configuration requirements:

• When max_attempts > 1, VLM review is required for each attempt
• Select VLM model from OpenClaw configuration as parameter for image recognition
• If no suitable VLM model exists in OpenClaw configuration:
  • Notify user that current parameter combination cannot be executed
  • Suggest adding VLM configuration or setting max_attempts to 1 to skip review
• If VLM call times out or fails: do not fallback, report the real error directly

Layout Consistency Review (only executed when max_attempts > 1):

Review candidate vs reference using prompts/layout_review.md (with blueprint as structural oracle):

python "$SN_IMAGE_BASE/scripts/sn_agent_runner.py" sn-image-recognize \
  --system-prompt-path "$SKILL_DIR/prompts/layout_review.md" \
  --user-prompt "Reference is image[0], candidate is image[1]. Layout blueprint JSON:\n$LAYOUT_BLUEPRINT_JSON\n\nEvaluate layout similarity and return JSON only." \
  --images "$REFERENCE_IMAGE" "$TEMP_DIR/attempt_<N>.png" \
  --output-format json

Expected review JSON (inside result):

{
  "layout_similarity_score": 0.0,
  "style_similarity_score": 0.0,
  "pass": false,
  "major_deviations": [],
  "fix_hints": []
}

Save Attempt Result:

{
  "attempt": 1,
  "image": "$TEMP_DIR/attempt_1.png",
  "layout_similarity_score": 0.0,
  "style_similarity_score": 0.0,
  "pass": false,
  "major_deviations": [],
  "timing": {
    "image_generation": { "elapsed_seconds": 12.34, "model": "sn_image_model" },
    "vlm_review": { "elapsed_seconds": 5.67, "model": "sensenova-122b" }
  }
}

Note: elapsed_seconds is read from the --output-format json return of each CLI call; image_generation.model is fixed to the hardcoded placeholder "sn_image_model" (sn-image-generate does not return the model field); vlm_review.model is read from the JSON return of sn-image-recognize. timing.vlm_review is omitted when max_attempts=1.

Early Termination Check (only executed when max_attempts > 1):

Pass criteria:

• layout_similarity_score >= layout_threshold

• pass = true

• If pass: immediately exit the loop, do not continue generating

• If fail and attempts remain, append correction hints to prompt:

Layout correction requirements:
- <fix_hint_1>
- <fix_hint_2>
...

• If all attempts fail to pass threshold, return highest-score candidate and mark layout_passed=false

Return Contract

Worker Agent final response must be bare JSON (no extra text, no code fence).

Normal Flow

{
  "status": "ok",
  "need_main_agent_send": true,
  "output_mode": "friendly|verbose",
  "result": {
    "image": "/tmp/openclaw/s