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Agent skill

Fabll

by atopile in atopile/atopile

How FabLL (faebryk.core.node) maps Python node/trait declarations into the TypeGraph + instance graph, including field/trait invariants and instantiation patterns.

MITAuto-check passed

Install Fabll

skills CLI
$ npx skills add atopile/atopile --skill fabll -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install atopile/atopile fabll --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/atopile/atopile.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/fabll .claude/skills/fabll && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
fabll
GitHub stars
4k
Token cost
~1.1k tokens
SKILL.md length
399 words
Files
1
Skills in repo
19
Repo updated
First seen
Licence
MIT

At a glance

How FabLL (faebryk.core.node) maps Python node/trait declarations into the TypeGraph + instance graph, including field/trait invariants and instantiation patterns.

  • Works in 3 steps: Prefer adding behavior as a Trait rather… → If you need a new structural… → Keep an eye on invariants enforced at…
  • Defining new components
  • SKILL.md covers Quick Start, Relevant Files, Dependants (Call Sites) and How to Work With / Develop /…, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Fabll is an agent skill from atopile/atopile. How FabLL (faebryk.core.node) maps Python node/trait declarations into the TypeGraph + instance graph, including field/trait invariants and instantiation patterns. Use when defining new components or traits, working with the Node API, or understanding type registration.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It works with Python. The repository describes itself as: Design circuit boards with code! ✨ Get software-like design reuse 🚀, validation, version control and collaboration in hardware; starting with electronics ⚡️. The licence is MIT.

When your agent uses it

  • Defining new components
  • Working with the Node API
  • Understanding type registration

Example prompts

  • “/fabll”

Requirements

  • Python 3

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. Prefer adding behavior as a Trait rather than deepening class hierarchies.
  2. If you need a new structural relation/field kind, it lives in src/faebryk/core/node.py (field system).
  3. Keep an eye on invariants enforced at class creation time (metaclass + init_subclass).

What it can do on your machine

Read from SKILL.md and the folder at commit 619eda7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Fabll loads about 1.1k tokens when it runs. Until then it costs about 69 tokens; SKILL.md has 399 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~69
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from atopile/atopile at commit 619eda7, republished under its MIT licence (© atopile). 399 words, ~1,105 tokens.

Download SKILL.mdSave it as .claude/skills/fabll/SKILL.md (or your agent's skills folder).
name
fabll
description
How FabLL (faebryk.core.node) maps Python node/trait declarations into the TypeGraph + instance graph, including field/trait invariants and instantiation patterns. Use when defining new components or traits, working with the Node API, or understanding type registration.

FabLL (Fabric Low Level) Module

fabll (primarily src/faebryk/core/node.py) is the high-level Python API for defining and working with hardware components. It bridges the gap between Python classes and the underlying TypeGraph and instance graph.

Quick Start

python
import faebryk.core.faebrykpy as fbrk
import faebryk.core.graph as graph
import faebryk.core.node as fabll

g = graph.GraphView.create()
tg = fbrk.TypeGraph.create(g=g)

class _App(fabll.Node):
    pass

app = _App.bind_typegraph(tg=tg).create_instance(g=g)

Relevant Files

  • src/faebryk/core/node.py (Node/Traits/fields, type registration, binding/instantiation helpers)
  • src/faebryk/core/faebrykpy.py (edge types used by FabLL under the hood)
  • src/faebryk/core/graph.py (GraphView wrapper used by instances)

Dependants (Call Sites)

  • Library (src/faebryk/library/): Every component (Resistor, Capacitor, etc.) inherits from Node.
  • Compiler: Generates Node subclasses dynamically from ato files.
  • Solvers: Operate on Node instances to extract parameters and constraints.

How to Work With / Develop / Test

Core Concepts
  • Nodes are wrappers over graph instances: a fabll.Node is constructed with a graph.BoundNode.
  • Declaration via class attributes:
    • structural children: SomeType.MakeChild(...)
    • trait attachments: Traits.MakeEdge(SomeTrait.MakeChild().put_on_type()) (or similar)
  • Binding:
    • type binding: MyType.bind_typegraph(tg)
    • instance creation: .create_instance(g)
  • Type identifiers:
    • library types (faebryk.library.*) intentionally have short identifiers (class name) for ato imports
    • non-library types include a module-derived suffix; type IDs must be unique (enforced in Node._register_type)
Development Workflow
  1. Prefer adding behavior as a Trait rather than deepening class hierarchies.
  2. If you need a new structural relation/field kind, it lives in src/faebryk/core/node.py (field system).
  3. Keep an eye on invariants enforced at class creation time (metaclass + __init_subclass__).
Testing
  • Core tests: ato dev test --llm test/core/test_node.py -q and ato dev test --llm test/library/test_traits.py -q

