sn-ppt-creative

name	sn-ppt-creative
description	Creative-mode PPT pipeline. One full-page 16:9 PNG per slide. LLM / VLM calls go through sn-ppt-standard/lib/model_client.py (shared thin client). Text-to-image (the actual png rendering) goes through sn-image-base/scripts/sn_agent_runner.py. Expects task_pack.json + info_pack.json already written by sn-ppt-entry.
metadata	{"project":"SenseNova-Skills","tier":1,"category":"scene","user_visible":false}
triggers	["sn-ppt-creative"]

sn-ppt-creative

Call-routing policy

Kind	Backend
LLM (text)	$PPT_STANDARD_DIR/lib/model_client.py → llm(sys, user)
VLM (image understanding)	$PPT_STANDARD_DIR/lib/model_client.py → vlm(sys, user, images)
T2I (image generation)	$SN_IMAGE_BASE/scripts/sn_agent_runner.py sn-image-generate

Never mix — LLM / VLM through sn-image-base, or T2I through model_client — both violate policy.

Preconditions

• <deck_dir>/task_pack.json exists and ppt_mode == "creative"
• <deck_dir>/info_pack.json exists
• <deck_dir>/pages/ exists
• $SN_IMAGE_BASE env var (OpenClaw-injected) points at the sn-image-base skill root
• $PPT_STANDARD_DIR env var points at the sn-ppt-standard skill root (so we can import model_client)

Any missing → stop and tell user to enter via /skill sn-ppt-entry.

Resume

python3 $SKILL_DIR/scripts/resume_scan.py --deck-dir <deck_dir>
# => {"style_spec_done": bool, "outline_done": bool, "pptx_done": bool,
#     "pages": [{"page_no": 1, "action": "skip|render_only|full"}, ...]}

Dispatch:

Manifest	Do
style_spec_done == false	Run Stage 2
outline_done == false	Run Stage 3
per-page action == "full"	Run Stage 4.1 + 4.2
per-page action == "render_only"	Run Stage 4.2 only (prompt.txt already on disk)
per-page action == "skip"	Skip
pptx_done == false (all pages done or failed)	Run Stage 5

Stage 2 — style_spec.md (LLM or VLM via model_client)

One independent exec tool_call. Two branches based on reference images.

Branch A (no ref images, or all missing on disk) — use model_client.llm:

python3 -c "
import sys, pathlib, json
sys.path.insert(0, '$PPT_STANDARD_DIR/lib')
from model_client import llm

deck = pathlib.Path('<deck_dir>')
tp = json.loads((deck / 'task_pack.json').read_text())
ip = json.loads((deck / 'info_pack.json').read_text())

sys_prompt = open('$SKILL_DIR/prompts/style_from_query.md').read()
user_prompt = json.dumps({
    'params': tp['params'],
    'query': ip.get('user_query'),
    'digest': ip.get('document_digest'),
}, ensure_ascii=False)

md = llm(sys_prompt, user_prompt)
(deck / 'style_spec.md').write_text(md, encoding='utf-8')
print('style_spec.md ok')
"

Branch B (≥1 reference image on disk) — use model_client.vlm:

python3 -c "
import sys, pathlib, json
sys.path.insert(0, '$PPT_STANDARD_DIR/lib')
from model_client import vlm

deck = pathlib.Path('<deck_dir>')
ip = json.loads((deck / 'info_pack.json').read_text())
tp = json.loads((deck / 'task_pack.json').read_text())

refs = [p for p in (ip.get('user_assets') or {}).get('reference_images', []) if pathlib.Path(p).exists()]

sys_prompt = open('$SKILL_DIR/prompts/style_from_image.md').read()
user_prompt = f'PPT 主题/参数: {json.dumps(tp[\"params\"], ensure_ascii=False)}\nuser_query: {ip.get(\"user_query\") or \"\"}'

md = vlm(sys_prompt, user_prompt, images=refs)
(deck / 'style_spec.md').write_text(md, encoding='utf-8')
print(f'style_spec.md ok (from {len(refs)} ref images)')
"

If user_assets.reference_images is non-empty but all paths missing on disk: fall through to Branch A and prepend a line reference_images_missing: <original paths> at the top of style_spec.md.

Stage 3 — outline.json (LLM via model_client)

python3 -c "
import sys, pathlib, json
sys.path.insert(0, '$PPT_STANDARD_DIR/lib')
from model_client import llm

deck = pathlib.Path('<deck_dir>')
tp = json.loads((deck / 'task_pack.json').read_text())
ip = json.loads((deck / 'info_pack.json').read_text())
style = (deck / 'style_spec.md').read_text()

sys_prompt = open('$SKILL_DIR/prompts/outline.md').read()
user_prompt = json.dumps({
    'style_spec_markdown': style,
    'params': tp['params'],
    'query': ip.get('user_query'),
    'digest': ip.get('document_digest'),
}, ensure_ascii=False)

raw = llm(sys_prompt, user_prompt).strip()
if raw.startswith('\`\`\`'):
    raw = raw.split('\n', 1)[1].rsplit('\`\`\`', 1)[0]
data = json.loads(raw)
assert len(data['pages']) == tp['params']['page_count'], 'page_count mismatch'
(deck / 'outline.json').write_text(json.dumps(data, ensure_ascii=False, indent=2))
print(f'outline ok, {len(data[\"pages\"])} pages')
"

On failure (non-JSON / length mismatch): abort.

