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 generative-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/generative-design)<a href="https://agentmods.dev/skills/abhinavbwj/claude-skills-for-computational-designers/generative-design"><img src="https://agentmods.dev/badge/skills/abhinavbwj/claude-skills-for-computational-designers/generative-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/generative-design"><img src="https://agentmods.dev/badge/skills/abhinavbwj/claude-skills-for-computational-designers/generative-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 | $0.00030 | $0.09244 |
| Opus 5.5 | $0.00012 | $0.03698 |
| Sonnet 5.5 | $0.00006 | $0.01849 |
| Haiku 4.5 | $0.00003 | $0.00924 |
Grade A, and why
generative-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 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.
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:
- generative-design — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 646 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generative Design for AEC Computational Design
1. Generative Design Paradigm
1.1 Definition and Scope
Generative design is a computational design methodology in which a designer defines a problem through goals, constraints, and variable parameters, and an algorithmic system autonomously generates, evaluates, and evolves candidate solutions across a defined design space. Unlike traditional design where the human produces every solution manually, generative design shifts the designer's role from direct form-maker to curator of outcomes — defining what is desired rather than how to achieve it.
In the AEC context, generative design applies to problems ranging from single-building floor plan layouts and structural topologies to neighborhood-scale massing studies and infrastructure routing. The common thread is a design space too large for exhaustive manual exploration.
1.2 Distinction from Parametric Design
The confusion between parametric and generative design is pervasive. The distinction is fundamental:
| Aspect | Parametric Design | Generative Design |
|---|---|---|
| Core action | Define relationships between parameters | Explore the solution space algorithmically |
| Designer's role | Adjust sliders, observe outcomes | Define objectives and constraints, curate results |
| Output | One solution per parameter state | Population of diverse candidate solutions |
| Search method | Manual, intuition-driven | Automated, algorithm-driven |
| Model requirement | Parametric model with exposed variables | Parametric model + fitness function + solver |
| Typical scale | Dozens to hundreds of manual explorations | Thousands to millions of evaluated candidates |
A parametric model is a prerequisite for generative design — it provides the mechanism by which the solver manipulates geometry. But parametric design alone does not search; it merely responds to human input. Generative design automates the search.
1.3 The Generate-Evaluate-Evolve Loop
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.
- 1mo ago First seen · 646 lines · 30 tokens per session scan A 5d5b4b19b79f
generative-design is a skill published in the GitHub repository Abhinavbwj/Claude-skills-for-Computational-Designers (224 stars, last pushed 7mo ago), licensed MIT. It adds 30 tokens to every session and 9,244 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
generative-design
Evolutionary algorithms, multi-objective optimization, design space exploration, fitness function design, population-based methods, and generative workflows for AEC computational design.
optimization-methods
Genetic algorithms, simulated annealing, particle swarm optimization, gradient-based methods, topology optimization, shape optimization, size optimization, and benchmark problems for AEC computational design.
data-scientist
!cat skills/shared/protocols/ux-protocol.md 2>/dev/null || true !cat skills/shared/protocols/input-validation.md 2>/dev/null || true !cat skills/shared/protocols/tool-efficiency.md 2>/dev/null || true !cat .production-grade.yaml 2>/dev/null || echo "No config — using defaults".
3d-spatial-engineer
!cat skills/shared/protocols/3d-spatial-foundations.md 2>/dev/null || echo "=== 3D Foundations not loaded ===" !cat skills/shared/protocols/ux-protocol.md 2>/dev/null || true !cat skills/shared/protocols/input-validation.md 2>/dev/null || true !cat skills/shared/protocols/tool-efficiency.md 2>/dev/null || true !cat…
database-schema-designer
Design production-ready database schemas for SQL and NoSQL databases. Covers normalization, indexing strategy, migration management with rollback safety, query optimization, and multi-tenant patterns. Supports PostgreSQL, MySQL, SQLite, MongoDB, and Vitess.
Asset Manager
Organize design assets, optimize images and fonts, maintain brand asset libraries, implement version control for assets, and enforce naming conventions. Keep design assets organized and production-ready.