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Found 372 Skills
Use this skill when the user asks about Goldsky Compose — the offchain-to-onchain TypeScript framework for onchain oracles, keepers, circuit breakers, and cross-chain automation. Triggers on: 'goldsky compose', 'compose.yaml', 'compose deploy/init/dev', 'compose task', 'cron task onchain', 'sponsored gas', 'writeContract from TypeScript', 'build a price oracle', 'resolve prediction market', 'onchain event listener', 'HTTP-triggered task', 'smart wallet'. Also use when the user wants to run TypeScript against EVM chains with managed gas, schedule onchain writes via cron, react to onchain events, or deploy a serverless task with secrets and a smart wallet. For debugging a broken app, use /compose-doctor. For manifest/CLI/API lookups, use /compose-reference. Do NOT trigger on Goldsky Turbo, Mirror, Subgraphs, Edge, or Datasets — those belong to their respective skills.
Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.
Use to run AutoMagicCalib on local MP4s, RTSP, or the bundled sample dataset, and to deploy vss-auto-calibration when needed. Not for non-AMC calibration or runtime analytics.
Used for smoke or dataset finetuning of NV-Segment-CT VISTA3D on CT NIfTI labels. Not for clinical validation.
Use this skill when working with Brain Imaging Data Structure (BIDS) datasets: organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS derivatives.
Standard single-step train/eval/export workflow for any TAO model. Use when training a TAO model on a dataset without iterative data augmentation, AutoML, or DEFT loops. Trigger phrases include "single train run", "train then evaluate then export", "plain TAO training", "normal training", "no AutoML", "skip the loop". Routes through the per-model SKILL.md for action specifics and through `tao-launch-workflow` for platform/credentials/dataset intake.
Identify which field values correlate with bad behavior (slowness, errors, anomalies, unusual values) using phi-coefficient correlation analysis over OPAL. Works on any time-series data — metrics, structured logs, span/trace data, or any dataset where rows can be split into a 'bad' and 'good' cohort by a threshold. Use when: (1) User asks for root-cause analysis on a dataset or metric (2) User wants to know what attributes / dimensions / values are most associated with a failure mode, anomaly, or unusual cohort (3) Investigating which services, hosts, regions, namespaces, or attributes drive outliers (4) User mentions phi coefficient, correlation, or outlier detection (5) User asks 'why is X slow/failing', 'what caused the errors on X', or 'what's different about the bad cohort'.
Use when creating or improving golden datasets for AI evaluation. Defines quality criteria, curation workflows, and multi-agent analysis patterns for test data.
Build this skill automates the adaptation of pre-trained machine learning models using transfer learning techniques. it is triggered when the user requests assistance with fine-tuning a model, adapting a pre-trained model to a new dataset, or performing... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
Unlock the surprising speed of SQLite in Flutter for building responsive UIs, showcasing its ability to handle large datasets with synchronous queries and optimized configurations.
Production-ready single-cell and expression matrix analysis using scanpy, anndata, and scipy. Performs scRNA-seq QC, normalization, PCA, UMAP, Leiden/Louvain clustering, differential expression (Wilcoxon, t-test, DESeq2), cell type annotation, per-cell-type statistical analysis, gene-expression correlation, batch correction (Harmony), trajectory inference, and cell-cell communication analysis. NEW: Analyzes ligand-receptor interactions between cell types using OmniPath (CellPhoneDB, CellChatDB), scores communication strength, identifies signaling cascades, and handles multi-subunit receptor complexes. Integrates with ToolUniverse gene annotation tools (HPA, Ensembl, MyGene, UniProt) and enrichment tools (gseapy, PANTHER, STRING). Supports h5ad, 10X, CSV/TSV count matrices, and pre-annotated datasets. Use when analyzing single-cell RNA-seq data, studying cell-cell interactions, performing cell type differential expression, computing gene-expression correlations by cell type, analyzing tumor-immune communication, or answering questions about scRNA-seq datasets.
Implement Syncfusion React HeatMap Chart component for data visualization. Use this skill when user needs to create heatmaps, visualize 2D data patterns, display matrix data with color gradients, configure axes (numerical/categorical/datetime), implement legends, handle cell selection, apply custom styling, or work with large datasets. Covers installation, data binding, axis configuration, appearance customization, interaction patterns, tooltips, events, and accessibility.