product-benchmark-report

Generate a single-product performance benchmark report with Markdown, Excel, and Python script.

Updated Jan 25, 2026

Search Tags:#markdown #python #excel #benchmark #financial-analysis #report-generation #nav-data

Ready to run

npx skills add https://github.com/xfs96192/claude-config --skill product-benchmark-report

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: product-benchmark-report
Source: https://github.com/xfs96192/claude-config/tree/main/skills/product-benchmark-report
Command: npx skills add https://github.com/xfs96192/claude-config --skill product-benchmark-report

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, EmQuantAPI, and includes scripts (resource) components.

What problem does it solve?

This skill automates the creation of a single-product performance benchmark report, delivering a Markdown narrative, a value-graph image, a data Excel file, and a calculation script to streamline monthly/quarterly performance reviews.

Core Features & Use Cases

Single-product report generation: Produces a complete report for a specified product code, including performance visuals and benchmarks.
Data orchestration: Pulls NAV data from local caches or weekly sheets, and assembles benchmark NAV using index data and a deposit rate.
Outputs: Creates a Markdown main report, a PNG value-trajectory image, an Excel data file, and a Python script clone for reproducibility.

Quick Start

Generate a report for a given product code (e.g., 9K717120) using the latest NAV data available.

Frequently Asked Questions about product-benchmark-report

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate single-product performance benchmarking with NAV data?▼

Single-product performance benchmarking automates report generation by pulling NAV data from local caches or weekly sheets, computing annualized returns and drawdowns against index benchmarks, and outputting a Markdown narrative, PNG graph, Excel data file, and Python script.

How does a benchmark report calculate drawdowns and annualized returns?▼

Benchmark report calculation assembles benchmark NAV using index data and a deposit rate, then computes annualized returns and drawdowns for the specified financial product. It uses pandas and numpy to process the NAV data and generate the performance metrics.

Do I need EmQuantAPI to generate a financial product benchmark report?▼

Yes, the EmQuantAPI is required to fetch index data for assembling the benchmark NAV. The report generation also requires Python with pandas, numpy, and matplotlib installed to compute metrics and render the value-trajectory graph.

Can I use Excel data to configure the index benchmark for financial analysis?▼

Yes, the financial analysis tool reads the benchmark configuration directly from an Excel file. It combines this configuration with NAV data from local caches or weekly sheets to produce the final performance benchmark outputs.

What outputs are generated when automating a product performance report?▼

Automating a product performance report generates a Markdown main report, a PNG value-trajectory image, an Excel data file containing the financial metrics, and a Python calculation script clone for full reproducibility of the analysis.

Best way to reproduce a financial benchmark report calculation script?▼

The best way to reproduce the financial benchmark report is through the generated Python calculation script clone. This script, alongside the Excel data file and benchmark configuration, allows you to rerun the NAV data processing and metric computation entirely.