/sn-ppt-standard

name: sn-ppt-standard
description: |
  Standard and fast PPT pipeline. All LLM / VLM / T2I calls are wrapped in a
  single CLI entry (scripts/run_stage.py). The main agent's job is simple:
  emit ONE shell command per stage, never write loops, never write prompts.
  
  Standard mode plans thoroughly with a three-sample deck preview checkpoint
  (three concatenated deck images plus a preview URL), web research, image
  search, and user-selected final output format (PPTX or PDF) for polished,
  delivery-ready presentations. Fast mode builds
  a complete draft immediately with autonomous decisions, then provides
  structured refinement suggestions so the user can iterate quickly. Supports
  AI-generated infographics (U1) for diagrams and flowcharts, web image search
  (Serper) for real photos, and ECharts for data charts.
metadata:
  project: SenseNova-Skills
  tier: 1
  category: scene
  user_visible: false
triggers:
  - "sn-ppt-standard"

sn-ppt-standard

> **⚠️ This skill must be invoked through `/skill sn-ppt-entry`.** Never start here directly — the entry skill collects parameters, parses uploaded files, 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".

This skill is **self-contained** — no dependency on `sn-image-base` for LLM/VLM (T2I still goes through `sn-image-base`). Generation logic stays in `$SKILL_DIR/scripts/run_stage.py`; use `run_stage_with_progress.py` only as a WebUI progress wrapper. Every subcommand is deterministic: one input set → one output artifact → one-line JSON status.

Preconditions

• `<deck_dir>/task_pack.json` exists and `ppt_mode in {"standard", "fast"}`
• `<deck_dir>/info_pack.json` exists

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

When `ppt_mode == "fast"`: **build first, then iterate.** Make decisions autonomously — do not ask the user about colors, fonts, page count, or layout preferences. Infer reasonable defaults from the query and start building immediately. Skip optional web search and image search. Run the full pipeline including PPTX export. **Data**: use uploaded documents first; if none, use mock data labeled `[Sample Data]` and tell the user in chat which data needs replacement. **Images**: AI generation for decorative images, ECharts for charts — no questions asked.

Post-generation (fast mode only)

After the PPTX is generated, do NOT just say "done, any feedback?" Instead, provide a **structured set of refinement suggestions** based on the actual content you generated. This helps the user understand what changed between your fast draft and what a polished standard-mode version would look like.

**1. Quick wins (3-5 specific suggestions):** Point to concrete things the user could improve with one-line instructions. Tie each suggestion to a specific slide or element. Examples:

• "Slide 3: replace the mock revenue numbers with your actual Q4 data"
• "Slide 5: swap the generic team photo placeholder with your real team picture"
• "Cover slide: try a darker background for more impact — I can switch it to deep navy"
• "Slide 7: the bar chart is using sample data — give me your real numbers and I'll regenerate it"

**2. Standard-mode comparison (2-3 gaps):** Explain what would have been different in standard mode, so the user knows what they're trading off. Examples:

• "In standard mode, I would have searched the web for competitor benchmarks to include on slide 4 — right now those numbers are estimates labeled [Sample Data]"
• "Standard mode includes a three-sample deck preview checkpoint where you would compare three concatenated deck images and a preview URL before I built all 12 slides"
• "With image search enabled, slides 2 and 8 could use real product photos instead of the AI-generated decorative images"

**3. Suggested next actions (3-4 paths):** Offer concrete directions the user can take:

• "Replace mock data: tell me which slides need real numbers and I'll update them"
• "Adjust style: I can change the color palette, fonts, or layout density across all slides at once"
• "Add a section: if you need a financial projections or risk analysis section, I can insert new slides"
• "Promote to standard: if this draft is close to what you need, I can re-run it in standard mode with full research and image search for a delivery-ready version"

When the user responds with a change request, apply it immediately and re-present the updated suggestions.

When `ppt_mode == "standard"`: **plan thoroughly first, then build.** Do thorough research and image search. Generate three visual deck samples for the user to choose from before creating the outline. Each sample includes one concatenated deck image and one HTML preview deck; also provide a single preview URL that shows all three decks. Produce the selected final format from `task_pack.params.output_format` (`"pptx"` or `"pdf"`). **Data**: documents first, web search second, ask user as last resort. Never fabricate numbers.

🚫 Hard rules (the main agent MUST NOT)

1. **Do NOT write Python scripts that loop over pages or slots** in a single exec. Use the batch subcommands, or per-item execs in the agent's own loop of tool_calls. 2. **Do NOT fake image generation.** If `gen-image` and its image-search fallback both fail, don't write a placeholder PNG — the HTML stage will redesign around the missing slot. 3. **Do NOT construct LLM prompts yourself.** `run_stage.py` is the only place that builds payloads. 4. **Do NOT add `timing` / logging / retry layers.** The skill is intentionally thin. 5. **Do NOT go silent between execs.** Echo a one-line Chinese progress message after each exec before issuing the next. 6. **Do NOT use python-pptx, pptxgenjs, libreoffice, or any alternative converter.** `run_stage.py export` is the ONLY way to produce the final PPTX/PDF file. Never write Python scripts that import `pptx`, Node scripts tha