anthropics/knowledge-work-plugins

Run a WCAG 2.1 AA accessibility audit on a design or web page. Use when reviewing color contrast, keyboard navigation, touch target size, or screen reader behavior before handoff.

Answer data questions -- from quick lookups to full analyses. Use when looking up a single metric, investigating what's driving a trend or drop, comparing segments over time, or preparing a formal data report for stakeholders.

Set up your bio-research environment and explore available tools. Use when first getting oriented with the plugin, checking which literature, drug-discovery, or visualization MCP servers are connected, or surveying available analysis skills before starting a new project.

Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use for analyzing RNA-seq, WGS/WES, or ATAC-seq data from local FASTQs or public GEO/SRA datasets.

Build a self-contained HTML dashboard with charts, filters, and tables. Use when creating an executive overview with KPI cards, turning query results into a shareable report, building a team monitoring snapshot, or needing a browser-openable file with multiple charts and filters.

Customize a Claude Code plugin for a specific organization's tools and workflows. Use when setting up or modifying a plugin's configuration to match team conventions and services.

Guides users through creating a Claude Cowork plugin from scratch in a five-phase cowork session, delivering a ready-to-install .plugin file.

Create publication-quality visualizations with Python. Use when turning query results or a DataFrame into a chart, selecting the right chart type for a trend or comparison, generating a plot for a report or presentation, or needing an interactive chart with hover and zoom.

Package a support issue into a structured escalation brief for engineering, product, or leadership. Useful when a bug needs engineering attention, multiple customers report the same issue, a customer threatens to churn, or an issue is past its SLA.

Multi-source research on a customer question or topic with source attribution. Use when a customer asks something you need to look up, investigating whether a bug has been reported before, checking what was previously told to a specific account, or gathering background before drafting a response.

Extract company-specific data knowledge from analysts to create or improve tailored data analysis skills for AI assistants. Helps Claude understand your data warehouse, metrics definitions, and query patterns.

Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.

Provides structured design feedback across usability, visual hierarchy, consistency, and accessibility. Useful for reviewing mockups, screenshots, or Figma designs at any stage from early exploration to final polish.

Draft a professional customer-facing response tailored to the situation and relationship. Use when answering product questions, handling escalations or outages, delivering bad news, declining feature requests, or replying to billing issues.

Profile and explore a dataset to understand its structure, quality, and patterns. Helps when encountering new data—checks null rates, column distributions, and identifies issues like duplicates or outliers.

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types.

Drafts publish-ready knowledge base articles from resolved support issues, common questions, or documented workarounds. Structures content for searchability and self-service.

Reconcile accounts by comparing GL balances to subledgers, bank statements, or third-party data. Use when performing bank reconciliations, GL-to-subledger recs, intercompany reconciliations, or identifying and categorizing reconciling items.

Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries.

Helps scientists systematically select research problems, generate ideas, troubleshoot projects, and make strategic decisions through a structured conversational framework.

Performs quality control on single-cell RNA-seq data using scverse best practices with MAD-based filtering and comprehensive visualizations. Use for QC analysis, filtering low-quality cells, or assessing data quality in .h5ad or .h5 files.

Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.

Triage and prioritize a support ticket or customer issue. Use when a new ticket comes in and needs categorization, assigning P1-P4 priority, deciding which team should handle it, or checking whether it's a duplicate or known issue before routing.

Review analyses for methodology, accuracy, and bias before stakeholder presentations. Spot-check calculations, verify SQL results, and assess if conclusions are data-supported.

Translates natural language data requests into optimized SQL queries tailored to your database dialect. Ideal for building complex queries with CTEs, joins, and aggregations, and for performance optimization on large partitioned tables.

Provides deep learning-based single-cell analysis workflows using scvi-tools for data integration, batch correction, multi-modal analysis, spatial transcriptomics, and RNA velocity.