القائمة
Preprocessing CNS papers (PDF + HTML) to extract text, figures, and links. Outputs structured data for use by the paper-filter skill.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Acquire and verify data for CNS papers that passed paper-filter. Clones repositories, downloads and organizes datasets per evaluation setting, performs Level 4 deep verification (acquisition integrity, initial state, separability, consistency, per-setting completeness), creates an organized data environment. Updates filter_result.json with verification results, acquisition details, and task_info corrections.
Verify data components in filter_result.json after data-check. Checks Algorithm A boundary, evaluation setting validity, separability correctness, data directory integrity, and description consistency. Outputs a verification report for human review — does not modify filter_result.json or data directories.
Fix Dockerfile issues identified by batch_verify.sh. Reads verify_result.txt to diagnose import failures, version conflicts, and missing dependencies, then repairs the Dockerfile and updates packages.json. May also fix scripts (Script-First Rule) when packages are unavailable.
Verify paper-filter results by checking pass/reject judgment correctness, validating task_info field accuracy, and detecting internal inconsistencies. Outputs actionable corrections to filter_result.json.
Filter CNS papers for suitability in extracting machine learning tasks. Uses a three-level cascade funnel (Task Nature → Evaluation System → Data Completeness) with progressive filtering; stops immediately upon rejection. Input is preprocessed paper data; output is the filtering decision result.
Build a structured task package from a paper that has passed paper-preprocess, paper-filter, and data-check. Reorganizes data, generates problem description, evaluator, metadata, and environment Dockerfile.v3.
| name | paper-preprocess |
| description | Preprocessing CNS papers (PDF + HTML) to extract text, figures, and links. Outputs structured data for use by the paper-filter skill. |
| context | fork |
| agent | general-purpose |
| allowed-tools | Read, Bash(python *) |
Preprocess CNS papers using HTML (text/links) and PDF (figures) to extract structured data.
Before invoking this skill, provide:
*.pdf and *.html files for the same paper (Nature/Springer structured HTML). The folder name is used as paper_id.Use scripts/extract_text.py to extract full text from the HTML:
Use scripts/extract_figures.py to extract figures from the PDF:
Use scripts/extract_links.py to extract links from the HTML:
cd skills/paper-preprocess/scripts
python3 preprocess.py <input_dir> <output_dir>
# Example:
python3 preprocess.py ./s42256-019-0037-0/ ./s42256-019-0037-0/preprocessed
{output_dir}/
text.md # Full paper text (Markdown format)
figures/ # Figures directory
Fig_1_page2.png
Fig_2_page3.png
...
tables/ # Tables directory
Table_1_page3.png
...
links.json # Extracted links with section classification
{
"links": [
{
"url": "https://github.com/example/repo",
"type": "github | zenodo | codeocean | huggingface | google_drive | dropbox | other",
"context": "Surrounding paragraph text (up to 400 chars)",
"section": "data_availability | code_availability | supplementary_information | other"
}
],
"data_availability": ["https://..."],
"code_availability": ["https://github.com/..."],
"paper_id": "s42256-019-0037-0",
"extraction_timestamp": "2026-01-01T00:00:00"
}
Install Python dependencies before running:
pip install pymupdf beautifulsoup4