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.
npx skills add Zhang-Henry/CoEvoSkills --skill evo-demographic-analysisgit clone --depth 1 https://github.com/Zhang-Henry/CoEvoSkillsWrote 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.
[](https://agentmods.dev/skills/zhang-henry/coevoskills/evo-demographic-analysis)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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.
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
- PDF Extraction: Multi-page tables with repeated headers, truncated column names
- Data Joining: Inner join on SA2_CODE, filtering 'np' (not publishable) rows
- Quartile Binning: Equal-width range binning on MEDIAN_INCOME (Q1-Q4)
- Pivot Tables: Proper openpyxl pivot table objects with cache fields, pivot fields, row/col/data field references
- 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 PDFfix_truncated_headers(headers)- Fix truncated PDF column headersread_income_data(xlsx_path)- Read income data from Exceljoin_data(pop_headers, pop_rows, inc_headers, inc_rows)- Inner join on SA2_CODE, filter npconvert_types(headers, rows)- Convert string values to numeric typescompute_quartile_boundaries(rows, median_income_idx)- Equal-width quartile boundariesassign_quartile(value, boundaries)- Assign Q1-Q4 labeladd_derived_columns(headers, rows, median_income_idx, earners_idx)- Add Quarter and Totalwrite_source_data_sheet(wb, sheet_name, headers, rows)- Write enriched data sheetcreate_pivot_cache(wb, source_ws_name, headers, num_data_rows)- Create pivot cachecreate_pivot_table_on_sheet(...)- Create proper pivot table on a sheetbuild_workbook(pdf_path, xlsx_path, output_path)- End-to-end entry pointvalidate_workbook(output_path)- Validate output meets requirements
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.
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.
- 9d ago First seen · 59 lines · 72 tokens per session scan A 7a8fef250e6f
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.
Other skills, from other repositories
doc-reader
Read any common document/data file — PDF, Word (.docx), Excel (.xlsx/.xls), PowerPoint (.pptx), images (OCR), CSV/TSV, plain text, JSON/YAML/TOML, HTML/XML, and most source-code files. Use the readdocument tool.
skill-doc-delivery
Convert markdown to DOCX, PPTX, XLSX, PDF office documents — use when you need exportable deliverables.
unified-deliverable-workflow
Generate spreadsheets, diagrams, and PDF reports with iteration budgeting and error recovery.
document-python-direct-exec
Use direct Python execution for reliable spreadsheet and document/PDF generation operations.
document-direct-python
Use direct Python execution for reliable document creation including spreadsheets, PDFs, and structured reports.
document-python-direct
Use direct Python execution for reliable spreadsheet and document/PDF generation operations.