# Group Meeting Ppt

> 做组会PPT / group meeting PPT builder for research experiment folders. Use when the user says “做组会ppt”, “根据当前目录的内容来做组会ppt”, “按上次实验三流程做PPT”, or asks to inspect/read an experiment folder or result export, answer methodological questions, create AI image prompts, generate many slide images, assemble a PPT/PPTX, or add detailed Chinese speaker notes for a research presentation.

- Skill: `rvme/group-meeting-ppt` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add rvme/group-meeting-ppt`
- Raw SKILL.md: https://api.skillmd.com/api/skills/rvme/group-meeting-ppt/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- Author: RvMe (https://skillmd.com/u/rvme)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/rvme/group-meeting-ppt

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# 做组会PPT

## Goal

Turn an experiment folder into a presentation-ready package:

- Read the experiment directory and recent result exports deeply.
- Extract the experiment story, methods, data usage, metrics, risks, and conclusions from actual files/code.
- Produce an image plan and AI image prompts in Markdown.
- Generate or collect numbered slide images.
- Build a PPTX with one full-slide image per page.
- Add detailed Chinese speaker notes without corrupting Chinese text.

## Workflow

### 1. Build Source Context

Start from the user-specified directory or current working directory.

Inspect:

- Top-level reports, summaries, README/navigation files, and dated result exports.
- Scripts that define data loading, training, inference, evaluation, and fusion logic.
- Tables, CSV/JSON/NPY exports, logs, and generated figures.
- Existing prompt files, notes files, image folders, and PPT outputs if continuing prior work.

Prefer `rg --files`, `rg`, `Get-ChildItem`, and targeted `Get-Content`. Do not assume a directory name is literal if the actual folder differs; state the resolved path.

When answering methodology questions, cite actual code behavior:

- Training/inference split and file globs.
- Whether H/P groups are used for unsupervised training, supervised losses, validation, or evaluation.
- Which losses are active, newly introduced, or carried over.
- Which result file supports each reported metric.

### 2. Extract Presentation Story

Before making visuals, form a concise talk narrative:

- Research problem and why the current experiment exists.
- Data representation and pipeline.
- Baselines and incremental method changes.
- Key quantitative results and negative results.
- What the experiment proves, what it does not prove, and the next technical direction.

For medical/research experiments, distinguish:

- Empirical result vs. interpretation.
- Mainline method vs. failed/negative branch.
- Code-confirmed fact vs. inference from filenames/reports.

### 3. Create Image Plan And Prompts

Create a Markdown prompt file in the experiment or output folder, usually:

`<experiment>_PPT_AI_IMAGE_PROMPTS.md` or `PROMPTS.md`

Use numbered images:

`01`, `02`, ..., with each entry containing:

- Slide purpose.
- What the image should show.
- AI image prompt.
- Optional style constraints.

Do not avoid text/numbers inside prompts if the user explicitly says the current image model can handle them. Still keep important text short and visually large.

Recommended prompt style:

- Use 16:9 wide slide illustration.
- Request clean research-presentation composition.
- Include meaningful labels, arrows, small charts, and schematic anatomy/pipeline elements.
- Avoid relying on exact tiny table values unless needed.
- Keep one core message per image.

### 4. Generate Or Collect Images

When the user asks to generate images, use the available image generation tool for each prompt. Save/copy the final image assets into a dedicated folder such as:

`<experiment>_ppt_ai_images/`

Use stable names:

`quest003_ppt_image_01.png`
`quest003_ppt_image_02.png`
...

Also copy the prompt Markdown into the same folder as `PROMPTS.md`.

After generation, verify:

- Expected image count exists.
- Numbering is continuous.
- Files are readable image assets.

### 5. Build PPTX

If asked for a simple PPT, create one slide per image and make the image cover the whole slide.

Use `python-pptx` for slides-only PPTX when notes are not needed.

For Chinese speaker notes, prefer PowerPoint COM automation on Windows because direct OpenXML or `python-pptx` notes manipulation can create repair warnings or corrupt Chinese notes.

Recommended output names:

- `QuestXXX_PPT_pythonpptx_slides_only.pptx`
- `QuestXXX_PPT_Final_with_Chinese_Notes.pptx`
- Optional Chinese copy: `QuestXXX_最终版_含中文备注.pptx`

Avoid hand-zipping PPTX OpenXML unless there is no better option.

### 6. Add Chinese Speaker Notes

Create a UTF-8 source notes file first:

`SLIDE_NOTES_ZH.md`

Use one numbered section per slide. Notes should explain:

- What the audience sees.
- What this slide is meant to say.
- How it connects to the experiment's method/result logic.
- Any caveats, failures, or interpretation boundaries.

Then insert notes into the PPTX.

On Windows, use PowerPoint COM automation when available:

- Create/open the PPTX in PowerPoint.
- Add each image full-slide.
- Insert notes text through the Notes Page text placeholders.
- Save as `.pptx`.
- Verify the file opens without repair prompts if possible.

After creation, verify by opening the `.pptx` as a zip or using PowerPoint that:

- Notes count matches slide count.
- Chinese text remains readable, not `????`.
- Images are present in all slides.

### 7. Report Outputs

At the end, give the user:

- Exact output folder.
- Main PPTX path.
- Notes source path.
- Prompt file path.
- Image count and naming pattern.
- Any limitation, such as not being able to open PowerPoint GUI in the environment.

Use clickable absolute file links when responding from Codex.

## Practical Defaults

- Default language for notes: Chinese.
- Default slide format: widescreen 16:9.
- Default slide layout: image fills slide, no extra titles unless user asks.
- Default image count: choose enough to explain the experiment clearly; for a full research experiment, 12-20 images is typical.
- Default folder naming: `<experiment>_ppt_ai_images`.

## Common Triggers

Example user requests that should use this skill:

- “根据当前目录的内容来做组会ppt。”
- “做组会PPT，图片和备注也一起做。”
- “读取这个实验目录，生成多张图片并制作 PPT+备注。”
- “把当前实验做成一份答辩 PPT。”
- “根据结果 export 生成图片提示词，再做 PPT。”
- “以后按上次实验三的流程来。”
- “为这个实验目录生成 AI 图和中文讲稿备注。”