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Found 20 Skills
Standardize and format phone numbers with international support, validation, and multiple output formats.
Use this skill any time the user wants to analyze data, create charts, or build data visualizations. This includes: sales analysis, financial modeling, cohort analysis, funnel analysis, A/B test results, KPI tracking, data reports, revenue breakdowns, user retention analysis, conversion rate analysis, CSV summarization, and dashboard creation. Also trigger when: user says 分析这组数据, 做个图表, 数据可视化, 销售分析, 漏斗分析, 留存分析, 做个数据报表. If data needs to be analyzed or visualized, use this skill.
Expert in high-performance CSV processing, parsing, and data cleaning using Python, DuckDB, and command-line tools. Use when working with CSV files, cleaning data, transforming datasets, or processing large tabular data files.
Parse, transform, and analyze CSV files with advanced data manipulation capabilities.
Bulk data enrichment. Adds web-sourced fields (CEO names, funding, contact info) to lists of companies, people, or products. Use for enriching CSV files or inline data.
Transform data between JSON, CSV, and other formats with filtering, mapping, and flattening. Use when: (1) Converting API responses to CSV, (2) Processing data pipelines, (3) Extracting specific fields, or (4) Flattening nested structures.
This skill should be used when analyzing CSV datasets, handling missing values through intelligent imputation, and creating interactive dashboards to visualize data trends. Use this skill for tasks involving data quality assessment, automated missing value detection and filling, statistical analysis, and generating Plotly Dash dashboards for exploratory data analysis.
Track product champions for job changes and qualify their new companies against ICP. Takes a CSV of known champions (with LinkedIn URLs), creates a baseline snapshot via Apify enrichment, then detects when champions move to new companies. Scores new companies on a 0-4 ICP fit scale. Outputs a downloadable CSV of movers with qualification verdicts.
Create interactive Sankey diagrams for flow visualization from CSV, DataFrame, or dict data. Supports node/link styling and HTML/PNG/SVG export.
Use when asked to parse, normalize, standardize, or convert dates from various formats to consistent ISO 8601 or custom formats.
Extract study data into a structured table (`papers/extraction_table.csv`) using the protocol’s extraction schema. **Trigger**: extraction form, extraction table, data extraction, 信息提取, 提取表. **Use when**: systematic review 在 screening 后进入 extraction（C3），需要把纳入论文按字段落到 CSV 以支持后续 synthesis。 **Skip if**: 还没有 `papers/screening_log.csv` 或 protocol 未锁定。 **Network**: none. **Guardrail**: 严格按 schema 填字段；不要在此阶段写 narrative synthesis（那是 `synthesis-writer`）。
Validate email addresses before campaign sending. Takes a contact CSV, validates each email via a verification provider, removes invalid/do_not_mail/abuse/unknown addresses, and optionally cleans them from sequencer campaigns. Outputs a verified CSV ready for campaign-sending. Fits between email-generation and campaign-sending in the pipeline. Triggers on: "verify emails", "validate emails", "email verification", "clean emails", "check emails before sending", "remove bad emails", "email hygiene".