--- name: opensensenova/sn-ppt-creative source: https://app.decimal.ai/s/opensensenova-sn-ppt-creative@1/SKILL.md source_sha256: 6577d88e8680 --- # sn-ppt-creative > **⚠️ This skill must be invoked through `/skill sn-ppt-entry`.** Never start here directly — the entry skill collects parameters and writes `task_pack.json` + `info_pack.json` that this skill requires. If you arrived here without those files, stop and tell the user to enter via `/skill sn-ppt-entry` or "生成 PPT". ## 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) | `$SN_IMAGE_BASE/scripts/sn_agent_runner.py sn-image-generate` | Never mix — LLM / VLM through sn-image-base, or T2I through model_client — both violate policy. ## Visual asset priority - Creative mode renders each slide as a generated full-page PNG, so **image generation is the first-priority visual path**. - If image generation fails for a page, use web search (`sn-search-image`) as a fallback to find a real image that fits the page's topic. Each search result includes the image URL, source page, title, and domain for traceability. - Do not create placeholders. If generation and search both fail, record the page failure and continue; never write fake PNGs, grey boxes, broken-image icons, or "image pending" text. - Do not mention the search provider name in prompts, visible slide text, progress, or summaries. ## Preconditions - `/task_pack.json` exists and `ppt_mode == "creative"` - `/info_pack.json` exists - `/pages/` exists - `$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`. ## Generation progress WebUI `sn-ppt-entry` starts the generation progress WebUI after `task_pack.json` / `info_pack.json` are written. During creative-mode generation, publish progress with the shared writer from `sn-ppt-standard`: ```bash P="python3 $PPT_STANDARD_DIR/scripts/progress_event.py" $P --deck-dir --stage creative-style --status running $P --deck-dir --stage creative-style --status ok --artifact style_spec.md $P --deck-dir --stage creative-outline --status running $P --deck-dir --stage creative-outline --status ok --artifact outline.json $P --deck-dir --stage creative-prompt --page N --status running $P --deck-dir --stage creative-prompt --page N --status ok $P --deck-dir --stage creative-render --page N --status running $P --deck-dir --stage creative-render --page N --status ok $P --deck-dir --stage export --status running $P --deck-dir --stage export --status ok ``` On failure, write the same stage with `--status failed --error ""` before moving on or aborting. On native Windows, use `python` if `python3` is unavailable. ## Resume ```bash python3 $SKILL_DIR/scripts/resume_scan.py --deck-dir # => {"style_spec_done": bool, "outline_done": bool, "pptx_done": bool, # "pages": [{"page_no": 1, "action": "skip|render_only|full"}, ...]} ``` Dispatch: | Manifest | Do | |---|---| | `style_spec_done == false` | Run Stage 2 | | `outline_done == false` | Run Stage 3 | | per-page `action == "full"` | Run Stage 4.1 + 4.2 | | per-page `action == "render_only"` | Run Stage 4.2 only (prompt.txt already on disk) | | per-page `action == "skip"` | Skip | | `pptx_done == false` (all pages done or failed) | Run Stage 5 | ## Stage 2 — style_spec.md (LLM or VLM via model_client) One independent exec tool_call. Two branches based on reference images. **Branch A (no ref images, or all missing on disk)** — use `model_client.llm`: ```bash python3 -c " import sys, pathlib, json sys.path.insert(0, '$PPT_STANDARD_DIR/lib') from model_client import llm deck = pathlib.Path('') tp = json.loads((deck / 'task_pack.json').read_text()) ip = json.loads((deck / 'info_pack.json').read_text()) sys_prompt = open('$SKILL_DIR/prompts/style_from_query.md').read() user_prompt = json.dumps({ 'params': tp['params'], 'query': ip.get('user_query'), 'digest': ip.get('document_digest'), }, ensure_ascii=False) md = llm(sys_prompt, user_prompt) (deck / 'style_spec.md').write_text(md, encoding='utf-8') print('style_spec.md ok') " ``` **Branch B (≥1 reference image on disk)** — use `model_client.vlm`: ```bash python3 -c " import sys, pathlib, json