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 ifc-qto-extractiongit 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/ifc-qto-extraction)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/ifc-qto-extraction"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/ifc-qto-extraction/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/ifc-qto-extraction"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/ifc-qto-extraction.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 | $0.00038 | $0.04570 |
| Opus 5.5 | $0.00015 | $0.01828 |
| Sonnet 5.5 | $0.00008 | $0.00914 |
| Haiku 4.5 | $0.00004 | $0.00457 |
Grade A, and why
ifc-qto-extraction scanned grade A with 1 finding 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 1mo 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
result = subprocess.run(cmd, capture_output=True, text=True) Copies of this mod
1 near-identical copy found in the catalogue:
- ifc-qto-extraction — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 598 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IFC Quantity Takeoff Extraction
Extract structured quantity data from BIM models (IFC, Revit) for cost estimation, material ordering, and progress tracking.
Business Case
Problem: Manual quantity takeoff is:
- Time-consuming (40-80 hours for medium project)
- Error-prone (human counting mistakes)
- Not repeatable (changes require full rework)
- Disconnected from design (no live updates)
Solution: Automated QTO from BIM that:
- Extracts all quantities in minutes
- Groups by type, level, zone
- Updates instantly with model changes
- Exports to Excel for pricing
ROI: 90% reduction in QTO time, near-zero counting errors
DDC Tools Used
┌──────────────────────────────────────────────────────────────────────┐
│ QTO EXTRACTION PIPELINE │
├──────────────────────────────────────────────────────────────────────┤
│ │
│ INPUT CONVERT ANALYZE │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│ │ .rvt │ │ DDC │ │ Python │ │
│ │ .ifc │─────────►│Converter│───────────►│ pandas │ │
│ │ .dwg │ │ │ │ │ │
│ └─────────┘ └─────────┘ └─────────┘ │
│ │ │ │
│ ▼ ▼ │
│ ┌─────────┐ ┌─────────┐ │
│ │ .xlsx │ │ Grouped │ │
│ │ raw data│ │ QTO │ │
│ └─────────┘ └─────────┘ │
│ │ │
│ OUTPUT ▼ │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ QTO Report │ │
│ │ • Element counts by type │ │
│ │ • Areas (m², ft²) │ │
│ │ • Volumes (m³, ft³) │ │
│ │ • Lengths (m, ft) │ │
│ │ • Weights (kg, tons) │ │
│ │ • Grouped by level/zone/system │ │
│ └─────────────────────────────────────────────────────────────┘ │
│ │
└──────────────────────────────────────────────────────────────────────┘
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.
- 1mo ago First seen · 598 lines · 38 tokens per session scan A 519add680caf
ifc-qto-extraction is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (373 stars, last pushed 8d ago), licensed MIT. It adds 38 tokens to every session and 4,570 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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