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 algorithmic-patternsgit 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/algorithmic-patterns)<a href="https://agentmods.dev/skills/marcinfinitesimal533/claude-skills-for-computational-designers/algorithmic-patterns"><img src="https://agentmods.dev/badge/skills/marcinfinitesimal533/claude-skills-for-computational-designers/algorithmic-patterns/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/algorithmic-patterns"><img src="https://agentmods.dev/badge/skills/marcinfinitesimal533/claude-skills-for-computational-designers/algorithmic-patterns.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 | $0.00034 | $0.06806 |
| Opus 5 | $0.00017 | $0.03403 |
| Sonnet 5 | $0.00007 | $0.01361 |
| Haiku 4.5 | $0.00003 | $0.00681 |
Grade A, and why
algorithmic-patterns 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.
This is a copy
100% identical to algorithmic-patterns — 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 — 520 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Algorithmic Patterns for AEC Design
1. Nature-Inspired Computation in AEC
Why Biological Algorithms Matter for Design
For three and a half billion years, evolution has solved the optimization problems architects and engineers face daily: distributing material efficiently, creating structures that resist loads with minimal mass, organizing circulation for millions of agents, regulating temperature without mechanical systems, and generating complex forms from simple rules. Nature-inspired computation translates these solutions into programmable algorithms that transform AEC practice.
The fundamental insight is that complexity does not require complex instructions. A fern frond with thousands of precisely placed leaflets emerges from a recursive rule fitting in a single line of code. A termite mound maintaining two-degree temperature stability is built by agents following three local rules. An oak tree optimally distributing material to resist wind has no central controller -- it grows according to Wolff's law, depositing material where stress is highest.
Emergence and Self-Organization
Emergence produces macro-scale patterns from micro-scale interactions without centralized control. In AEC, this challenges conventional top-down design, replacing it with local rules and boundary conditions that self-organize into coherent spatial configurations.
Key properties of emergent systems:
- Nonlinearity -- small changes in rules produce disproportionate changes in output
- Feedback loops -- positive feedback amplifies patterns, negative feedback stabilizes them
- Decentralization -- no single agent has global knowledge of the system
- Adaptation -- the system responds to environmental changes in real time
- Robustness -- local failures do not cascade to system-level collapse
The computational thesis underlying all algorithmic patterns is that irreducible complexity can emerge from reducible rules. Stephen Wolfram demonstrated this with elementary cellular automata: Rule 110, defined by 8 binary transitions, is Turing-complete. A one-dimensional grid of cells with two states and nearest-neighbor rules can compute anything computable. For AEC: a branching structure with thousands of unique members can be specified by 3-4 L-system rules; a facade with apparent randomness generated by a 2-state CA; an optimal circulation network by 10,000 agents following 3 flocking rules.
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.
- 11d ago First seen · 520 lines · 34 tokens per session scan A e84ec26d9dc4
algorithmic-patterns is a skill published in the GitHub repository marcinfinitesimal533/Claude-skills-for-Computational-Designers (2 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 6,806 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to algorithmic-patterns, differing in 0 lines, and is treated as a copy.
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