sys.path.insert(0, '$PPT_STANDARD_DIR/lib') from model_client import vlm deck = pathlib.Path('') 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('$SKILL_DIR/prompts/style_from_image.md').read() user_prompt = f'PPT 主题/参数: {json.dumps(tp[\"params\"], ensure_ascii=False)}\nuser_query: {ip.get(\"user_query\") or \"\"}' md = vlm(sys_prompt, user_prompt, images=refs) (deck / 'style_spec.md').write_text(md, encoding='utf-8') print(f'style_spec.md ok (from {len(refs)} ref images)') " ``` If `user_assets.reference_images` is non-empty but **all** paths missing on disk: fall through to Branch A and prepend a line `reference_images_missing: ` at the top of style_spec.md. ## Stage 3 — outline.json (LLM via model_client) ```bash python3 -c " import sys, pathlib, json sys.path.insert(0, '$PPT_STANDARD_DIR/lib') from model_client import llm deck = pathlib.Path('') 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('$SKILL_DIR/prompts/outline.md').read() user_prompt = json.dumps({ 'style_spec_markdown': style, 'params': tp['params'], 'query': ip.get('user_query'), 'digest': ip.get('document_digest'), }, ensure_ascii=False) raw = llm(sys_prompt, user_prompt).strip() if raw.startswith('\`\`\`'): raw = raw.split('\n', 1)[1].rsplit('\`\`\`', 1)[0] data = json.loads(raw) assert len(data['pages']) == tp['params']['page_count'], 'page_count mismatch' (deck / 'outline.json').write_text(json.dumps(data, ensure_ascii=False, indent=2)) print(f'outline ok, {len(data[\"pages\"])} pages') " ``` On failure (non-JSON / length mismatch): **abort**. ## Stage 4 — per-page: one independent exec per page ### 4.1 Compose prompt (LLM via model_client) — skip if `action == "render_only"` ```bash python3 -c " import sys, pathlib, json sys.path.insert(0, '$PPT_STANDARD_DIR/lib') from model_client import llm deck = pathlib.Path('') N = 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 /pages/page_.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。 ```bash python $SN_IMAGE_BASE/scripts/sn_agent_runner.py sn-image-generate \ --prompt "$(cat /pages/page_.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 /pages/page_.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。 ```bash python3 $SKILL_DIR/scripts/build_pptx.py --deck-dir # => {"deck_id": "...", "output": "/.pptx", # "total_slides": N, "included_pages": [...], "missing_pages": [...]} ``` 行为约定： - 输出路径默认 `/.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： 📄 结果文件： - style_spec.md - outline.json - pages/page_001.png ~ page_NNN.png（失败 M 页：page_..., page_...） - .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 .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 each progress write | `.workbench/progress.json 已更新： ` | | 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] .pptx ✓（N 页，缺失 M 页）` or `[pptx] ✗ ` | | 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.** If the same stage fails twice in a row with the same error, treat it as permanent and move on. 6. **Do NOT generate editable JSON from PNG** (out of scope). 7. **Language integrity.** All user-visible text MUST match the user's query language. A single English slide in a Chinese deck is a regression. 8. **Do NOT use python-pptx, pptxgenjs, or any alternative PPTX builder.** `scripts/build_pptx.py` is the ONLY way to produce a PPTX. Never `pip install python-pptx` or write Node scripts that import `pptxgenjs`. If PPTX build fails, the PNG pages are the final deliverable. 9. **Do NOT fabricate data.** All numbers and factual claims MUST come from the user's documents or web search. Use qualitative descriptions if no data source is available. 10. **Wait for responses.** If you ask the user a question, do NOT proceed until they reply. Never assume default values. 11. **Multi-round edits: regenerate.** When the user requests changes, re-run the affected pipeline stages. Do NOT sed/perl/patch files in-place. 12. **Validate paths before writing.** All output goes under `/` — the absolute path written in `task_pack.json`. Before writing any file, verify the parent directory exists. Never write to `/workspace/`, `/tmp/`, `~/`, `./`, or any hallucinated path.