python "$SN_IMAGE_BASE/scripts/sn_agent_runner.py" sn-text-optimize \
  --system-prompt-path "$SKILL_DIR/references/evaluation-standard.md" \
  --user-prompt "$USER_PROMPT" \
  --output-format json

ANALYSIS=$(python "$SN_IMAGE_BASE/scripts/sn_agent_runner.py" sn-text-optimize \
  --system-prompt-path "$SKILL_DIR/references/analysis-framework.md" \
  --user-prompt "$USER_PROMPT" \
  --output-format json)

echo "$ANALYSIS" > "$TEMP_DIR/analysis.json"

ANALYSIS=$(cat "$TEMP_DIR/analysis.json")

LAYOUT_DEF=$(cat "$SKILL_DIR/references/layouts/<layout>.md")
STYLE_DEF=$(cat "$SKILL_DIR/references/styles/<style>.md")

{
  "layout": "<layout>",
  "style": "<style>"
}

ANALYSIS=$(cat "$TEMP_DIR/analysis.json")
LAYOUT_STYLE=$(cat "$TEMP_DIR/layout-style.json")
STRUCTURED_CONTENT_TEMPLATE=$(cat "$SKILL_DIR/references/structured-content-template.md")

cat > "$TEMP_DIR/structured-content.md" << 'EOF'
<Content generated based on structured-content-template.md format>
EOF

STRUCTURED_CONTENT=$(cat "$TEMP_DIR/structured-content.md")
LAYOUT_STYLE=$(cat "$TEMP_DIR/layout-style.json")
LAYOUT=$(echo "$LAYOUT_STYLE" | jq -r '.layout')
STYLE=$(echo "$LAYOUT_STYLE" | jq -r '.style')
LAYOUT_DEF=$(cat "$SKILL_DIR/references/layouts/${LAYOUT}.md")
STYLE_DEF=$(cat "$SKILL_DIR/references/styles/${STYLE}.md")

cat > "$TEMP_DIR/expand-system-prompt.md" << EOF
$(cat "$SKILL_DIR/references/prompts-expand-system.md")

---

Use the content of `structured-content.md` as user-prompt, read system prompt from temporary file and call sn-text-optimize:

```bash
python "$SN_IMAGE_BASE/scripts/sn_agent_runner.py" sn-text-optimize \
  --system-prompt-path "$TEMP_DIR/expand-system-prompt.md" \
  --user-prompt "$STRUCTURED_CONTENT" \
  --output-format json

echo "$EXPANDED_PROMPT" > "$TEMP_DIR/expanded-prompt.txt"

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

python "$SN_IMAGE_BASE/scripts/sn_agent_runner.py" sn-image-recognize \
  --system-prompt-path "$SN_IMAGE_INFOG/references/prompts-critic-system.md" \
  --user-prompt "Evaluate the diagram in the image against the rules. Output your assessment." \
  --images "$TEMP_DIR/round_<N>.png" \
  --output-format json

{
  "round": 1,
  "image": "$TEMP_DIR/round_1.png",
  "result": "PASS|FAIL",
  "violations_count": 0,
  "violations": [],
  "reasoning": "<Reasoning process, empty string when max_rounds=1>",
  "timing": {
    "image_generation": { "elapsed_seconds": 12.34, "model": "sn_image_model" },
    "vlm_review": { "elapsed_seconds": 5.67, "model": "sensenova-6.7-flash-lite" }
  }
}

{
  "status": "ok",
  "need_main_agent_send": true,
  "output_mode": "friendly|verbose",
  "expanded_prompt": "<always contains when output_mode=verbose; value is original user_prompt when prompts_expand_skipped=true, otherwise is expanded result>",
  "prompts_expand_skipped": true,
  "early_terminated": true,
  "timing": {
    "total_elapsed_seconds": 35.12,
    "prompt_detection": { "elapsed_seconds": 2.11, "model": "sensenova-6.7-flash-lite" },
    "content_analysis": { "elapsed_seconds": 3.22, "model": "sensenova-6.7-flash-lite" },
    "prompt_expand": { "elapsed_seconds": 8.45, "model": "sensenova-6.7-flash-lite" }
  },
  "rounds": [
    {
      "round": 1,
      "image": "$TEMP_DIR/round_1.png",
      "result": "PASS|FAIL",
      "violations_count": 0,
      "violations": [],
      "reasoning": "<Reasoning process, empty string when max_rounds=1>",
      "timing": {
        "image_generation": { "elapsed_seconds": 12.34, "model": "sn_image_model" },
        "vlm_review": { "elapsed_seconds": 5.67, "model": "sensenova-6.7-flash-lite" }
      }
    }
  ]
}

{
  "status": "error",
  "error": "<Actual error information>"
}

Quality ranking result (high -> low)
---
Expanded prompt: [expanded | not expanded, using original prompt]
<expanded_prompt>
---
#1 round=<n> result=<PASS|FAIL> violations=<n> [early terminated]
#2 round=<n> result=<PASS|FAIL> violations=<n>
...
---
Time statistics: Total <total>s | Prompt evaluation <t>s | Content analysis <t>s | Prompt expansion <t>s | Image generation <t>s×<n> rounds | VLM review <t>s×<n> rounds
---
Images (sent in rank order)