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 datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill xlsx-constructiongit clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_ConstructionWrote 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/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/xlsx-construction)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/xlsx-construction"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/xlsx-construction/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/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/xlsx-construction"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/xlsx-construction.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 | $0.00030 | $0.02108 |
| Opus 5 | $0.00015 | $0.01054 |
| Sonnet 5 | $0.00006 | $0.00422 |
| Haiku 4.5 | $0.00003 | $0.00211 |
Grade A, and why
xlsx-construction scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- xlsx-construction — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 266 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Excel Processing for Construction
Overview
Adapted from Anthropic's XLSX skill for construction spreadsheet workflows.
Construction Use Cases
1. Cost Estimate Template
from openpyxl import Workbook
from openpyxl.styles import Font, Alignment, Border, Side, PatternFill
from openpyxl.utils import get_column_letter
def create_estimate_template(output_path: str, project_name: str):
"""Create construction cost estimate template."""
wb = Workbook()
ws = wb.active
ws.title = "Cost Estimate"
# Styles
header_font = Font(bold=True, size=12)
currency_format = '$#,##0.00'
thin_border = Border(
left=Side(style='thin'),
right=Side(style='thin'),
top=Side(style='thin'),
bottom=Side(style='thin')
)
header_fill = PatternFill(start_color="4472C4", end_color="4472C4", fill_type="solid")
# Project header
ws['A1'] = "CONSTRUCTION COST ESTIMATE"
ws['A1'].font = Font(bold=True, size=16)
ws['A2'] = f"Project: {project_name}"
ws['A3'] = "Date:"
ws['B3'] = "=TODAY()"
# Column headers (row 5)
headers = ['CSI Code', 'Description', 'Quantity', 'Unit', 'Unit Cost',
'Labor', 'Material', 'Equipment', 'Total']
for col, header in enumerate(headers, 1):
cell = ws.cell(row=5, column=col, value=header)
cell.font = Font(bold=True, color="FFFFFF")
cell.fill = header_fill
cell.border = thin_border
cell.alignment = Alignment(horizontal='center')
# Set column widths
widths = [12, 40, 10, 8, 12, 12, 12, 12, 14]
for i, width in enumerate(widths, 1):
ws.column_dimensions[get_column_letter(i)].width = width
# Sample data rows with formulas
for row in range(6, 26): # 20 empty rows
# Total formula: Labor + Material + Equipment
ws.cell(row=row, column=9,
value=f"=SUM(F{row}:H{row})")
# Apply borders
for col in range(1, 10):
ws.cell(row=row, column=col).border = thin_border
# Currency formatting
for col in [5, 6, 7, 8, 9]: # Cost columns
for row in range(6, 26):
ws.cell(row=row, column=col).number_format = currency_format
# Subtotals section
ws['G27'] = "SUBTOTAL"
ws['I27'] = "=SUM(I6:I25)"
ws['I27'].number_format = currency_format
ws['G28'] = "Contingency (10%)"
ws['I28'] = "=I27*0.10"
ws['G29'] = "TOTAL"
ws['I29'] = "=I27+I28"
ws['I29'].font = Font(bold=True, size=14)
wb.save(output_path)
return output_path
What ships with it
2 files 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 · 266 lines · 30 tokens per session scan A b951bf13e2ba
xlsx-construction is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (310 stars, last pushed 21d ago), licensed MIT. It adds 30 tokens to every session and 2,108 once invoked, about $0.0002 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.
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