Breadboarding

Community

koolamusic

breadboarding

Transform a workflow description into affordance tables showing UI and Code affordances with their wiring. Use to map existing systems or design new ones from shaped parts.

Overview

Publisher	koolamusic
Repository	`claudefiles`
Skill name	`breadboarding`
Stars	121
Forks	14
Bundled files	9
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
9 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by koolamusic on GitHub. Read the source before you install it.

Installation

Install the Breadboarding AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/koolamusic/claudefiles/tree/main/skills/breadboarding
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/koolamusic/claudefiles.git /tmp/claudefiles
mkdir -p .claude/skills
cp -r /tmp/claudefiles/skills/breadboarding .claude/skills/breadboarding

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Breadboarding in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Breadboarding on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Breadboarding is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Breadboarding

Breadboarding transforms a workflow description into a complete map of affordances and their relationships. The output is always a set of tables showing numbered UI and Code affordances with their Wires Out and Returns To relationships. The tables are the truth. Mermaid diagrams are optional visualizations for humans.

Use Cases

1. Mapping an Existing System

You have a workflow you're trying to understand. Provide code repo(s) and a workflow description (always from the perspective of an operator trying to make an effect happen). Output: affordance tables + optional Mermaid.

2. Designing from Shaped Parts

You have a new system sketched as an assembly of parts (mechanisms), which is what a shaping pass produces — /grill shape is one way to get there. Provide the parts list, the R (requirement/outcome), and optionally the existing system. Output: affordance tables + optional Mermaid.

3. Mixtures

An existing system that must remain as-is, plus new pieces or changes. Breadboard both together — existing affordances and new ones — showing how they connect.

Core Concepts

Places — A bounded context of interaction. The blocking test: can you interact with what's behind? No → different Place. Yes → same Place with local state changes. Places get IDs (P1, P2...) and can contain subplaces (P2.1, P2.2).

Affordances — Things you can act upon. UI affordances (U): inputs, buttons, displays. Code affordances (N): methods, subscriptions, stores, framework mechanisms.

Wiring — How affordances connect. Wires Out = control flow (what triggers what). Returns To = data flow (where output goes). Navigation wires go to Places, not to affordances inside them.

The Output: Affordance Tables

Every breadboard produces these tables:

Places Table

#	Place	Description
P1	Search Page	Main search interface
P2	Detail Page	Individual result view

UI Affordances Table

#	Place	Component	Affordance	Control	Wires Out	Returns To
U1	P1	search-detail	search input	type	→ N1	—
U2	P1	search-detail	loading spinner	render	—	—

Code Affordances Table

#	Place	Component	Affordance	Control	Wires Out	Returns To
N1	P1	search-detail	`activeQuery.next()`	call	→ N2	—
N2	P1	search-detail	`activeQuery` subscription	observe	→ N3	—

Data Stores Table

#	Place	Store	Description
S1	P1	`results`	Array of search results

Column Definitions

Column	Description
#	Unique ID (P1, P2... for Places; U1, U2... for UI; N1, N2... for Code; S1, S2... for Stores)
Place	Which Place this affordance belongs to (containment)
Component	Which component/service owns this
Affordance	The specific thing you can act upon
Control	The triggering event: click, type, call, observe, write, render
Wires Out	What this triggers: `→ N4`, `→ P2` (control flow, including navigation)
Returns To	Where output flows: `→ N3` or `→ U2, U3` (data flow)

References

Load these progressively as needed:

File	Contains	Load When
concepts.md	Places, subplaces, place references, modes, containment vs wiring, navigation wiring	You need to determine Place boundaries or model containment
procedures.md	Step-by-step for mapping existing systems (11 steps) and designing from parts (8 steps)	Starting a breadboarding task
principles.md	Never use memory, mechanisms aren't affordances, two flows, data source rules, store placement	Reviewing or debugging a breadboard
catalog.md	Complete element/relationship reference, qualification criteria, verification checks	Quick lookup of what qualifies as what
visualization.md	Mermaid conventions, colors, lines, subgraphs, chunking, workflow annotations	Creating or reviewing Mermaid diagrams
slicing.md	Vertical slicing methodology, constraints, procedure, visualization	Breaking a breadboard into implementation slices
reflection.md	Design smell detection, naming test, splitting affordances, fixing wiring	Reviewing a breadboard for correctness
examples.md	Example A: mapping existing system, Example B: designing from parts + slicing	Need a worked example for reference

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Breadboarding AI skill do?

Transform a workflow description into affordance tables showing UI and Code affordances with their wiring. Use to map existing systems or design new ones from shaped parts.

Why use Breadboarding on TypingMind?

Because you install it once and use it with any model. Breadboarding is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Breadboarding in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/koolamusic/claudefiles/tree/main/skills/breadboarding. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Breadboarding?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Breadboarding?

As many as you like. As long as a model supports skills, you can use Breadboarding with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Breadboarding AI skill free?

Yes. It is published on GitHub by koolamusic under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Breadboarding

Use Cases

1. Mapping an Existing System

2. Designing from Shaped Parts

3. Mixtures

Core Concepts

The Output: Affordance Tables

Places Table

UI Affordances Table

Code Affordances Table

Data Stores Table

Column Definitions

References

Bundled files

Frequently asked questions

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