Verify data-backed claims from markdown documents against datasets.
npx skills add https://github.com/gaasher/Agent-Loop-Skills --skill claim-verify
Please help me install this Agent Skill. Skill: claim-verify Source: https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/claim-verify Command: npx skills add https://github.com/gaasher/Agent-Loop-Skills --skill claim-verify
This Skill streamlines the process of verifying data-backed claims by conducting adversarial checks against a dataset, ensuring the accuracy and robustness of findings before publication.
Use the claim-verify skill to verify all claims in the 'results_draft.md' document against the 'trial_data.csv' dataset.
High-intent search queries and answers about installing and using this skill.
To verify data-backed claims, you can run adversarial checks against your dataset to test for outliers, confounds, and subgroup sizes. This process ensures your research findings are reproducible and survive stress tests prior to publication.
Adversarial data verification is the process of checking each claim against potential threats like outliers and confounds within a dataset. It ensures the accuracy and robustness of research findings by confirming claims survive stress tests.
You can automate claim verification by running a Python script that extracts claims from a markdown draft and checks them against a CSV dataset. The tool outputs a verified draft with revisions based on the adversarial checking results.
Yes, you need Python 3.9 or higher installed to run the adversarial checks on your research data. The verification scripts depend on this specific Python environment to process the markdown claims and CSV dataset.
Adversarial claim checking tests for potential threats like outliers, confounds, and subgroup sizes within the dataset. This validates data accuracy and reduces the risk of misinterpretation before publication.
Verified claims are recorded in an automated draft revision. The tool updates the original markdown document based on the verification results, ensuring all claims are reproducible and have survived the adversarial stress tests.
gaasherRun iterative hypothesis testing and re-computation for reproducible data analysis.
catlog22Analyze experimental results into validated statistics and publication-ready reporting.
Casper-StudiosAnalyze CSV datasets with statistics, quality checks, and visualizations.
LambenthanReview research artifacts and produce structured scores, weaknesses, and improvement plans.
