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 data-driven-designgit 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/data-driven-design)<a href="https://agentmods.dev/skills/abhinavbwj/claude-skills-for-computational-designers/data-driven-design"><img src="https://agentmods.dev/badge/skills/abhinavbwj/claude-skills-for-computational-designers/data-driven-design/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/data-driven-design"><img src="https://agentmods.dev/badge/skills/abhinavbwj/claude-skills-for-computational-designers/data-driven-design.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 | $0.00032 | $0.08753 |
| Opus 5 | $0.00016 | $0.04376 |
| Sonnet 5 | $0.00006 | $0.01751 |
| Haiku 4.5 | $0.00003 | $0.00875 |
Grade A, and why
data-driven-design 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 11d 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:
- data-driven-design — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 643 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data-Driven Design
This skill provides comprehensive guidance on integrating quantitative data into every stage of the architectural and urban design process. It covers geospatial data, environmental sensing, occupancy analytics, spatial network analysis, climate processing, urban datasets, and the APIs that serve them. The goal is to replace intuition-only design with evidence-based reasoning while preserving creative agency.
1. Data-Driven Design Philosophy
1.1 Evidence-Based vs. Intuition-Based Design
Traditional design relies heavily on precedent, aesthetic judgment, and professional intuition. These are valuable but unverifiable. Evidence-based design augments intuition with measurable inputs:
| Dimension | Intuition-Based | Evidence-Based |
|---|---|---|
| Site analysis | Walkthrough, photos | GIS layers, sensor grids, satellite imagery |
| Program sizing | Rules of thumb | Occupancy analytics, utilization studies |
| Circulation | Designer judgment | Space syntax integration/choice values |
| Orientation | Sun path intuition | EPW-parsed radiation/temperature analysis |
| Massing | Formal exploration | Daylight/energy simulation feedback loops |
| Post-occupancy | Anecdotal feedback | Sensor-driven POE dashboards |
Evidence-based design does not eliminate intuition. It provides a quantitative substrate on which creative decisions rest, making design rationale transparent, defensible, and reproducible.
1.2 Data as Design Input, Not Just Validation
The critical shift: data must enter the design process at the very beginning, not after decisions are made. In the traditional workflow, simulation runs after design is locked, serving only to confirm or reject. In a data-driven workflow:
- Pre-design data collection -- site climate, demographics, transport, land use, environmental constraints
- Data-informed brief -- program areas derived from utilization studies, not guesses
- Generative exploration -- design options generated with data constraints embedded
- Continuous feedback -- every design iteration evaluated against data-derived KPIs
- Post-occupancy loop -- sensor data feeds back into future project templates
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.
- 11d ago First seen · 643 lines · 32 tokens per session scan A 3505192cd70a
data-driven-design is a skill published in the GitHub repository Abhinavbwj/Claude-skills-for-Computational-Designers (212 stars, last pushed 5mo ago), licensed MIT. It adds 32 tokens to every session and 8,753 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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