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Get Started Free →Pipelines, schema design, and data-quality conventions.
AI agent specialized in monitoring Excel files and extracting key sales metrics (MTD, YTD, Year End) for internal live reporting
Build and manage ETL pipelines for data migration with transformation, CDC, and monitoring
Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness. Returns a structured report with exact patch locations.
Standardizes a free-form postal address into one canonical mailing form — uppercased, punctuation stripped, USPS-approved street-suffix and directional abbreviations, secondary-unit designators, two-letter state, ZIP+4, one element per line. Use when normalizing, deduplicating, or cleaning mailing addresses for storage, matching, or bulk import. Do NOT use for geocoding an address to coordinates, verifying an address is deliverable, or parsing an address into typed fields without reformatting.
Designs robust ETL/ELT data pipelines covering ingestion, idempotency, schema evolution, orchestration, and data quality validation. Use this skill when the user asks to design, build, or review a data pipeline, ingest data from APIs/databases/files into a warehouse or lake, set up batch or streaming ETL/ELT, choose an orchestrator (Airflow, Dagster, Prefect, dbt), make a pipeline idempotent or backfill-safe, handle late-arriving or duplicate data, manage schema drift/evolution, add data quality
Interact with a web-based Excel-like spreadsheet API for AI agents. Use when you need to create, manipulate, or query spreadsheet data programmatically, or when the user asks to work with Excel-like data. Authenticate using API key in Authorization header.
Download large datasets from NCBI efficiently using history server, batching, and rate limiting. Use when performing bulk sequence downloads, handling large query results, or production-scale data retrieval.
Discover available blockchain datasets for Turbo pipelines. Use when exploring what data Goldsky offers, finding chain prefixes, or selecting the right dataset for a pipeline.
MongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on \"design schema\", \"embed vs reference\", \"MongoDB data model\", \"schema review\", \"unbounded arrays\", \"one-to-many\", \"tree structure\", \"16MB limit\", \"schema validation\", \"JSON Schema\", \
Run ICE-Crawler’s Frost→Glacier→Crystal pipeline to ingest repositories safely, emit bounded artifact bundles, and hand off sealed fossils for downstream agents.
Expert guidance for Apache Arrow, the cross-language columnar memory format for analytics workloads. Helps developers use Arrow for high-performance data interchange between systems, zero-copy reads, and efficient columnar processing in Python (PyArrow) and JavaScript (Arrow JS).
Partitioning, ordering guarantees and consumer-group rebalances: what a key buys you, and the reprocessing a retry actually causes.
Create portable bioinformatics pipelines with Workflow Description Language (WDL) using Cromwell or miniwdl execution engines. Use when running GATK best practices pipelines, working with Terra/AnVIL platforms, or building workflows for cloud execution on Google Cloud or AWS.
Use this skill when designing or reviewing a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features
Validate alignment quality with insert size distribution, proper pairing rates, GC bias, strand balance, and other post-alignment metrics. Use when verifying alignment data quality before variant calling or quantification.
Generate consensus FASTA sequences by applying VCF variants to a reference using bcftools consensus. Use when creating sample-specific reference sequences or reconstructing haplotypes.
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
PDF 数据提取工具。当用户提到"PDF 提取"、"PDF 转 Markdown"、"PDF 解析"、"提取 PDF 内容"、"PDF 转 JSON"、"RAG PDF"时使用。OpenDataLoader PDF 是目前基准测试第一的 PDF 解析器，支持本地模式（快速、确定）和混合 AI 模式（复杂表格、扫描件、公式），输出 Markdown、JSON（带边界框）、HTML。适用于需要从 PDF 提取结构化数据用于 RAG/LLM pipeline，或需要批量处理 PDF 文档的场景。
Analyzes and optimizes Apache Spark jobs for performance, cost, and resource utilization
Sort alignment files by coordinate or read name using samtools and pysam. Use when preparing BAM files for indexing, variant calling, or paired-end analysis.
Important: Neon is no longer just a Serverless Postgres provider. Neon is the backend for apps and agents with Serverless Postgres, Auth, Functions, Storage, and an AI Gateway: instant, branchable, serverless.
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
You are an expert in Airbyte, the open-source data integration platform with 300+ pre-built connectors. You help developers sync data from SaaS tools, databases, and APIs into data warehouses and lakes — handling incremental syncs, CDC (Change Data Capture), schema evolution, and error recovery for production data pipelines.
DEPRECATED in v0.2.0 -- use browser-extract instead; this is a thin shim for backward compatibility, removed in v0.3.0
Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.
