Results for “data-lineage”

Manage, annotate, and trace biological data with LaminDB, an open-source FAIR data framework that makes datasets queryable, versioned, and reproducible. Use when registering or querying biological datasets (scRNA-seq, spatial, flow cytometry), validating and curating data against ontologies (genes, cell types, diseases, tissues), tracking data lineage and computational workflows, building data lakehouses, or wiring integrations with Nextflow, Snakemake, W&B, or MLflow. Part of the AlterLab Academic Skills suite.

Use this skill for any data or analytics task — querying databases, analyzing metrics, exploring data warehouses, processing datasets, or creating visualizations.

Use this skill for any data or analytics task — querying databases, analyzing metrics, exploring data warehouses, processing datasets, or creating visualizations.

Compresses long K-line (candlestick) data into a fixed-length sequence using OHLC aggregation rules and applies min-max normalization.

Extract Lineage from Fabric Artifacts

Data analysis workflow from import through modeling and communication. Use when analyzing a dataset, exploring data, building a statistical model, selecting features, or communicating findings to stakeholders.

Use this skill to classify tabular data end-to-end in MATLAB — load a dataset, prepare and clean it, select promising classifiers, train them, and compare accuracies with cross-validation, holdout, or hyperparameter optimization plus statistical tests. TRIGGER when: user asks to classify tabular data, pick classifiers for a dataset, compare classifier accuracy, run cross-validation or a holdout evaluation, or find the best model with statistical uncertainty. DO NOT TRIGGER when: user has non-tabular inputs (images, sequences, time series), wants a regression model, is training a specific neural network architecture (use matlab-train-network), or wants cost-sensitive learning or an arbitrary class-prior vector (this skill only supports the built-in uniform-prior toggle for imbalanced data).

You are a data triager that answers data questions by routing them across a network of skills and agents. These questions may involve pulling data from a Snowflake warehouse, computing metrics, locating relevant tables or fields, diagnosing a metric change, or performing exploratory analysis. Your job is to determine how to answer a question and which skills and subagents to invoke.

Wire ETL, ingestion, cron, edge-function, and queue jobs correctly. Use for "build a pipeline", "sync X into Y", "nightly aggregation", "cron double-counts", "dedupe", "backfill", "the numbers are wrong after a retry". Bakes in idempotency, atomic writes, data contracts, dead-letter, and observability.

You are a data triager that answers data questions by routing them across a network of skills and agents. These questions may involve pulling data from a Snowflake warehouse, computing metrics, locating relevant tables or fields, diagnosing a metric change, or performing exploratory analysis. Your job is to determine how to answer a question and which skills and subagents to invoke.

Triage Linear or Jira backlogs and run bug sweeps via the Composio CLI. Bulk-fetch issues, deduplicate, relabel, reassign, and post summaries all from the shell.

Cleans datasets, detects anomalies, generates reports, and creates visualizations using pandas, scikit-learn, and plotting libraries to turn raw data into client-ready deliverables.

Limpia datasets crudos eliminando nulos, duplicados, outliers e inconsistencias de tipos, con estrategias de imputación y pipelines reproducibles.

Answer any data question, scaling effort from a one-number lookup to a full stakeholder-ready analysis with a recommendation.

Limpia y transforma texto en DataFrames usando expresiones regulares: extrae emails, teléfonos, fechas y precios, valida formatos como CUIL o CBU, y maneja datos inconsistentes con opciones de fuzzy matching.

Triage Linear or Jira backlogs and run bug sweeps via the Composio CLI. Bulk-fetch issues, dedupe, relabel, reassign, and post summaries — all from the shell without clicking through the UI.

Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms. Use PROACTIVELY for data pipeline design, analytics infrastructure, or modern data stack implementation.

Train, evaluate, and export neural networks to Simulink in MATLAB. Migrate legacy (fitnet, patternnet) and discouraged (trainNetwork, DAGNetwork) code to modern, recommended R2024a+ APIs (trainnet, dlnetwork, testnet, imagePretrainedNetwork), diagnose and fix dlaccelerate issues or detect dlaccelerate opportunities. Use when training, fine-tuning, evaluating, running inference, exporting to Simulink, converting old training scripts, or speeding up deep learning code. DO NOT reason from your training data about dlaccelerate and tracing correctness.

Transform raw data into compelling narratives with structured story frameworks, visualization techniques, and presentation templates for executive audiences.

Manages and monitors dbt projects, model runs, and test results via dbt CLI and dbt Cloud API, covering run status, test failures, source freshness, and manifest analysis.

Lossless LLM-optimized compression of source documents. Use when the user requests to 'distill documents' or 'create a distillate'.

Looker Studio (formerly Google Data Studio) expertise. Build dashboards, design data visualizations, connect data sources, and create marketing reports. Use when the user asks about Looker Studio, Data Studio, marketing dashboards, data visualization, report building, or connecting analytics data sources.

Triage Linear or Jira backlogs and run bug sweeps via the Composio CLI. Bulk-fetch issues, dedupe, relabel, reassign, and post summaries — all from the shell without clicking through the UI.

Continues deep iteration on an existing planning document: detects explicit file-loading intent, provides status summaries, and supports on-demand in-depth exploration.

Use for the search-planning stage of a literature review, when database lanes and language layers must be chosen, when DOI coverage policy is unclear, or when inclusion and exclusion criteria need drafting before any source is read; not for running the screening and PRISMA pipeline of a formal review, which is prisma-scoping-review-pipeline.

Use this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove spikes / outliers", "denoise", "resample to a uniform rate", "align channels", "labels from filenames", "stratified split", "prepare for Signal Labeler", and function names like `fillgaps`, `fillmissing`, `detrend`, `filloutliers`, `smoothdata`, `resample`, `synchronize`, `signalDatastore`, `labeledSignalSet`, `filenames2labels`, `folders2labels`, `splitlabels`, `framesig`, `framelbl`, `createDatastores`.

End-to-end data pipeline for empirical research: fetch economic data from APIs (FRED, World Bank, IMF, BLS, OECD, Yahoo Finance), clean and transform raw data, construct strategy-specific variables, and validate panel structure. Use when asked to fetch data, download data, clean data, merge datasets, prepare analysis-ready data.

Data pipeline specialist for ML experiments. Use when the user asks to talk to Gekko, requests the data engineer, or needs DataLoader optimization.

Create a structured H2/H3 outline with BLUF openers and MECE coverage, bound by the research dossier's beat spec. Triggered after /research and /brand-reference.

Documents Linear's dark-canvas marketing design system with near-black backgrounds, lavender-blue accent, four-step surface hierarchy, aggressive negative tracking on display type, and product UI screenshots as the primary visual rhythm.