Loading...
Loading...
Found 19 Skills
Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with fair-comparison caveats and no-overclaim summaries in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline execution, conservative training verification, default routing, verified SOTA claims, or implicit experimentation.
Use when creating an R modeling package that needs standardized preprocessing for formula, data frame, matrix, and recipe interfaces. Covers: mold() for training data preprocessing, forge() for prediction data validation, blueprints, model constructors, spruce functions for output formatting.
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
Audit any eval, metric, experiment, or benchmark for leakage — does external ground-truth enter independently, or are the model, scorer, and designer just confirming a result no outside truth ever produced? Use before trusting any 'how we'll know it worked' — an A/B, a holdout, a score, a validation — and whenever a result feels too clean or self-confirming. Walks an 8-pattern leakage taxonomy and returns only the patterns that fire, each with an independence fix. Read-only.
Use when training, fine-tuning, or validating Ultralytics YOLO models in Platform, cloud GPUs, or local code — model.train(), yolo train/val, remote metric streaming, epochs, batch, imgsz, devices, augmentation, multi-GPU, resumes, results, and fixing OOM, NaN loss, low mAP, or overfitting. For hyperparameter search and systematic improvement loops, see yolo-tuning.
Reconstruct Blender models from supplied reference sheets, branding templates, texture atlases, orthographic front/side/back/top views, or mascot/logo art where visual fidelity to the source is more important than a plausible generated object. Use when the user says the model must match a template, wireframe, texture pack, character sheet, mascot sheet, or brand asset exactly; also use after feedback like "does not look like the reference", "fit the texture 1:1", "wrong number of visible parts", or "compare against the template". Requires Blender MCP plus local Python with Pillow/OpenCV/numpy; pairs with blender-uv-texturing, wireframe-to-3d, blender-modeling, blender-materials, and blender-export.
Exports and validates Physical AI Studio policies for Runtime deployment. Use when working on policy.export(...), the physicalai export CLI, the ONNX/OpenVINO/Torch/ExecuTorch backends, export metadata, numerical parity checks, or the Studio side of the export/load contract that Runtime consumes with InferenceModel(...).
Check BIM model consistency: naming conventions, parameter completeness, spatial relationships, and data integrity across model elements.
ASP.NET Core Web API implementation: clean controllers with CQRS, global error handling, model validation, Swagger/OpenAPI, API versioning, security (CORS, auth), middleware pipeline, and performance patterns. Use when creating or editing controllers, filters, middleware, Program.cs, or API endpoints.
Run queries against a Cube deployment's semantic layer and interpret the results, from the terminal. Use whenever someone wants numbers out of Cube — pull a metric, check a total, compare periods, verify that a measure returns what it should, sanity-check a model change. Triggers on "how many", "what's the total", "show me revenue by month", "did that measure work", "what's our MoM growth", "pull the numbers for", and any request to verify a model change against real data. To find out what is queryable first use cube-explore-model; to change the model use cube-build-model; to save a result as a report or dashboard use cube-build-content.
Write a durable sports model card covering identity, intended use, target, decision time, data, features, baselines, validation, results, limits, maintenance, and kill conditions. Use when freezing or sharing a model.
Cross Validation Setup - Auto-activating skill for ML Training. Triggers on: cross validation setup, cross validation setup Part of the ML Training skill category.