Stage 4 — per-page: one independent exec per page

4.1 Compose prompt (LLM via model_client) — skip if action == "render_only"

python3 -c "
import sys, pathlib, json
sys.path.insert(0, '$PPT_STANDARD_DIR/lib')
from model_client import llm

deck = pathlib.Path('<deck_dir>')
N = <NNN>
style = (deck / 'style_spec.md').read_text()
outline = json.loads((deck / 'outline.json').read_text())
page = next(p for p in outline['pages'] if int(p['page_no']) == N)

sys_prompt = open('$SKILL_DIR/prompts/page_prompt.md').read()
user_prompt = json.dumps({'style_spec_markdown': style, 'page': page}, ensure_ascii=False)

txt = llm(sys_prompt, user_prompt)
(deck / 'pages' / f'page_{N:03d}.prompt.txt').write_text(txt, encoding='utf-8')
print(f'prompt page {N} ok')
"

# sanitize the written prompt in-place: strip hex/rgb/hsl/CSS/px/em/rem etc
# to prevent T2I server-side prompt-enhance from baking them into the image.
# Silent: no chat-facing notification; removals go to stderr only.
python3 $SKILL_DIR/scripts/sanitize_prompt.py --path <deck_dir>/pages/page_<NNN>.prompt.txt

4.2 Generate image (T2I via sn-image-base)

--negative-prompt 是针对可能带自身 prompt-enhance 的 T2I 后端的最后一道防线： 即使前面的 sanitize 没拦住、或后端重写时引入了新的样式元数据，也通过反向约束压制模型把它们画出来。这段字符串在所有页上都一致。

python $SN_IMAGE_BASE/scripts/sn_agent_runner.py sn-image-generate \
  --prompt "$(cat <deck_dir>/pages/page_<NNN>.prompt.txt)" \
  --negative-prompt "hex color code, #RRGGBB, rgb(), rgba(), hsl(), hsla(), css, json, yaml, code snippet, pixel values, px, em, rem, pt, color palette text, typography label, design spec, style guide, font stack, hex code, layout annotation, dimensional callout, figma-style spec sheet, wireframe annotation, swatch with numbers" \
  --aspect-ratio 16:9 \
  --image-size 2k \
  --save-path <deck_dir>/pages/page_<NNN>.png \
  --output-format json

4.3 Failure handling

• 4.1 failure (model timeout / empty / malformed): record page_no into failed_pages, echo failure line, continue.
• 4.2 failure: same — record, echo, continue.
• No retries. No placeholder PNG. Don't write 1x1 transparent PNGs to fake success.
• .prompt.txt may remain on disk for a later manual re-run of 4.2 only.

Stage 5 — pptx 打包（一次独立 exec）

所有页图生成后（含部分失败的情况），把 pages/page_*.png 平铺打包成 16:9 整册 PPTX，每张图满版一页。由 scripts/build_pptx.py 完成，模型只负责执行脚本。

python3 $SKILL_DIR/scripts/build_pptx.py --deck-dir <deck_dir>
# => {"deck_id": "...", "output": "<deck_dir>/<deck_id>.pptx",
#     "total_slides": N, "included_pages": [...], "missing_pages": [...]}

行为约定：

• 输出路径默认 <deck_dir>/<deck_id>.pptx；可用 --output 覆盖。
• 页序按 outline.json 的 page_no 排；缺失 outline.json 时按 page_001..page_NNN 走。
• 缺失的 PNG 会插入空白页并在 stderr 记录一行，不中止；这样跟 Stage 4 的"失败跳过"语义一致。
• 脚本失败（依赖缺失 / 写盘失败）：echo 失败原因，不中止整个 skill，仍进入 Stage 6 收尾；PNG 已在磁盘上。 如果 python-pptx 缺失导致失败：🚫 不要尝试 pip install python-pptx 或任何替代方案。PNG 页面已经是最终交付物，直接进入 Stage 6。

Stage 6 — closing

Emit:

创意模式已完成。

📁 输出目录：<deck_dir>
📄 结果文件：
  - style_spec.md
  - outline.json
  - pages/page_001.png ~ page_NNN.png（失败 M 页：page_..., page_...）
  - <deck_id>.pptx（整册，缺失页插入空白）

⚠️ 未完成：
  - page_007：生图返回超时，已跳过（pptx 中为空白页）

下一步：
  - 可直接打开 <deck_id>.pptx 查看整册
  - 或在 pages/ 目录查看 PNG

Progress echo — MANDATORY

Stage	Example
After resume_scan	已进入 sn-ppt-creative，共 N 页
After Stage 2	[1] style_spec.md ✓
After Stage 3	[2] outline.json ✓（N 页）
Per page-prompt (4.1)	[prompt 3/10] ✓
Per page-image (4.2)	[图 3/10] page_003.png ✓ or [图 3/10] ✗ 超时
After Stage 5	[pptx] <deck_id>.pptx ✓（N 页，缺失 M 页） or [pptx] ✗ <reason>
Closing	full summary above

• Each echo is a chat reply, not a log write.
• Per-page echo is the heartbeat for Stage 4.
• On failure, echo failure line with reason before moving on.

🚫 Hard rules

1. Do NOT loop inside a single exec. One page = one tool_call.
2. Do NOT fake images. Failed T2I → record failed, move on. No 1x1 placeholder PNGs.
3. Do NOT use model_client.t2i — T2I must go through sn-image-base. model_client handles only LLM / VLM.
4. Do NOT use sn-text-optimize or sn-image-recognize from sn-image-base — those must go through model_client.llm / model_client.vlm.
5. Do NOT retry on first failure.
6. Do NOT generate editable JSON from PNG (out of scope).