Pythesis Plot

Python scientific plotting tool for thesis/dissertation scenarios. Workflow: data upload → analysis → recommendations → confirmation → generation. Triggers w...

What to consider before installing or running this skill: - Source authenticity: The README references a GitHub repo, but the registry metadata lists no homepage/source — confirm the canonical source (e.g., the GitHub repo) before trusting the package. If you obtained the skill from the registry, verify the included files are unchanged and from a trusted author. - Inspect the scripts: Before running, open scripts/workflow.py, scripts/data_analyzer.py, and scripts/plot_generator.py and search for any network libraries or calls (requests, urllib, socket, ftplib, boto3, paramiko, subprocess calls that curl/wget, etc.). Ensure there are no hard-coded endpoints or attempts to upload data. - Run in isolation: Use a throwaway virtual environment or sandbox/container and do not run on sensitive data until you confirm behavior. Install Python dependencies inside a venv, not globally. - Watch for data exfiltration risk: The skill auto-saves uploaded files to disk; ensure the output path is acceptable and that the scripts don't send files to external endpoints. If scripts use logging, confirm logs do not include full data contents. - Confirm the confirmation step: SKILL.md emphasizes waiting for explicit user confirmation before generating charts — make sure the agent enforces that and does not autonomously skip the confirmation. - Verify dependency installs: The README suggests pip installing common packages. Installing packages runs code from PyPI — prefer pinned versions and inspect the install commands. If you want, I can (1) scan the three Python scripts for network I/O and suspicious patterns, (2) list concrete strings/lines to watch for, or (3) suggest a minimal safe test (small dummy CSV) and exact steps to run the skill in a sandboxed venv.

Type: OpenClaw Skill Name: pythesis-plot Version: 1.0.3 The pythesis-plot skill bundle is a well-structured tool designed for scientific data analysis and publication-quality plotting. The workflow, as defined in SKILL.md and implemented in scripts/workflow.py, data_analyzer.py, and plot_generator.py, follows a safe and transparent process that includes mandatory user confirmation before generating any files. The code uses standard data science libraries (pandas, matplotlib, seaborn) and lacks any indicators of malicious intent, such as data exfiltration, unauthorized network access, or prompt injection attacks.