evo-demographic-analysis

evo-demographic-analysis is a skill for Claude Code, Codex from Zhang-Henry/CoEvoSkills. It costs 72 tokens per session (670 once invoked), scanned A, original, Apache-2.0.

An Excel workbook builder that combines population data from PDF files with income data from spreadsheets. It joins regions, groups incomes into four ranges, and creates pivot tables for analysis.

In plain words
What is it for?
Use it for demographic and income analysis by local region, including comparisons of earners, income levels, and population.
Why use it?
It removes the manual work of extracting tables from PDFs, matching regional records, calculating derived values, and setting up pivot tables.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it for demographic and income analysis by local region, including comparisons of earners, income levels, and population.

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Install with agentmods
npx agentmods add skills/zhang-henry/coevoskills/evo-demographic-analysis
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Zhang-Henry/CoEvoSkills --skill evo-demographic-analysis
Clone the repo
git clone --depth 1 https://github.com/Zhang-Henry/CoEvoSkills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for evo-demographic-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/zhang-henry/coevoskills/evo-demographic-analysis/github.svg)](https://agentmods.dev/skills/zhang-henry/coevoskills/evo-demographic-analysis)
Your own site
<a href="https://agentmods.dev/skills/zhang-henry/coevoskills/evo-demographic-analysis"><img src="https://agentmods.dev/badge/skills/zhang-henry/coevoskills/evo-demographic-analysis/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for evo-demographic-analysis

Your own site · 80×15
<a href="https://agentmods.dev/skills/zhang-henry/coevoskills/evo-demographic-analysis"><img src="https://agentmods.dev/badge/skills/zhang-henry/coevoskills/evo-demographic-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 670 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00072 $0.00670
Opus 5 $0.00036 $0.00335
Sonnet 5 $0.00014 $0.00134
Haiku 4.5 $0.00007 $0.00067

Measured 9d ago against content hash 7a8fef250e6f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

evo-demographic-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/utils.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

artifacts/skills/sales-pivot-analysis/evo-demographic-analysis/SKILL.md · 59 lines

How it starts

The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

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

Read the full file on GitHub · 59 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 59 lines · 72 tokens per session scan A 7a8fef250e6f

Subscribe to this mod's changes

evo-demographic-analysis is a skill published in the GitHub repository Zhang-Henry/CoEvoSkills (66 stars, last pushed 22d ago), licensed Apache-2.0. It adds 72 tokens to every session and 670 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.