Results for “data-readiness”

Audits scraped affiliation data against logo strip and social proof best practices, evaluating logo quality, grouping, copy framing, prominence ordering, and strip readiness.

Run an AI-readiness assessment on the current repository and generate a static HTML dashboard at reports/index.html.

Use this when reviewing whether an app, service, worker, CLI, or serverless system is ready for its target environment with respect to config, secrets, migrations, dependencies, observability, rollback, and runtime constraints.

Use to build Science's data, code, and materials availability — mandatory deposition in approved repositories, accession numbers, a compliant data-availability statement, and materials/reagent sharing.

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.

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

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

Persists app data on Android using Room, SQLite, SharedPreferences, DataStore, and encryption.

Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.

Guides backend development across API design, authentication, databases, performance, security, scalability, and testing, with quick decision guides and defaults for production-ready systems.

Design reliable, scalable, and maintainable data systems by applying principles from storage engines, replication, partitioning, transactions, and consistency models.

Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence.

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

所有联网操作必须通过此 skill 处理，包括：搜索、网页抓取、登录后操作、网络交互等。触发场景：用户要求搜索信息、查看网页内容、访问需要登录的网站、操作网页界面、抓取社交媒体内容（小红书、微博、推特等）、读取动态渲染页面、以及任何需要真实浏览器环境的网络任务。

Provides a SQLite-backed persistence layer for skill execution metrics, feedback, improvement candidates, and version history, with query and maintenance workflows.

Assesses a project against all five SOC 2 Trust Service Criteria, producing a control-by-control PASS/PARTIAL/FAIL matrix with evidence references, remediation roadmap, and evidence collection checklist for audit readiness.

Internal technical readiness drill assistant for release and handoff.

Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data.

Audits deployment readiness across CI/CD, containers, monitoring, IaC, secrets, CDN, and DNS, scoring each area and identifying critical gaps, with optional auto-fix via chained sub-skills.

Prepare, audit, or revise Nature-ready Data Availability statements, data repository plans, dataset citations, and FAIR metadata checklists for manuscripts. Use when the user asks about Nature data availability, research data sharing, repository selection, accession numbers, restricted or sensitive data, source data, supplementary datasets, DataCite-style dataset references, FAIR metadata for academic publication, or Chinese-to-English data availability wording for Chinese-speaking authors preparing Nature-family submissions. Also trigger on general academic-writing data needs even without the word "Nature", such as writing a data availability statement for any journal, code/data sharing sections, repository selection while writing a paper, and Chinese phrasings like 数据可用性声明、数据可用性、 数据共享、代码可用性、学术写作数据声明、写数据声明、数据存放、数据仓库选择.

Runs a full website SEO audit by crawling up to 500 pages, detecting business type, delegating to specialist subagents, and producing a prioritized action plan with a health score.

Expert data engineer specializing in building reliable data pipelines, lakehouse architectures, and scalable data infrastructure. Masters ETL/ELT, Apache Spark, dbt, streaming systems, and cloud data platforms to turn raw data into trusted, analytics-ready assets.

Clean and transform messy data for analysis in Python, R, or Stata

所有联网操作必须通过此 skill 处理，包括：搜索、网页抓取、登录后操作、网络交互等。

Big Data Based Analysis Skill

Use to build PNAS's Data Availability Statement and deposition plan — mandatory deposition of data in an approved repository at submission, accession numbers/DOIs, public + archived code, and materials sharing. "Available on request" is not sufficient for primary data.

Guides backend development across API design, authentication, security, performance, architecture, and testing, with decision guides and defaults for production-ready systems.

Implements database replication, failover, backups, connection pooling, and monitoring for production PostgreSQL, MySQL, and MongoDB systems.

Big Data Testing Expert Skill

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.