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 marcinfinitesimal533/Claude-skills-for-Computational-Designers --skill generative-designgit clone --depth 1 https://github.com/marcinfinitesimal533/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/marcinfinitesimal533/claude-skills-for-computational-designers/generative-design)<a href="https://agentmods.dev/skills/marcinfinitesimal533/claude-skills-for-computational-designers/generative-design"><img src="https://agentmods.dev/badge/skills/marcinfinitesimal533/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/marcinfinitesimal533/claude-skills-for-computational-designers/generative-design"><img src="https://agentmods.dev/badge/skills/marcinfinitesimal533/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.
This is a copy
100% identical to generative-design — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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 marcinfinitesimal533/Claude-skills-for-Computational-Designers (3 stars, last pushed 14d 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. It is 100% identical to generative-design, differing in 0 lines, and is treated as a copy.
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.
design-is
Audit a design against Dieter Rams' ten "Good design is..." principles, then hand off a /make-plan prompt for one of three outcomes — new design, refine design, or redesign. Use when the user says "audit this design", "design review", "check this UI against Rams", "is this UI good", "critique this design", "design…
method-figure
Generate a publication-grade method / architecture / pipeline / workflow figure (a paper or README 'Figure 1') as an AUDITABLE object, not a one-shot prompt. A deterministic JSON blueprint LOCKS the content; an image model (gpt-image-2, baked by the agent via mcpcodexcodex — Codex GPT-5.5 xhigh, sandbox…
academic-poster
A guide for turning a research paper or abstract into a printable academic conference poster. It covers large single-page layouts, including traditional columns and a design that enlarges the main finding.
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.
landing-not-slop
Marketing pages that do not read as machine-generated. Use when writing or reviewing a landing, hero, pricing table, feature grid or marketing copy.