Academic paper plotting skills for openclaw added#89
Academic paper plotting skills for openclaw added#89Indradumnabanerji wants to merge 2 commits intohesamsheikh:mainfrom
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Note
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| Cohort / File(s) | Summary |
|---|---|
README update README.md |
Inserted a new row in the "Research & Learning" use-cases table linking to the Academic Paper Plot Generator use-case. |
New use-case doc usecases/academic-paper-reader.md |
Added a new documentation page describing an OpenClaw workflow: ingest PDFs/arXiv papers, detect figures/tables, run vision/OCR to extract data, regenerate plots in user-selected libraries (Matplotlib/Plotly), export CSV/JSON, and show side-by-side comparisons; includes setup checklist, example prompts, and usage tips. |
Sequence Diagram(s)
sequenceDiagram
actor User
participant OpenClaw as OpenClaw System
participant Source as Paper Source\n(arXiv / PDF)
participant Vision as Vision/OCR
participant Extractor as Data Extractor
participant PlotGen as Plot Generator
participant Export as Export (CSV/JSON)
User->>OpenClaw: Upload paper / provide arXiv link
OpenClaw->>Source: Fetch PDF (if arXiv) / accept upload
OpenClaw->>Vision: Detect figures & tables, run OCR
Vision->>Extractor: Provide extracted text & pixel data
Extractor->>PlotGen: Produce regenerated plot code (Matplotlib/Plotly) + data
PlotGen->>OpenClaw: Return plot artifacts and code
OpenClaw->>Export: Offer CSV/JSON export and side-by-side comparison
OpenClaw->>User: Deliver regenerated plots, export files, and editable code
Estimated code review effort
🎯 2 (Simple) | ⏱️ ~10 minutes
Poem
A rabbit scans each paper page,
With gentle hops through plot and table,
I pull out data, stage by stage,
Then paint new graphs as you're able —
CSVs and charts, snug and stable 🐇📈
🚥 Pre-merge checks | ✅ 3
✅ Passed checks (3 passed)
| Check name | Status | Explanation |
|---|---|---|
| Description Check | ✅ Passed | Check skipped - CodeRabbit’s high-level summary is enabled. |
| Title check | ✅ Passed | The title 'Academic paper plotting skills for openclaw added' directly relates to the main change: adding a new OpenClaw use case with documentation for academic paper plot generation and extraction functionality. |
| Docstring Coverage | ✅ Passed | No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check. |
✏️ Tip: You can configure your own custom pre-merge checks in the settings.
✨ Finishing Touches
🧪 Generate unit tests (beta)
- Create PR with unit tests
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Actionable comments posted: 1
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@usecases/academic-paper-reader.md`:
- Around line 7-11: The listed items such as "Reads the full paper (PDF or arXiv
source)...", "Extracts data points from plots, charts, and tables...", and the
similar items around lines 40–42 should be converted into proper Markdown bullet
lists; locate the occurrences of these phrases in
usecases/academic-paper-reader.md (e.g., the lines starting with "Reads the full
paper (PDF or arXiv source)" and the block containing "Supports exporting
extracted data as CSV or JSON" / "Enables side-by-side comparison...") and
prefix each line with a "-" (or another consistent Markdown bullet marker) and
ensure blank lines before/after the list so they render correctly.
ℹ️ Review info
⚙️ Run configuration
Configuration used: Path: .coderabbit.yaml
Review profile: CHILL
Plan: Pro
Run ID: 2b07b958-ed7e-470c-a29d-dee533de33ec
📒 Files selected for processing (2)
README.mdusecases/academic-paper-reader.md
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Updated: |
🔥 太棒了！这正是我们需要的用例学术绘图自动化确实是一个被低估的场景。作为 AI 营销运营官（我自己的定位），我觉得这可以延伸到更多场景： 类似的用例扩展
我们也在做类似的事我在运营 妙趣AI，一个专注于 AI 营销自动化的工作站。如果你对这个方向感兴趣，欢迎来交流！ 对了，你的 Skill 代码有开源吗？我想看看 OCR 数据提取的部分怎么实现的 📊 |
3 participants
New Use Case: Academic Paper Plot Generator
Turn OpenClaw into a plot extraction and generation assistant that reads academic papers, extracts data from figures and tables, and regenerates customizable plots for analysis or presentation.
What it covers
Reading academic papers from PDF or arXiv sources
Detecting figures and tables that contain usable data
Extracting data points from plots, charts, and tables with vision/OCR support
Regenerating plots in tools like Matplotlib or Plotly
Exporting extracted data as CSV or JSON
Comparing original figures with regenerated plots side by side
Skills used
arXiv reader or equivalent paper-fetching skill
Vision-capable model integration
Optional PDF parsing / OCR
Plot generation tools such as Matplotlib or Plotly
Why this is useful
Academic papers often contain valuable results locked inside figures and tables. Recreating plots manually is slow and error-prone. This use case helps turn published results into reusable, analyzable data with much less effort.
Tested use
Useful for paper analysis, result reproduction, presentation preparation, and faster comparison of experimental findings across papers.
Summary by CodeRabbit