Load gene expression count matrices from various formats including CSV, TSV, featureCounts, Salmon, kallisto, and 10X. Use when importing quantification results for downstream analysis.
Import CSV or Excel files into seekdb vector database and manage collections. Supports automatic vectorization of specified columns using embedding functions. When users need to: (1) Read and preview Excel files, (2) Import CSV/Excel data into seekdb, (3) Create vector collections from tabular data, (4) Vectorize specific text columns for semantic search, (5) Batch insert product/document data with embeddings, (6) Delete collections, or (7) Access sample data files (sample_products.csv/xlsx) for
Optimizes PostgreSQL queries, configures replication, implements advanced features including EXPLAIN analysis, JSONB operations, extension usage, and VACUUM tuning.
Detect when content on a website changes and get notified by webhook or email. Each page in a check is labeled `same`, `new`, `changed`, `removed`, or `error`, with snapshot history and structured per-field diffs so notifications can be wired straight into downstream tools.
Use when the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas. Provides chDB DataStore — same pandas API, ClickHouse engine underneath. Also handles reading from S3, MySQL, PostgreSQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake as DataFrames and joining across sources. TRIGGER when: user mentions DataFrame, parquet, csv, "fast pandas", "speed up pandas", or cross-source DataFrame joins; user imports `chdb
Scrape sites with stealth browsing and Cloudflare bypass.
Implements Change Data Capture patterns for real-time data integration
Filter alignments by flags, mapping quality, and regions using samtools view and pysam. Use when extracting specific reads, removing low-quality alignments, or subsetting to target regions.
PostgreSQL best practices, query optimization, schema design, security, operations, and performance tuning. Load when working with Postgres databases.
PostgreSQL database patterns for query optimization, schema design, indexing, and security. Based on Supabase best practices.
Designs optimal windowing strategies for stream processing
PostgreSQL database specialist for query optimization, schema design, security, and performance. Use PROACTIVELY when writing SQL, creating migrations, designing schemas, or troubleshooting database performance. Incorporates Supabase best practices.
Merge, concatenate, sort, intersect, and subset VCF files using bcftools. Use when combining variant files, comparing call sets, or restructuring VCF data.
Create, manipulate, and convert bedGraph files for genome browser visualization. Covers bedGraph format, conversion to/from bigWig, normalization, and signal processing. Use when handling coverage and signal tracks from ChIP-seq, ATAC-seq, or RNA-seq.
Normalize indel representation and split multiallelic variants using bcftools norm. Use when comparing variants from different callers or preparing VCF for downstream analysis.
从 PubMed/PMC 论文取结构化全文并按章节切分为 RAG 友好的 chunk。当用户需要批量抽取论文全文、按 Methods/Results 等章节分段、提取表格上下文、构建文献向量库时，使用本 skill。支持 PMID（标题+摘要）和 PMCID（全文，开放获取子集）。token 级 RAG chunking 不跨章节，支持缓存与限流。
Design a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features
ClickHouse is a columnar OLAP database built for real-time analytical queries over massive datasets. It supports a familiar SQL interface with powerful aggregation functions, materialized views for pre-computed rollups, and high-throughput inserts that make it ideal for event tracking, time-series analytics, and ad-hoc exploration of billions of rows.
Agent web access acceleration layer — reads any URL as structured JSON. Cache-first (public library hit = 0 tokens). The smart alternative to raw web_fetch.
Manage Goldsky secrets for pipeline sink credentials. Use when creating secrets for PostgreSQL, ClickHouse, Kafka, or other sinks, or when managing existing secrets.
Work with sparse matrices for memory-efficient storage of count data. Use when dealing with single-cell data or large bulk RNA-seq datasets where most values are zero.
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
You are an **Identity Graph Operator**, the agent that owns the shared identity layer in any multi-agent system. When multiple agents encounter the same real-world entity (a person, company, produc...
Route web scraping, crawling, site-ingestion, URL discovery, structured extraction, and public-site capture tasks to the right local stack. Make sure to use this whenever the user wants to scrape a site, crawl docs, export markdown from many pages, map a website, capture a public website for later analysis, understand a website thoroughly, gather comprehensive information from a site, extract structured data from pages, or recover blocked public web content.
Design MongoDB architectures with document modeling, indexing (ESR rule), sharding, aggregation pipelines, replica sets, and WiredTiger tuning.
Google BigQuery API integration with managed OAuth. Run SQL queries, manage datasets and tables, and analyze data at scale. Use this skill when users want to query BigQuery data, create or manage datasets/tables, run analytics jobs, or work with BigQuery resources. For other third party apps, use the api-gateway skill (https://clawhub.ai/byungkyu/api-gateway).
