drugclaw Scientific Workflow Tools
Use this skill when the user needs higher-level research method support rather than raw database lookup or computation.
Typical triggers:
- generate or compare mechanistic hypotheses from observations
- review a manuscript draft for rigor, missing controls, or overstated claims
- build a reproducibility checklist before submission or release
- structure a scientific report, review, or response-to-reviewers plan
- identify missing controls, statistical gaps, or reporting-standard issues
Bundled Asset
templates/reproducibility_checklist.py
Preferred Workflow
- Restate the research question, claim, or draft under review.
- Separate what is observed from what is inferred.
- Enumerate methodological risks before proposing fixes.
- Use the checklist template to create a durable artifact for reporting or project tracking.
- Keep outputs explicitly on the research side. Do not cross into patient-level diagnosis or treatment planning.
Reproducibility Checklist
python3 templates/reproducibility_checklist.py \
--profile omics \
--output research/omics_checklist.md \
--summary research/omics_checklist.json
Supported baseline profiles:
generalomicsmlclinical-research
Use the generated checklist as a starting artifact, then tailor it to the exact study.
Working Rules
- Hypotheses should be testable and distinguish observation from mechanism.
- Peer-review style critique should prioritize reproducibility, controls, statistics, and claim scope.
- Scientific writing support should strengthen structure and rigor, not fabricate citations or results.
- Reporting-guideline and checklist outputs are planning artifacts, not proof that the study is compliant.
Related Skills
For literature search outputs and evidence tables, activate literature-review-tools.
For clinical-study design and reporting-guideline selection, activate clinical-research-tools.
For numerical statistical execution, activate stat-modeling-tools or survival-analysis-tools.
For experiment suggestion or bounded closed-loop optimization, activate bayesian-optimization-tools.
For figure generation, activate scientific-visualization-tools.
For bioinformatics, chemistry, or docking execution, activate the corresponding domain skill instead of keeping the task abstract.