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Grow a changing network of ink-like trails as small agents sense, follow and reinforce nearby paths. Each agent compares forward, left and right trail samples before turning. Agents deposit a field that diffuses and evaporates, while supplied food points add attraction. This feedback creates the visible network; it is an artificial trail system rather than a biological prediction. Reuse supplied assets or quote new media through PopNow.

Uses PopNow for
Music & voice
Needs
Python 3 · FFmpeg

Generation is optional when you have suitable input material.

v1.0.0 · Updated Oct 25, 2026 · 975 KB · Use it for personal and client work; please don't resell or share the package.

What you get

  1. An editable 8-second 720p sample and local MP4 renderer, silent.

  2. Sensor steering, trail feedback and food-point placement are already implemented. Changing sensing distance or attraction locations changes the network's development, without drawing its routes by hand.

  3. A six-frame contact sheet, render receipt and input self-check.

Good for

  • Organic network motion art
  • Exploring artificial growth through editable sensing rules

Not for

  • The system is an artistic agent simulation. It does not predict biological behavior, solve optimal routes or guarantee one connected network.

How it works

Includes a local renderer

  1. Choose the material

    No image is required. Provide two to eight normalized food-point coordinate pairs, or use the supplied arrangement.

  2. Shape the motion

    Edit the documented project controls, then validate the exact settings. Two to eight food_points place the attraction sites within normalized coordinates from 0.1 to 0.9.

  3. Render and review

    Export locally to MP4, inspect the contact sheet and watch the whole film. Watch new trails emerge and compete, and verify that changing food points changes the visible network rather than only its markers.

How the workflow runs

How Physarum Trail Garden works

Each agent compares forward, left and right trail samples before turning. Agents deposit a field that diffuses and evaporates, while supplied food points add attraction. This feedback creates the visible network; it is an artificial trail system rather than a biological prediction.

What's inside the skill

What you can change

  • Two to eight food_points place the attraction sites within normalized coordinates from 0.1 to 0.9.
  • Sensor angle from 0.2 to 1.4 and sensor distance from 4 to 22 change steering.
  • Evaporation from 0.01 to 0.12 controls how long trails persist.
  • The seed changes initial positions and headings.

What to review before delivery

  • The local self-check compares three frame samples after a meaningful input change and rejects its documented invalid case.
  • Output protection rejects overlap with input files and preserves existing sidecars unless overwrite is explicit.
  • Watch new trails emerge and compete, and verify that changing food points changes the visible network rather than only its markers. This final visual review is manual.

See the package files

These are selected working files from this edition, with their actual role. The complete download also contains instructions and applicable license notices.

  • The task-specific motion mechanism and supported inputs.

    scripts/scene.py

  • Protected MP4 export, contact sheet and hash receipt.

    scripts/runtime.py

  • Editable settings used by the supplied sample.

    examples/project.json

  • Actual changed-frame, invalid-input and output-protection checks.

    scripts/selftest.py

  • Input ranges, material placement and honest limits.

    references/inputs.md

Why use this skill

Start with this package

Sensor steering, trail feedback and food-point placement are already implemented. Changing sensing distance or attraction locations changes the network's development, without drawing its routes by hand.

What PopNow adds

Use PopNow for the missing Music & voice in this workflow. Keep adequate material you already own, then bring new assets back into the editing and assembly steps described here.

Compare the options

A prompt, your own skill, or this workflow?

Start with a prompt

Your agent can create from a plain request. The brief, visual rules, asset choices, output format and review process still need to be established for that project.

Write your own skill

You choose and maintain the reusable instructions, defaults, file structure and checks. This is useful when your process needs different rules or integrations.

A workflow created for PopNow

This workflow was built for PopNow. Its source files, included dependencies and applicable notices are part of the package; the scope described below belongs to this edition.

Guide reviewed Oct 25, 2026

What will this cost?

Agents, trails and attraction sites are calculated locally. Optional new music or sound effects can be quoted through PopNow; the default network requires no generated image.

Generation is optional when you have suitable input material. Free skill. New media is quoted before it runs.

The skill package

Free to install under your account's download allowance. The package license covers your own and client work; redistribution is excluded.

Existing assets and local work

Reusing suitable assets, editing files and running local assembly do not call a PopNow generation model. Your computer and any required runtime still do the work.

New AI images, clips or sound

Quoted for the actual model, inputs, count, duration and resolution. Extra alternatives, revisions or optional enhancement passes can add generation requests.

Your agent and hosting

Any subscription, hosted computer, deployment or other provider fee belongs to that service. Installing this skill does not include those services.

Check the cost of your version first

Your assistant can inspect the available models and quote the exact request first. You choose which optional passes to include and approve spending before generation.

Ask your assistant

  • Use the PopNow skill "Physarum Trail Garden" for my project. List the inputs you can reuse, quote only the missing media, show the quoted total and flag any unpriced steps. Separate optional enhancements, and wait for my approval before starting anything paid.

How PopNow billing works

Connections using a PopNow API key, including API-key MCP connections, spend from the separate API balance in USD. A ChatGPT or Claude connection authorized through OAuth spends website credits. These balances are separate; the quote shown by your connection is the approval basis.

Download allowances and current account conditions apply. A free package does not mean unlimited free generation.

See model ratesAPI USD balanceWebsite credit packs

How to use it

You say

  • Use the PopNow skill "Physarum Trail Garden" (physarum-trail-garden) for this: Render the supplied 8-second example. Make a second version with sensor_distance set to 16. Show the changed motion and quote any missing media before generation.

Then try

  • Show how the changed setting affects the actual motion.

  • Keep my adequate source material and prepare an optional soundtrack quote.

What to have ready

  • No image is required. Provide two to eight normalized food-point coordinate pairs, or use the supplied arrangement.
  • Python 3, Pillow, NumPy, FFmpeg and ffprobe
  • The project controls listed in the input contract

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Questions

Does it calculate an optimal route between points?

No. Agents follow and reinforce a changing trail field. The result can branch, loop or disconnect and is not an optimization guarantee.

What do the food points do?

They add attraction to the field sampled by agents, influencing where paths develop. They are not simply labels painted over a fixed pattern.

Is it a model of a real organism?

It borrows trail-sensing ideas for generative art. It is not calibrated to predict a particular organism's behavior.

What is already packaged in Physarum Trail Garden?

The task-specific motion mechanism and supported inputs. Protected MP4 export, contact sheet and hash receipt. Editable settings used by the supplied sample. Actual changed-frame, invalid-input and output-protection checks. Input ranges, material placement and honest limits.

Which parts of Physarum Trail Garden can add a charge?

Agents, trails and attraction sites are calculated locally. Optional new music or sound effects can be quoted through PopNow; the default network requires no generated image. Connections using a PopNow API key, including API-key MCP connections, spend from the separate API balance in USD. A ChatGPT or Claude connection authorized through OAuth spends website credits. These balances are separate; the quote shown by your connection is the approval basis.

How does Physarum Trail Garden differ from writing a prompt or my own skill?

Sensor steering, trail feedback and food-point placement are already implemented. Changing sensing distance or attraction locations changes the network's development, without drawing its routes by hand.