OverKill Hill P³ SharePoint List database architecture reviewer. Use when designing, reviewing, or hardening SharePoint Online Lists used as lightweight databases, controlled vocabularies, lookup-backed registries, or CMDB-lite data stores. Also activate when the user asks about list thresholds, indexed columns, lookup relationships, required fields, content types, attachments, offline sync, Quick Edit, SharePoint UI-only build constraints, or whether a SharePoint list setting is safe for databa
Converts inconsistent job titles into standardized role, department, and seniority labels. Use when the user provides records with free-text titles and wants consistent role family, department, and seniority fields.
Expert database specialist focusing on schema design, query optimization, indexing strategies, and performance tuning for PostgreSQL, MySQL, and modern databases like Supabase and PlanetScale.
Write and understand SQL transforms for Turbo pipelines. Use when decoding EVM logs, filtering events, casting types, chaining transforms, combining data with UNION ALL, or building complex data processing logic.
Build and operate reproducible genomics workloads on DNAnexus with the dx CLI, dxpy, apps/applets, native workflows, dxCompiler, and Nextflow. Use for DNAnexus data transfers, dxapp.json development, execution monitoring, workflow import, and project automation.
Extract structured data from IFC (Industry Foundation Classes) files using IfcOpenShell. Parse BIM models, extract quantities, properties, spatial relationships, and export to various formats.
Selects and configures optimal incremental model strategies
Transform and manipulate JSON data — jq queries, schema validation, nested object traversal, and JSON-to-CSV or flat-file conversions.
Profile construction data to understand characteristics, distributions, quality metrics, and patterns. Essential for data quality assessment and ETL planning.
Build, run, and debug Nextflow data pipelines and nf-core workflows end to end. Use whenever the user mentions Nextflow, nf-core, .nf files, nextflow.config, DSL2, processes/channels/operators, samplesheets, or wants to run a community pipeline (e.g. nf-core/rnaseq, nf-core/sarek), write or test a module/subworkflow with nf-test, configure executors/containers (Docker, Singularity/Apptainer, Conda, Wave), scale a workflow to HPC/SLURM or cloud (AWS Batch, Google Batch, Azure, Kubernetes), or deb
X (Twitter) data extraction and monitoring via Xquik: tweet search, user lookup, follower extraction, giveaway draws, trending topics, account monitoring with webhooks, reply/retweet/quote extraction, community and Space data, follow checks. 22 MCP tools + REST API.
Analyze ERP system integration for construction data flows. Map and optimize data flows between ERP modules
Classify construction data by type (structured, unstructured, semi-structured). Analyze data sources and recommend appropriate storage/processing methods
Review a database migration before it runs in production. Covers lock acquisition and how long each statement holds one, the difference between a metadata lock and a row lock, adding a column with a default on a large table, backfilling in batches with a bounded window, creating an index concurrently and what happens when that fails halfway, dropping a column that application code still references, renaming in two deploys instead of one, foreign keys added as NOT VALID and validated separately,
Detect and map data silos in construction organizations. Identify disconnected data sources and integration opportunities
Builds evidence-only Kimball semantic layers from APIs, CLIs, or tables with an HTML decision workbench; emits OKF/YAML. Use this skill when users request semantic layers, dimensional models, ontology, OKF, 语义层、接口转语义、维度建模或本体.
Designs and optimizes One Big Table (OBT) patterns
Maps the site first to discover pages, then scrapes each one into nested directories under `.firecrawl/`. All scrape options work with download. Always pass `-y` to skip the confirmation prompt.
Use when listing, showing, searching, creating, testing, or removing MaxCompute UDFs and UDF resources.
Comprehensive variant filtering including GATK VQSR, hard filters, bcftools expressions, and quality metric interpretation for SNPs and indels. Use when filtering variants using GATK best practices.
Universal SQL code review assistant that performs comprehensive security, maintainability, and code quality analysis across all SQL databases (MySQL, PostgreSQL, SQL Server, Oracle). Focuses on SQL injection prevention, access control, code standards, and anti-pattern detection. Complements SQL optimization prompt for complete development coverage.
Download sequencing data from NCBI SRA using the SRA toolkit. Use when downloading FASTQ files from SRA accessions, prefetching large datasets, or validating SRA downloads.
Process large-scale data with Apache Spark. Use when a user asks to process big data, run distributed computations, build ETL pipelines, perform data analysis at scale, or use PySpark for data engineering.
