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Evo Demographic Analysis

Community
Zhang-Henry
evo-demographic-analysis

Create demographic analysis Excel workbooks with pivot tables from PDF population data and Excel income data. Handles PDF table extraction, data joining on SA2_CODE, quartile binning (equal-width ranges on MEDIAN_INCOME), derived columns, and proper openpyxl pivot table creation with cache fields, pivot fields, and reference lists.

Overview

PublisherZhang-Henry
RepositoryCoEvoSkills
Skill nameevo-demographic-analysis
Stars
83
Forks
9
Bundled files
1
LicenseApache-2.0
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Zhang-Henry on GitHub. Read the source before you install it.

Installation

Install the Evo Demographic Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Zhang-Henry/CoEvoSkills/tree/main/artifacts/skills/sales-pivot-analysis/evo-demographic-analysis
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Zhang-Henry/CoEvoSkills.git /tmp/CoEvoSkills
mkdir -p .claude/skills
cp -r /tmp/CoEvoSkills/artifacts/skills/sales-pivot-analysis/evo-demographic-analysis .claude/skills/evo-demographic-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evo Demographic Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Evo Demographic Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Evo Demographic Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Demographic Analysis Pivot Table Builder

Overview

This skill creates an Excel workbook with proper pivot tables from two data sources:

  • A PDF containing population data by SA2 region (SA2_CODE, SA2_NAME, STATE, POPULATION_2023)
  • An Excel file containing income data by SA2 region (SA2_CODE, SA2_NAME, EARNERS, MEDIAN_INCOME, MEAN_INCOME)

Key Concepts

  1. PDF Extraction: Multi-page tables with repeated headers, truncated column names
  2. Data Joining: Inner join on SA2_CODE, filtering 'np' (not publishable) rows
  3. Quartile Binning: Equal-width range binning on MEDIAN_INCOME (Q1-Q4)
  4. Pivot Tables: Proper openpyxl pivot table objects with cache fields, pivot fields, row/col/data field references
  5. Derived Columns: Quarter (from MEDIAN_INCOME ranges) and Total (EARNERS * MEDIAN_INCOME)

Usage

python
import sys
sys.path.insert(0, '/app/environment/skills/evo-demographic-analysis/scripts')
from utils import build_workbook, validate_workbook

# Build the workbook
output = build_workbook(
    pdf_path='/root/population.pdf',
    xlsx_path='/root/income.xlsx',
    output_path='/root/demographic_analysis.xlsx'
)

# Validate
errors = validate_workbook('/root/demographic_analysis.xlsx')
if errors:
    print('FAILED:', errors)
else:
    print('SUCCESS')

Functions in scripts/utils.py

  • extract_pdf_table(pdf_path) - Extract table data from multi-page PDF
  • fix_truncated_headers(headers) - Fix truncated PDF column headers
  • read_income_data(xlsx_path) - Read income data from Excel
  • join_data(pop_headers, pop_rows, inc_headers, inc_rows) - Inner join on SA2_CODE, filter np
  • convert_types(headers, rows) - Convert string values to numeric types
  • compute_quartile_boundaries(rows, median_income_idx) - Equal-width quartile boundaries
  • assign_quartile(value, boundaries) - Assign Q1-Q4 label
  • add_derived_columns(headers, rows, median_income_idx, earners_idx) - Add Quarter and Total
  • write_source_data_sheet(wb, sheet_name, headers, rows) - Write enriched data sheet
  • create_pivot_cache(wb, source_ws_name, headers, num_data_rows) - Create pivot cache
  • create_pivot_table_on_sheet(...) - Create proper pivot table on a sheet
  • build_workbook(pdf_path, xlsx_path, output_path) - End-to-end entry point
  • validate_workbook(output_path) - Validate output meets requirements

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Evo Demographic Analysis AI skill do?

Create demographic analysis Excel workbooks with pivot tables from PDF population data and Excel income data. Handles PDF table extraction, data joining on SA2_CODE, quartile binning (equal-width ranges on MEDIAN_INCOME), derived columns, and proper openpyxl pivot table creation with cache fields, pivot fields, and reference lists.

Why use Evo Demographic Analysis on TypingMind?

Because you install it once and use it with any model. Evo Demographic Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Evo Demographic Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Zhang-Henry/CoEvoSkills/tree/main/artifacts/skills/sales-pivot-analysis/evo-demographic-analysis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Evo Demographic Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Evo Demographic Analysis?

As many as you like. As long as a model supports skills, you can use Evo Demographic Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Evo Demographic Analysis AI skill free?

Yes. It is published on GitHub by Zhang-Henry under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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