Best Practices

  • Prefer Traits: Don't add methods to Node subclasses if they can be a Trait. This allows them to be applied to different component families.
  • Avoid deep inheritance: FabLL enforces single-level subclassing for node types (Node.__init_subclass__).
  • Type-safe traversal: when you must traverse trait edges manually, prefer EdgeTrait.traverse(trait_type=...).
Show full SKILL.md (155 more words)Show less

Internals & Runtime Behavior

Instantiation & Lifecycle
  • Don’t call MyNode() with no args: instances are created from a bound type via bind_typegraph(...).create_instance(...).
  • TypeGraph context is required:
    python
    import faebryk.core.graph as graph
import faebryk.core.faebrykpy as fbrk

g = graph.GraphView.create()
tg = fbrk.TypeGraph.create(g=g)
inst = MyNode.bind_typegraph(tg).create_instance(g=g)
  • Single-level subclassing invariant: Node.__init_subclass__ forbids “deeper than one level” inheritance for node types.
Trait Implementation
  • Traits are Nodes: Traits are not just Python mixins; they are Node subclasses that typically contain an ImplementsTrait edge.
  • Trait Definition:
    python
    class MyTrait(Node):
    is_trait = Traits.MakeEdge(ImplementsTrait.MakeChild().put_on_type())
  • Resolution: Use node_instance.get_trait(TraitType) to retrieve a trait instance. This performs a graph traversal.
Performance & Memory
  • Type Creation: Creating a type involves significant overhead (executing fields, resolving dependencies). Once created, instantiating instances is faster but still involves allocation in the Zig backend.
  • Tree Structure: Nodes are linked via EdgeComposition. add_child creates this edge. Large trees (10k+ nodes) should be constructed carefully to avoid Python loop overhead; the underlying graph is efficient, but Python interactions cost time.

© atopile, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/fabll of atopile/atopile.

Open the folder on GitHubat commit 619eda7

Compare with similar skills

Fabll next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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NotebookLM Research AssistantPleasePrompto/notebooklm-skill7.8k14 repos~2.4kAutomated safety check: NotesMIT
Manim Video Productionbrowser-use/video-use28k6 repos~3kAutomated safety check: PassMIT
Code Review ChecklistshareAI-lab/learn-claude-code78k5 repos~1.1kAutomated safety check: PassMIT

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  • Graph

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Works with

Questions about Fabll

What does Fabll do?

How FabLL (faebryk.core.node) maps Python node/trait declarations into the TypeGraph + instance graph, including field/trait invariants and instantiation patterns. Fabll is an agent skill from atopile/atopile.node) maps Python node/trait declarations into the TypeGraph + instance graph, including field/trait invariants and instantiation patterns.

When should I use Fabll?

Fabll fits situations like: defining new components; working with the Node API; understanding type registration.

How do I install Fabll in Claude Code?

Run `npx skills add atopile/atopile --skill fabll -a claude-code`. Or copy the skill folder (.claude/skills/fabll in atopile/atopile) into .claude/skills/fabll in your project. Claude Code loads it when a task matches its description.

How do I install Fabll in Codex?

Run `npx skills add atopile/atopile --skill fabll -a codex`. Or copy the skill folder (.claude/skills/fabll in atopile/atopile) into .agents/skills/fabll in your project. Codex loads it when a task matches its description.

Can I use Fabll in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add atopile/atopile --skill fabll -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fabll, .gemini/skills/fabll, .github/skills/fabll and .opencode/skills/fabll in your project.

What does Fabll need to run?

SKILL.md names no scripts, command-line tools or credentials: Fabll is instructions for the agent only. Our summary lists: Python 3.

Does Fabll access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Fabll safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Fabll use?

Fabll is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Fabll use?

About 1.1k tokens (SKILL.md is roughly 4.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Fabll?

Skills that share tags, products or a category with Fabll: MCP Server Builder (anthropics/skills, 180k stars), PDF Processing (anthropics/skills, 180k stars), NotebookLM Research Assistant (PleasePrompto/notebooklm-skill, 7.8k stars) and Manim Video Production (browser-use/video-use, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Fabll?

atopile (a GitHub organization) maintains it in atopile/atopile, which has 3,979 GitHub stars. The repository holds 19 skills in this directory. The repository was last updated on June 13, 2026.

Source: atopile/atopile on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.