You are a **Data Engineer**, an expert in designing, building, and operating the data infrastructure that powers analytics, AI, and business intelligence. You turn raw, messy data from diverse sour...
The outcome of this skill should be a created Neon branch (or a clear, actionable next step if creation cannot proceed).
对Excel文件进行文本标准化清洗（如去除异常前缀、提取纯中文字符等），并，最终输出清洗后的Excel文件并提供下载链接。
Dataset versioning skill using DVC for tracking data changes, managing data pipelines, and ensuring reproducibility.
Expert database administrator specializing in modern cloud databases, automation, and reliability engineering.
Sets up, manages, and executes queries against Cloud Firestore database instances. You MUST unconditionally activate this skill if you plan to use Firestore in any way. Use when listing or creating Firestore databases, configuring security rules, designing data models, writing client SDK queries, or checking indexes.
Design and architect Turbo pipelines. Use when choosing between source types (dataset vs kafka), designing data flow patterns (fan-in, fan-out, linear), sizing resources, planning multi-chain deployments, or deciding how to split work across pipelines.
Build globally distributed apps with Azure Cosmos DB. Work with multiple data models (document, key-value, graph), configure global replication with tunable consistency levels, manage throughput with RU/s, and query with SQL API.
Content-addressed provenance DAG for research lineage tracking. Use for: recording which pipeline stage produced which artifact, querying edges between recorded nodes, running a DAG-wide review pass. CLI: python -m infrastructure.provenance {list,record-artifact,review}. Library: infrastructure.provenance.Provenance (record/link/get/list/query). Orchestrator: scripts/pipeline/stage_09_provenance_record.py --project {name} --stage NAME
Build a fully automated AI-powered data collection agent for any public source — job boards, prices, news, GitHub, sports, anything. Scrapes on a schedule, enriches data with a free LLM (Gemini Flash), stores results in Notion/Sheets/Supabase, and learns from user feedback. Runs 100% free on GitHub Actions. Use when the user wants to monitor, collect, or track any public data automatically.
SQL database migration strategies with zero-downtime deployment, rollback plans, and data integrity validation for PostgreSQL, MySQL, and SQL Server. Use when designing schema changes, adding columns/indexes, renaming tables, or planning production migrations with minimal downtime.
Use this skill when designing or reviewing a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features
Index at Creation Time
Extracts tables and headers from CSV files and validates them against a schema. Use when the user mentions CSV parsing, header validation, or tabular data cleanup.
Design high-performance PostgreSQL databases with schema optimization, indexing strategies, partitioning, replication, and PostgreSQL 17 tuning.
Chief Data Officer advisory for startups: AI training data rights and consent provenance, data product strategy (warehouse vs lakehouse vs mesh, build-vs-buy), B2B customer-data-as-asset valuation and M&A readiness, data team org evolution. Use when deciding whether to train models on customer data, choosing data architecture, valuing data for fundraising or M&A, sequencing data hires, or when user mentions CDO, chief data officer, data strategy, data mesh, lakehouse, training data, data product
Apache Cassandra is a distributed NoSQL database designed for high availability and linear scalability. Learn CQL (Cassandra Query Language), data modeling with partition keys, replication strategies, and integration with Node.js using the DataStax driver.
Execute database migrations across ORMs and platforms with zero-downtime strategies, data transformation, and rollback procedures. Use when migrating databases, changing schemas, performing data transformations, or implementing zero-downtime deployment strategies.
Automate Asin Data API tasks via Rube MCP (Composio). Always search tools first for current schemas.
Data model and relationship diagrams in Mermaid - entity-relationship (ER) diagrams, class diagrams, schema documentation. Use when the user wants to diagram a database schema, data model, object structure, class hierarchy, or entity relationships with cardinality. Always load okhp3-mermaid-core first for audience/type/theming.
Convert canonical training examples to JSONL format for training frameworks
Use when the user explicitly asks to build, refresh, onboard, or maintain a MaxCompute semantic package.
End-to-end ATAC-seq workflow from FASTQ files to differential accessibility and TF footprinting. Covers alignment, peak calling with MACS3, QC metrics, and optional TOBIAS footprinting. Use when running end-to-end ATAC-seq analysis from FASTQ to differential accessibility.
Analyzes and optimizes SQL queries across different data warehouse platforms (Snowflake, BigQuery, Redshift, Databricks) with platform-specific recommendations.
AI-powered autonomous extraction. The agent navigates sites and extracts structured data (takes 2-5 minutes).
Use when the user asks to design database schemas, plan data migrations, optimize queries, choose between SQL and NoSQL, or model data relationships.