scitex-python
Reduces time-to-publication and ensures reproducibility by automating the full pipeline from raw data ingestion through manuscript generation, cutting manual…
Python toolkit for reproducible science — from raw data to manuscript. Includes 42 modules, 282 CLI commands, 0 MCP tools, and 0 skills.
- Automate conversion of raw experimental data into publication-ready figures and tables.
- Generate reproducible analysis pipelines that document every data transformation step.
- Execute 282 CLI commands to orchestrate full workflows from raw data to final manuscript.
Reduces time-to-publication and ensures reproducibility by automating the full pipeline from raw data ingestion through manuscript generation, cutting manual integration work and compliance overhead.
Data science teams and research organizations standardizing workflows across experimental projects with strict reproducibility requirements.
https://github.com/ywatanabe1989/scitex-python
By ywatanabe1989
How to Get It
claude plugins install ywatanabe1989/scitex-python
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Help me automate conversion of raw experimental data into publication-ready figures and tables
Trust Signals Auto-scanned
Community Pulse Emerging
Discussed on Reddit
- Organizing my research Python code for others - where to start? — Reddit · 3 pts
1 mentions across 1 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 85 GitHub stars; 3 contributors; last commit 8d ago; license AGPL-3.0.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
- License (AGPL-3.0) may restrict commercial use. Check with your legal team.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 85 GitHub stars; 3 contributors; last commit 8d ago; license AGPL-3.0.