paper-research-assistant

This package looks like a legitimate paper-reading / scaffold generator but it is sloppy and incomplete. Things to consider before installing or running: - Path mismatches: SKILL.md refers to scripts/ and references/ subfolders (e.g., scripts/parse_paper.py, references/report_template.md), but the actual files are at the repository root. Running the example commands as-written will likely fail unless you move/rename files or adjust paths. - Missing web/resource search: SKILL.md describes automated searches of arXiv, GitHub, HuggingFace, and license verification. The provided scripts do not perform network/API calls for that — the AI agent or you would need to implement those steps separately. Do not assume the skill will automatically fetch remote resources. - Data handling: parse_paper.py extracts full text and writes a 5000-character preview into the metadata JSON. If you give it private or embargoed PDFs, that content will be written to disk. Run in a safe environment and inspect outputs if privacy is a concern. - Code generation: scaffold_code.py will create files under the output directory you specify. Review generated files (e.g., requirements.txt, placeholder dataset code) before executing any generated training scripts. Requirements and placeholders may need correction (e.g., package names/versions). Recommended actions: 1. Run the scripts in an isolated environment (container/VM) the first time. 2. Fix or adapt the path references in SKILL.md or move files into the expected directories so examples work as intended. 3. If you need automatic resource discovery (GitHub/arXiv/HuggingFace), implement or verify a network-safe method and confirm any API tokens/credentials are handled securely (this skill does not request them). 4. Inspect outputs (metadata JSON, generated code) before running any generated training jobs to avoid accidental execution on sensitive data or unexpected code. Given the inconsistencies and missing functionality (not strictly malicious), treat the skill as potentially useful but immature; review and test it before trusting it with sensitive papers or running generated experiments.