faebryk

Name: faebryk
Author: atopile

Other Verified

Repo stars 3,367
License MIT
Author updated Live
Author repo atopile

Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network

Domain: Other
Compatible agents: Claude Code
Cursor
Cline
Codex
Windsurf
Gemini CLI

+20
Trust score: 94 / 100 · audit passed
Author / version / license: @atopile · MIT
Token usage: Lean
Setup complexity: Guided setup
External API key: Not required
Operating systems: macOS · Linux · Windows
Runtime requirements: Node.js · Python
Permissions: Read-only
Write / modify
Network behavior: Local-only
Install commands: 26 variants

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview faebryk.preview

---
name: faebryk
description: How Faebryk's TypeGraph works (GraphView + Zig edges), how to traverse/resolve references, and h…
category: other
runtime: Node.js / Python
---

# faebryk output preview

## PART A: Task fit
- Use case: How Faebryk's TypeGraph works (GraphView + Zig edges), how to traverse/resolve references, and how FabLL types/traits map onto edge types. Use when working with TypeGraph traversal, edge types, or building type-aware queries..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Quick Start / Relevant Files / Dependants (Call Sites)” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “How Faebryk's TypeGraph works (GraphView + Zig edges), how to traverse/resolve references, and how FabLL types/traits map onto edge types. Use when working with TypeGraph traversal, edge types, or building type-aware queries.”.
- **02** When the source has headings, the agent prioritizes “Quick Start / Relevant Files / Dependants (Call Sites)” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.

## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.

SKILL.md SKILL.md

---
name: faebryk
description: How Faebryk's TypeGraph works (GraphView + Zig edges), how to traverse/resolve references, and h…
category: other
source: atopile/atopile
---

# faebryk

## When to use
- How Faebryk's TypeGraph works (GraphView + Zig edges), how to traverse/resolve references, and how FabLL types/traits…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.

## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.

## Execution rules
- Organize steps around “Quick Start / Relevant Files / Dependants (Call Sites)” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.

## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies.

Evidence boundary and execution chain

The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.

skill "faebryk" {
  input      -> user goal + target files + boundaries + acceptance criteria
  context    -> Quick Start / Relevant Files / Dependants (Call Sites)
  rules      -> SKILL.md triggers / order / output contract
  runtime    -> Node.js / Python | read files, write/modify files | mostly runs locally
  guardrails -> usually needs no extra API key + small-sample validation + diff/log review
  output     -> copyable result + checklist + next iteration
}

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: How Faebryk's TypeGraph works (GraphView + Zig edges), how to traverse/resolve references, and how FabLL types/traits map onto e…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “Quick Start”, “Relevant Files”, “Dependants (Call Sites)”, “How to Work With / Develop / Test”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name faebryk directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “Quick Start / Relevant Files / Dependants (Call Sites)” before expanding.

Boundaries And Review

Dependencies: It usually needs no extra API key, so start with a small validation task.
Permissions: Declared permissions include read / write; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Faebryk Core Module

The Faebryk core here is the TypeGraph + edge types implemented in Zig and exposed to Python via faebryk.core.faebrykpy.

Source-of-truth for API + behavior:

src/faebryk/core/faebrykpy.py (Python-facing wrapper + type-safe EdgeTrait.traverse)
src/faebryk/core/zig/gen/faebryk/typegraph.pyi (public stubbed API surface)
src/faebryk/core/zig/src/faebryk/* (Zig implementation)

Quick Start

import faebryk.core.faebrykpy as fbrk
import faebryk.core.graph as graph

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

Relevant Files

src/faebryk/core/faebrykpy.py (re-exports + EdgeTraversal + type-safe EdgeTrait.traverse)
src/faebryk/core/zig/gen/faebryk/typegraph.pyi (TypeGraph stub)
Key edge types (imported by faebrykpy.py):
- EdgeComposition (parent/child structure)
- EdgeTrait / Trait (trait attachment)
- EdgePointer (references)
- EdgeInterfaceConnection (interface connections)
- EdgeOperand (solver operand wiring)
- EdgeType / EdgeNext (type graph plumbing)
Linker:
- Linker (used by compiler/linking stages)

Dependants (Call Sites)

FabLL: src/faebryk/core/node.py (binds Python classes into the TypeGraph; uses composition/trait edges)
Compiler: src/atopile/compiler/* (creates and links TypeGraphs)
Solver: src/faebryk/core/solver/* (operand edges and instance traversal)
Build/export pipeline: src/atopile/build_steps.py (visits type/instance edges for PCB/layout features)

How to Work With / Develop / Test

Core Concepts

GraphView + TypeGraph: a TypeGraph is created against a GraphView:

import faebryk.core.graph as graph
import faebryk.core.faebrykpy as fbrk

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

Type nodes vs instance nodes:
- TypeGraph stores type definitions (“what exists structurally on a type”)
- GraphView also holds instances created from those types (“a concrete design graph”)
EdgeTraversal: TypeGraph.ensure_child_reference(..., path=[...]) uses EdgeTraversal items to walk references through the type graph.

Development Workflow

Zig-side changes: edit src/faebryk/core/zig/src/faebryk/* (edges, typegraph internals).
Rebuild bindings: ato dev compile (imports faebryk.core.zig).
Python ergonomics: add wrappers/helpers in src/faebryk/core/faebrykpy.py (example: type-safe EdgeTrait.traverse).

Testing

TypeGraph-heavy tests live in compiler/runtime suites:
- ato dev test --llm test/compiler/test_typegraph.py -q
- ato dev test --llm test/compiler/test_runtime.py -q
Zig-backed traversal tests:
- ato dev test --llm test/core/zig/test_interface_pathfinder.py -q

Best Practices

Import edges/TypeGraph via faebryk.core.faebrykpy (so callers get Python helpers, not just raw generated types).
Prefer type-safe trait traversal:
- EdgeTrait.traverse(trait_type=SomeTrait) over stringly-typed trait_type_name=....
When building reference paths, be explicit about edge semantics (composition vs pointer vs trait) rather than relying on implicit string behavior.

Install command

One-click installPick a runner, click Copy, paste into your terminal, press enter

npx skills add atopile/atopile/.claude/skills/faebryk -y

Open general install guide

View skill source

Works with

Claude Code
Cursor
Cline
Codex
Windsurf
Gemini
OpenCode
Roo Code
AAide
+Augment
GitHub Copilot
OpenClaw
KKiro
Junie
WWarp
Goose
Trae
Kilo Code
OpenHands
Amp
Zencoder
CContinue
AIAider
CodeBuddy
CodeGeeX
Replit Agent

Security audit

94 / 100

Verified

未声明 version。
未限定 allowed-tools，默认拥有全部工具权限。

Metadata

Source: github
atopile/atopile
License: MIT
中文

More by @atopile

Category & tags

Category Other