Paper Tweet Generator

Source: https://github.com/aipoch/medical-research-skills

Paper Reading Tweet Generator

This skill analyzes an academic paper (PDF, Word, or Text) and generates a structured reading tweet including basic info, background, results, and conclusion. It can highlight specific product/drug advantages and ensures standardized terminology.

When to Use

• Use this skill when the request matches its documented task boundary.
• Use it when the user can provide the required inputs and expects a structured deliverable.
• Prefer this skill for repeatable, checklist-driven execution rather than open-ended brainstorming.

Key Features

• Scope-focused workflow aligned to: Generates a structured reading tweet from an academic paper (PDF, Word, or Text), highlighting specific product advantages. Use when the user wants to turn a document into a social media post or reading summary.
• Packaged executable path(s): scripts/extract_pdf.py plus 1 additional script(s).
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python: 3.10+. Repository baseline for current packaged skills.
• Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.

Example Usage

cd "20260316/scientific-skills/Others/paper-tweet-generator"
python -m py_compile scripts/extract_pdf.py
python scripts/extract_pdf.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/extract_pdf.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/extract_pdf.py with additional helper scripts under scripts/.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Workflow

To generate a tweet, follow these steps sequentially:

1. Locate and Extract Content

First, locate the file and extract its text content.

• Locate File: If the user provides a file path, use it. If not (e.g., "uploaded file"), use Glob to search for .pdf, .docx, or .txt files in the entire workspace (pattern: **/*.pdf). Select the most relevant file (e.g., recently added).
• Extract Text:
  • Recommend using an output file to avoid console buffer limits.
  • Run: python scripts/extract_text.py <file_path> extracted_content.txt
  • Read the content: Read extracted_content.txt
• Handle Output:
  • If the extraction fails or returns empty text (check stderr logs), inform the user.
  • If "Warning: No text extracted" is logged, the PDF is likely a scanned image.
• Fallback: If the script fails, try reading the file directly with built-in tools (only for text files).

2. Generate Tweet Sections

Use the extracted text to generate the following sections using the prompts in references/prompt_templates.md. Note: If the extracted text is very long (> 50k chars), focus on the Abstract, Introduction, Results, and Conclusion sections.

• Basic Info: Extract title, authors, journal, DOI.
• Background: Summarize the research background (< 500 words).
• Results: Summarize key findings highlighting the product (< 800 words).
• Conclusion: Summarize the main conclusion.