logic-lab AGENTS.md

Project instructions for Logic Lab, a collection of Python and py5 creative-coding experiments. They set rules for file organization, implementation, testing, and Git commits.

In plain words
What is it for?
Organizing experiments such as physics, fractals, cellular automata, genetic algorithms, and steering behaviors, while running checks and leaving changes ready for review.
Why use it?
They keep related algorithms in predictable folders and prevent the agent from committing changes on the user’s behalf.

Instructions file for CodexOpenCode

Install

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.

agentmods
npx agentmods add instructions/asamiile/logic-lab/agents-md
Clone the repo
git clone --depth 1 https://github.com/asamiile/logic-lab

Made for: Codex, OpenCode.

Per session 724 This file is loaded in full into every session.
When invoked 724 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00724 $0.00724
Opus 5 $0.00362 $0.00362
Sonnet 5 $0.00145 $0.00145
Haiku 4.5 $0.00072 $0.00072

Measured yesterday against content hash 12e349e63dba, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

logic-lab AGENTS.md 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 yesterday.

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.

AGENTS.md · 82 lines

How it starts

The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent Instructions for Logic Lab

Commit Policy

Do not commit changes to git. The user handles all commits manually.

Agents may edit files, create directories, and run checks, but must not run git add, git commit, or other git write operations.

Implementation Preferences

  • Implement straightforward tasks directly.
  • Keep changes scoped to the requested domain or tool.
  • Run syntax checks for changed Python files.
  • Run focused tests or smoke checks when practical.
  • Leave the working tree ready for user review.

Project Structure

Logic Lab contains Python and py5 translations and experiments for creative coding algorithms.

Use domain-based organization:

  • physics/ for motion, forces, particles, waves, oscillation, and physical systems.
  • steering_behaviors/ for autonomous agents, path following, flow fields, sensors, and swarms.
  • genetic_algorithms/ for DNA, mutation, selection, and evolutionary search.
  • neuro_evolution/ for neural networks evolved by genetic algorithms.
  • fractals/ for recursion, L-systems, trees, Koch curves, and spatial subdivision.
  • cellular_automata/ for CA grids, lattice rules, Pascal patterns, and emergence.
  • mathematical/ for mathematical geometry and numerical generative systems.
  • tiling_patterns/ for symmetry, tiling, textile, and deformation systems.
  • research/ for experimental or hybrid systems that do not fit cleanly elsewhere.
  • shared/ for reusable libraries used by multiple sketches.

Each simulation should usually follow:

domain/simulation_name/
├── simulation_name.py
├── README.md
└── screenshots/

MCP And Manifest

Logic Lab exposes selected algorithm knowledge through a local read-only MCP server in mcp/logic_lab_server.py.

The search index lives at .agents/art_manifest.json. When adding a new algorithm or simulation, agents should:

  1. Add or update the simulation code and README.md.

  2. Run:

    uv run python .agents/update_art_manifest.py --write
    

Read the full file on GitHub · 82 lines

Changes

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.

  1. yesterday First seen · 82 lines · 724 tokens per session scan A 12e349e63dba

Subscribe to this mod's changes

logic-lab AGENTS.md is an instructions file published in the GitHub repository asamiile/logic-lab (0 stars, last pushed 5d ago), licensed MIT. It adds 724 tokens to every session, about $0.0036 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 2026-08-31.

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