Meta-Intelligence¶
Commands that operate above individual experiments: transferring institutional knowledge across projects and auditing research methodology before submission.
/turing:transfer: Cross-project knowledge transfer¶
Find similar prior projects and surface what worked. Builds institutional ML memory: "Last time you had tabular classification with class imbalance, LightGBM beat everything by 3%." Similarity matching uses task type, dataset size, feature types, class balance, and dimensionality. With --auto, winning strategies are queued as hypotheses for the current project.
Syntax: /turing:transfer [--from project-path] [--auto] [--index ~/.turing/project_index.yaml] [--json]
Examples:
/turing:transfer # Search index for similar projects
/turing:transfer --from ~/projects/fraud-detection # Transfer from specific project
/turing:transfer --auto # Auto-queue hypotheses
/turing:audit: Pre-submission methodology audit¶
A reviewer checklist you run before submitting. Catches methodology mistakes that cause desk rejections: data leakage, missing baselines, cherry-picked seeds, incomplete ablations, undocumented hyperparameter budgets, and more. Each failure suggests the specific /turing: command to fix it. Supports venue-specific checklists for NeurIPS, ICML, and ICLR.
Syntax: /turing:audit [--strict] [--checklist neurips|icml|iclr] [--json]
Examples: