Audit mechanistic interpretability claims against statistical rigor and validation tiers.
npx skills add https://github.com/AmitabhainArunachala/clawd --skill mi-auditor
Please help me install this Agent Skill. Skill: mi-auditor Source: https://github.com/AmitabhainArunachala/clawd/tree/main/skills/mi-auditor Command: npx skills add https://github.com/AmitabhainArunachala/clawd --skill mi-auditor
This Skill provides rigorous, multi-dimensional auditing of mechanistic interpretability research claims, ensuring scientific validity and reproducibility.
Use the mi-auditor skill to audit the claim that R_V contraction is causally linked to induction head formation.
High-intent search queries and answers about installing and using this skill.
Mechanistic interpretability research findings are categorized into Ironclad, Discovery, and Problematic tiers based on their validation status, cross-architecture replication, and statistical rigor to determine publication readiness.
Mechanistic interpretability research findings are categorized into Ironclad, Discovery, and Problematic tiers based on their validation status, cross-architecture replication, and statistical rigor to determine publication readiness.
Yes, auditing mechanistic interpretability claims requires Python libraries for statistical analysis, causal inference, and literature retrieval, alongside pypdf, pdfplumber, and pdf2image dependencies to process research documents.
Cross-architecture replication is a core dimension assessed during mechanistic interpretability auditing, determining whether claims hold across different model architectures to ensure scientific validity and reproducibility.
Yes, you can submit specific mechanistic interpretability claims such as R_V contraction being causally linked to induction head formation to receive a comprehensive report detailing strengths, weaknesses, and necessary next steps.
RubixOwlExtract text and tables from PDFs using pypdf and pdfplumber.
DamarisDengExecutes statistical analyses from research_questions.json to produce analysis_results.json.
tapanshahDesign A/B tests and build predictive models using Python, R, and SQL.
XWIlluDeluEvaluate scientific claims by critiquing methodology, bias, and statistical validity.
