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 Abhinavbwj/Claude-skills-for-Computational-Designers --skill ml-for-aecgit clone --depth 1 https://github.com/Abhinavbwj/Claude-skills-for-Computational-DesignersWrote 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/abhinavbwj/claude-skills-for-computational-designers/ml-for-aec)<a href="https://agentmods.dev/skills/abhinavbwj/claude-skills-for-computational-designers/ml-for-aec"><img src="https://agentmods.dev/badge/skills/abhinavbwj/claude-skills-for-computational-designers/ml-for-aec/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/abhinavbwj/claude-skills-for-computational-designers/ml-for-aec"><img src="https://agentmods.dev/badge/skills/abhinavbwj/claude-skills-for-computational-designers/ml-for-aec.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00039 | $0.10830 |
| Opus 5 | $0.00019 | $0.05415 |
| Sonnet 5 | $0.00008 | $0.02166 |
| Haiku 4.5 | $0.00004 | $0.01083 |
Grade A, and why
ml-for-aec 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 12d 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:
- ml-for-aec — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 987 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Machine Learning for AEC
Machine learning is reshaping specific domains within Architecture, Engineering, and Construction, though the transformation is uneven. This skill provides a thorough, practitioner-oriented guide to where ML delivers real value in AEC today, the architectures and methods that work, the data challenges that constrain adoption, and practical pipelines for training, deploying, and maintaining ML models in production AEC workflows.
1. ML in AEC: Current State
1.1 Where ML Actually Works in AEC Today
ML in AEC is most effective where three conditions converge: (a) sufficient training data exists or can be generated, (b) the task is well-defined with measurable performance metrics, and (c) the cost of errors is manageable or human review is in the loop.
Proven, deployed applications:
- Construction progress monitoring (photo comparison to BIM schedule)
- Safety monitoring on construction sites (PPE detection, exclusion zones)
- Defect detection (crack detection in concrete, facade inspections via drone imagery)
- Document classification (sorting drawings by discipline, type)
- Energy performance prediction (surrogate models replacing full simulation)
- Point cloud semantic segmentation (labeling structural elements from LiDAR scans)
- Cost estimation from early-stage design parameters
Promising but not yet mature:
- Floor plan generation from adjacency programs
- Automated scan-to-BIM conversion
- Generative massing from site constraints
- Structural topology optimization acceleration
- Natural language to BIM queries
Overhyped or premature:
- Fully autonomous building design from text prompts
- AI replacing architectural design judgment
- General-purpose design AI that understands building codes, physics, and aesthetics simultaneously
- End-to-end text-to-construction-documents
1.2 Data Challenges in AEC
The AEC industry faces unique data challenges that limit ML adoption:
Small datasets: Unlike ImageNet (14M images) or web-scale text corpora, AEC datasets are small. A large architecture firm might have 5,000 floor plans in its portfolio. A structural engineering firm might have 2,000 analyzed buildings. These numbers are 3-4 orders of magnitude below what deep learning models typically require.
What ships with it
3 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.
- 12d ago First seen · 987 lines · 39 tokens per session scan A a482c233945f
ml-for-aec is a skill published in the GitHub repository Abhinavbwj/Claude-skills-for-Computational-Designers (212 stars, last pushed 5mo ago), licensed MIT. It adds 39 tokens to every session and 10,830 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 2026-08-30.
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