How does this skill ensure my data reports are accurate compared to just asking a standard AI?

This skill eliminates 'hallucinated' numbers by using a five-stage gate-based workflow that validates every calculation against your raw CSV or SQL data before it reaches the final report.

Which AI agents or platforms can run the Data Insight Runbook?

The skill is optimized for data environments that support Node.js and DuckDB, making it compatible with most modern AI agent platforms that allow for local file processing and script execution.

Do I need to install any additional software to use the DuckDB features?

For standard CSV files, the skill works out of the box; however, for complex SQL queries or datasets exceeding 2,000 lines, a local installation of the DuckDB CLI is recommended for peak performance.

What exactly is included in the purchase of this skill?

You receive the full runbook workflow, standardized Markdown reporting templates, and the rigorous logic gates designed for profiling, anomaly detection, and visualization.

Are there any limits on the size of the data files I can analyze?

While the skill handles standard CSV and SQL inputs seamlessly, very large datasets are restricted by your AI agent's context window; we recommend using the integrated DuckDB workflow to process these files locally.

Will I receive updates if the underlying frameworks or best practices change?

Yes, your purchase includes access to future refinements of the analytical logic and reporting templates to ensure compatibility with evolving data processing libraries.

data insight runbook

Example session with this skill installed

I have a sales_data.csv file. Help me analyze the GMV trends for the last quarter, calculate the month-over-month growth, and identify any anomalous sales days.

Read your context and instructions
Compiled the data insight runbook
Generated the data export

I have completed the analysis of sales_data.csv. The data contains 1,250 records with no significant missing values in the GMV or Date columns. I used csv-profile.mjs for initial exploration and calculated metrics using the standard growth formula.

Sales Data Insight Report

核心结论

GMV 稳步增长：本季度总 GMV 达 425,000，环比增长 12.4%。
高峰识别：11 月 11 日记录到单日最高 GMV (24,300)，超出日均值 3.2 倍。
异常预警：10 月 15 日出现 GMV 异常下跌（-85%），经查为支付接口抖动导致的数据缺失。

关键指标

指标名称	本期数值	基期数值	环比增长	口径说明
总 GMV	425,000	378,000	+12.4%	SUM(gmv_amount)
日均 GMV	4,620	4,108	+12.5%	AVG(daily_gmv)
订单数	8,500	7,900	+7.6%	COUNT(order_id)

趋势分布 (Mermaid)

xychart-beta
    title "Monthly GMV Trend"
    x-axis [Oct, Nov, Dec]
    y-axis "GMV" 0 200000
    bar [120000, 165000, 140000]

附录：数据口径

数据源: sales_data.csv (1,250 rows)
处理逻辑: 重复行已按 order_id 去重；缺失日期按 0 填充。
异常定义: |Z-score| > 3 定义为极值，1.5 * IQR 定义为统计异常。

Next steps

Review the detailed anomaly log in data-insight-report.md to confirm the Oct 15th outage.
Run the Top-N category script to see which products drove the November peak.
Export the summary table to your monthly slide deck.

sales_data.csv

CSV · data export

Generated

Example file from a real run - the skill writes it into your workspace.

Connects securely to your tools. The creator never sees your data.

About this skill

Data analysis often fails when figures lack context or logic is hidden. This skill enforces a rigorous five-stage pipeline to transform raw data into structured Markdown reports that separate hard facts from business inferences.

What it does

Data profiling runs automated checks on CSV or SQL inputs to identify schemas, missing values, and data types.
Metric calculation computes sums, means, percentiles, and period-over-period growth using standardized formulas.
Anomaly detection flags outliers using Z-score and IQR methods with documented thresholds.
Multi-channel visualization generates data tables, Mermaid diagrams, and ASCII bar charts for cross-platform compatibility.
Audit-ready reporting outputs a full Markdown file including methodology, assumptions, and raw data references.

How it works

Input validation involves identifying the source (CSV, SQL, or DuckDB) and confirming business definitions for core metrics.
Exploration phase performs technical profiling to handle missing data and duplicates before any calculation starts.
Execution stage runs analytical scripts or DuckDB queries to calculate Top-N, trends, and statistical distributions.
Report generation assembles the findings into a standardized template with a mandatory rigor checklist.

Frameworks & tools

Built for data environments supporting Node.js for CSV profiling and DuckDB for high-performance SQL querying on large datasets.

Why this beats prompting it yourself

Generic prompts often hallucinate numbers or lose track of calculation logic in long conversations. This skill uses a strict "gate-based" workflow where each phase must pass a rigor check before the next begins, ensuring every number in your report is traceable to a specific formula or raw data point.

Use cases

Generating weekly business performance reports from CSV exports.
Auditing transaction data for statistical anomalies and outliers.
Performing cohort analysis or Top-N category breakdowns via SQL.
Creating reproducible data summaries for technical documentation.

Known limitations

Large files over 2000 lines require DuckDB or local scripts to avoid context window limits. DuckDB integration requires a local CLI installation.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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data insight runbook