Total 55,549 skills, Data Processing has 2840 skills
Showing 12 of 2840 skills
Access protein metadata, function, taxonomy, and sequences across UniProtKB, UniParc, and UniRef. Use when searching for proteins, mapping identifiers, or retrieving functional annotations and publications. Don't use for sequence alignment, protein folding, or sequence similarity search (use specialized skills for those tasks).
Query the ENCODE Registry of cis-Regulatory Elements (cCREs) via the SCREEN GraphQL API, or make custom queries to the ENCODE Portal REST API for experiments and files (ChIP-seq peaks, etc.). Use when you want to query regulatory annotations or raw experimental data across human cell types.
Fetch Evolutionary Conservation scores (phyloP, phastCons) and Transcription Factor Binding Sites (TFBS) from the UCSC Genome Browser. Use when analyzing whether genomic variants or regions are evolutionarily conserved, functionally important, or bounded by TF regulators across major projects (ENCODE, JASPAR, ReMap).
Prioritize drug targets from a ranked gene list (e.g., scRNA-seq DE output) by orchestrating parallel API queries against UniProt, OpenTargets (with integrated DepMap CRISPR essentiality + gnomAD constraint), PubMed, the Human Protein Atlas (HPA), and ChEMBL tool compounds, then re-ranking by a composite score combining protein localization, druggability, disease genetics, tissue specificity (safety), focus-cell-type expression, CRISPR essentiality, LoF safety constraint, and research maturity. Use whenever the user wants to filter, triage, prioritize, or "do due diligence" on a list of candidate genes for drug discovery, especially after a DE / DEG analysis when they say things like "which of these should I follow up on", "filter for druggable targets", "make a target dossier", "rank these for tractability", "annotate these genes for druggability", or "build a target report". Trigger even when the user says just "filter these candidate genes" or hands over a CSV from a DE pipeline.
Guides cleaning and standardizing tabular datasets before analysis, modeling, or reporting—profiling, quality rules, missing values, duplicates, outliers, type coercion, encoding fixes, record linkage, deduplication, high-level PII handling (not legal advice), actuarial/insurance field scrubbing, reproducible scrub pipelines, validation checks, and sign-off. Distinct from warehouse ETL or statistical modeling. Use when the user asks for "data scrubbing", "clean this dataset", "scrub the data", "data cleaning", "dedupe records", "handle missing values", "outlier treatment", "standardize columns", "data quality rules", "profile this table", or "prepare data for modeling". Not warehouse pipelines (data-warehouse-engineer), ML modeling (data-scientist, actuary), privacy programs (compliance-engineer), FinOps only (finops-analyst), or assumption governance (assumption-setting).
Search, qualify, and enrich people and companies. Use this skill whenever the user wants to find professionals, candidates, or KOLs by title, company, location, seniority, or audience; enrich known contacts with email, phone, or LinkedIn; research companies for industry, funding, tech stack, or hiring activity; look up someone's contact info; source candidates for recruiting; generate B2B lead lists; or perform background web research on people or organizations. Trigger this skill even when the user doesn't explicitly say "search" or "enrich" — any mention of finding contacts, sourcing, prospecting, looking up a person or company, or gathering business intelligence should activate it.
Convert any data file to another format: CSV, Parquet, JSON, Excel, GeoJSON, and more. Use when the user says "convert to parquet", "save as xlsx", "export as JSON", "make this a CSV", "turn into parquet", or any variation of format-to-format conversion for data files. Also triggers when the user wants to write Parquet, Excel, or other binary formats that Claude cannot produce natively.
Placekey integration. Manage data, records, and automate workflows. Use when the user wants to interact with Placekey data.
MPSTATS marketplace analytics API. Use when working with MPSTATS API, Wildberries analytics, Ozon analytics, Yandex Market analytics, marketplace data, product research, sales analytics, competitor analysis, niche research, SKU analysis, seller analytics, brand analytics.
ALWAYS use when: creating/editing marimo notebooks, working with any .py file containing @app.cell decorators, building reactive Python notebooks, doing exploratory data analysis in notebook form, converting Jupyter (.ipynb) to marimo, or when user mentions "marimo", "reactive notebook", or asks for an interactive Python notebook. Covers marimo CLI (edit, run, convert, export), UI components (mo.ui.*), layout functions, SQL integration, caching, state management, and wigglystuff widgets. If a task involves notebooks and Python, invoke this skill first.
Profile a new tabular dataset before modeling. Find target leakage, missing data patterns, high-cardinality categoricals, near-constant features, redundant pairs, and non-linear relationships that Pearson correlation misses. Use whenever the user hands you a CSV or parquet and asks "what should I do with this?" Always run this skill before training any model on data you haven't seen before.
Reference portfolio demonstrating Azure data engineering patterns, Medallion architecture, and end-to-end analytics solutions