The Problem
Researchers spend many hours drafting repetitive sections of manuscripts, checking consistency across figures, tables, and references, and ensuring compliance with reporting guidelines. Manual effort leads to errors, inconsistent style, and delayed submissions.
What This Does
The repository supplies 500+ “agent skills” that encapsulate discrete writing tasks (e.g., abstract drafting, figure‑legend generation, limitation writing). Each skill lives in its own folder under awesome-med-research-skills/Academic Writing/ and contains:
SKILL.md – a markdown description of the skill’s purpose and usage. evalreport.json – sample evaluation output from the built‑in Medical Skill Auditor. references/ – rule files (e.g., hard-rules.md, clarification-first-rule.md) that drive the skill’s logic. scripts/main.py – a thin Python entry point that invokes the skill’s core routine.
For example, the figure‑legend writer lives at awesome-med-research-skills/Academic Writing/figure-legend-writer/ and includes a requirements.txt listing openai, pydantic, etc., plus a scripts/main.py that reads a JSON payload, applies the rule set from references/legendtemplates.md, and returns a formatted legend.
How To Use It
Setup – Install the Python dependencies for the skill you intend to run. The repo does not provide a top‑level requirements.txt, so install per‑skill, e.g.:
cd awesome-med-research-skills/Academic\ Writing/figure-legend-writer pip install -r requirements.txt
(Other skills have no explicit Python requirements and can be run with the system Python.) Configuration – No environment variables are documented. Skills expect input via stdin or a JSON file passed as the first argument to main.py. Check each SKILL.md for the expected schema; for the figure‑legend writer, the required fields are figurenumber, description, and optional style. Running – Invoke the script directly. Example for the abstract writer:
python awesome-med-research-skills/Academic\ Writing/conference-abstract-writer/scripts/main.py path/to/input.json
The script prints the generated abstract to stdout. Replace the path with your own input payload. Testing – The repository includes nine test files (e.g., tests/testfigurelegend.py). Run them with:
pytest
(Assumes pytest is installed globally or in a virtual environment.) CI – GitHub Actions workflow .github/workflows/release.yml runs on pushes; it installs dependencies per‑skill and executes the test suite.
Real‑World Use
A research group can integrate the consistency‑checker‑across‑manuscript skill into their manuscript‑assembly pipeline. After generating individual sections, a CI job runs:
python awesome-med-research-skills/Academic\ Writing/consistency-checker-across-manuscript/scripts/main.py manuscript.json > consistency_report.md
The resulting markdown report flags mismatched figure/table references and missing citations, allowing the team to correct issues before journal submission.
Code Health & Issues
Low – Missing lockfile – awesome-med-research-skills/Academic Writing/figure-legend-writer/requirements.txt lists packages without a requirements.lock or pipfile.lock; reproducible builds are not guaranteed. Medium – Inconsistent dependency management – Only one skill provides a requirements.txt; the majority rely on the system environment, increasing the chance of version drift. Low – Sparse test coverage – Nine test files exist, but many skills have no dedicated tests, leaving large portions of the library unvalidated. Low – No package metadata – No setup.py, pyproject.toml, or top‑level requirements.txt; the repo cannot be installed as a Python package without manual path adjustments. Low – Documentation gaps – README.md describes the overall library but does not detail per‑skill CLI flags or input schemas; users must infer from SKILL.md or source code. Low – License present – MIT license is included, satisfying basic legal compliance.
Overall, the code is straightforward and modular, with clear folder separation and a functioning CI pipeline. The primary technical debt lies in dependency lockfiles and broader test coverage.
The Bottom Line
The repo delivers a rich, modular collection of medical‑research writing assistants that can be dropped into existing workflows with minimal code changes. It is best suited for teams comfortable managing per‑skill Python environments and willing to supplement the limited test suite. For solo users or production deployments, the lack of a unified package and lockfiles may require additional engineering effort.