Use when facing 3+ independent failures that can be investigated without shared state or dependencies - dispatches multiple Claude agents to investigate and fix independent problems concurrently
Write a complete Numerai experiment report in experiment.md (abstract, methods, results tables, decisions, next steps) and generate/link the standard show_experiment plot(s). Use after running any Numerai research experiments, or when a user asks for a “full report”, “write up”, “experiment.md update”, or “generate the standard plot”.
Design and manage Numerai experiments in this repo for any model idea.
Add a new Numerai model type to the agents training pipeline. Use when you need to register a model in `agents/code/modeling/utils/model_factory.py`, handle fit/predict quirks in `agents/code/modeling/utils/numerai_cv.py`, and update configs so the model can run via `python -m agents.code.modeling`.
Create Numerai Tournament model upload pickles (.pkl) with a self-contained predict() function. Use when preparing upload artifacts, debugging numerai_predict import errors, or documenting model-upload requirements and testing steps.
End-to-end Numerai research workflow for trying a new idea: design experiments, implement new model types if needed, run scout→scale experiments, write a full experiment.md report with standard plots, and optionally package/upload a Numerai pickle. Use when a user asks to “try/test a new idea”, “run an experiment”, “sweep configs”, “compare model variants”, or otherwise do new Numerai research.
Guides evaluation of RAG pipeline retrieval and generation quality. Use when evaluating a retrieval-augmented generation system, measuring retrieval quality, assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval testing, or optimizing chunking strategies.
Create diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.
Fits causal models, estimates impacts, and plots results using CausalPy. Use when performing analysis with DiD, ITS, SC, or RD.
Selects the appropriate quasi-experimental method (DiD, ITS, SC) based on data structure and research questions. Use when the user is unsure which method to apply.
Loads internal CausalPy example datasets. Use when the user needs example data or asks about available demos.
Simple operations on user-provided text files including summarization.
Comprehensive guide for BFL FLUX image generation models. Covers prompting, T2I, I2I, structured JSON, hex colors, typography, multi-reference editing, and model-specific best practices for FLUX.2 and FLUX.1 families.
Create Model Context Protocol (MCP) servers that expose tools, resources, and prompts to Claude. Use when building custom integrations, APIs, data sources, or any server that Claude should interact with via the MCP protocol. Supports both TypeScript and Python implementations.
Create charts and visualizations from note data using Chart.js via dataviewjs. Use when user wants bar charts, line graphs, pie charts, or any data visualization. Requires Obsidian Charts plugin.
Use this skill when a task involves the local SToFM project in /DATA/disk0/zhaosy/home/SToFM, especially preprocessing spatial transcriptomics data for SToFM, generating cell embeddings with the cell encoder plus SE(2) Transformer pipeline, handling spatial coordinates, or preparing SToFM embeddings for downstream region segmentation or cell type annotation.
Propose high-fitness and high-diversity mutants of the VP1 capsid protein of Adeno-Associated Virus (AAV) through multi-round iterative optimization.
Propose high-fluorescence and high-diversity mutants of Green Fluorescent Protein (GFP) through multi-round iterative optimization.
This skill empowers Claude to perform feature engineering tasks for machine learning. It creates, selects, and transforms features to improve model performance. Use this skill when the user requests feature creation, feature selection, feature transformation, or any request that involves improving the features used in a machine learning model. Trigger terms include "feature engineering", "feature selection", "feature transformation", "create features", "select features", "transform features", "improve model performance", and similar phrases related to feature manipulation.
Regression Analysis Helper - Auto-activating skill for Data Analytics. Triggers on: regression analysis helper, regression analysis helper Part of the Data Analytics skill category.
Anomaly Detector - Auto-activating skill for Data Analytics. Triggers on: anomaly detector, anomaly detector Part of the Data Analytics skill category.
Automate budget vs actual variance analysis in Excel with flagging, commentary, and executive summaries for financial reporting and FP&A teams Activates when you request "excel variance analyzer" functionality.
