⚡ kicad-happy

AI-powered design review for KiCad. Analyzes schematics, PCB layouts, and Gerbers. Catches real bugs before you order boards.

Works with Claude Code, OpenAI Codex, GitHub Copilot CLI, and Gemini CLI, as a GitHub Action for automated PR reviews, or as standalone Python scripts you can run anywhere.

These skills turn your AI coding agent into a full-fledged electronics design assistant that understands your KiCad projects at a deep level: parses schematics and PCB layouts into structured data, cross-references component values against datasheets, detects common design errors, and walks you through the full prototype-to-production workflow.

🔬 What it looks like in practice

Point your agent at a KiCad project and it does the rest — parses every schematic and PCB file, traces every net, computes every voltage, and tells you what's wrong before you spend money on boards.

"Analyze my KiCad project at hardware/rev2/"

Here's a condensed example from an open-source robot controller board. The agent found all of this automatically:

It builds your power tree — tracing every regulator from input to load, computing output voltages from feedback dividers:

VBUS (USB-C / battery input, fused)
├── AP63357 buck (500kHz switching) → 5V
│   └── Feedback: R8/R9 ratio=0.155 → Vout=3.87V
│       Power dissipation: ~0.15W (85% efficiency assumed)
└── RT9080-3.3 LDO → 3.3V
    └── Decoupling: 16 caps, 10.8µF total

It identifies every subcircuit — not just passives, but the functional blocks and how they connect:

It audits every connector for ESD protection — and flags the ones that are exposed:

ESD coverage: 19 connectors audited

  USB-C:     ESD clamp on D+/D-  ✓ (partial — 13 signal pins per ground ⚠️)
  Fuse F1:   2.5A motor input  ✓
  Fuse F2:   0.75A signal input  ✓
  ⚠️ 6-pin header:    no protection (exposed signals)
  ⚠️ Motor outputs:   no protection (exposed to back-EMF)
  ⚠️ Servo connectors: no protection (exposed signals)
  ⚠️ Sensor port:     no protection
  ... 19 of 19 connectors have coverage gaps

It validates your passive networks — computing actual circuit behavior from component values:

It suggests applicable certifications — based on what it detects in the design:

Suggested certifications:
  FCC Part 15 Subpart B (US) — unintentional radiator compliance
  CISPR 32 / CE EMC Directive (EU) — EMC compliance for EU market

It checks production readiness — BOM lock status, connector ground distribution, decoupling adequacy:

BOM lock: 0% — no MPNs assigned (prototype stage)
Decoupling: 5 rails, 34 caps total (132µF motor, 110µF logic, 10.8µF 3.3V)
Connector ground: USB-C has 13:1 signal-to-ground ratio (recommended ≤3:1)

For complete examples with all sections, see:

• Example 1: Robot controller — schematic + PCB + EMC + SPICE, 184 components
• Example 2: GNSS disciplined oscillator — full workflow including datasheet sync, 296 components, 10 power rails, Ethernet + USB + SMA

For the end-to-end walkthrough from S-expression parsing through signal detection and datasheet cross-referencing, see How It Works.

🚀 Install

[!TIP] For detailed installation, upgrade, and troubleshooting guidance across all platforms, have your AI agent read install-guidance.md. It covers platform-specific quirks, known bugs, workarounds, and OS-specific issues.

Claude Code:

/plugin marketplace add aklofas/kicad-happy
/plugin install kicad-happy@kicad-happy

[!NOTE] /plugin update may not detect new versions due to a known Claude Code issue. To get the latest version, clear the cache and reinstall:

rm -rf ~/.claude/plugins/cache/kicad-happy ~/.claude/plugins/marketplaces/kicad-happy
/plugin marketplace add aklofas/kicad-happy
/plugin install kicad-happy@kicad-happy

OpenAI Codex:

Use Codex's built-in skill installer first:

"Use $skill-installer to install the kicad-happy skills from https://github.com/aklofas/kicad-happy"

If you prefer a manual install, install the skills into ~/.codex/skills/.

Google Gemini CLI:

gemini skills install <url> does not recurse into this monorepo's skills/ directory. Clone and link all 12 at once:

git clone https://github.com/aklofas/kicad-happy.git
gemini skills link ./kicad-happy/skills

Or install all 12 skills directly from the URL using --path (requires Gemini CLI ≥ Jan 13 2026):

for skill in kicad spice emc datasheets bom digikey mouser lcsc element14 jlcpcb pcbway kidoc; do
  gemini skills install https://github.com/aklofas/kicad-happy.git --path skills/$skill
done

See install-guidance.md for workspace-scope installs and upgrade notes.

Claude Code (macOS / Linux):

git clone https://github.com/aklofas/kicad-happy.git
cd kicad-happy
mkdir -p ~/.claude/skills
for skill in kicad spice emc datasheets bom digikey mouser lcsc element14 jlcpcb pcbway kidoc; do
  ln -sf "$(pwd)/skills/$skill" ~/.claude/skills/$skill
done

OpenAI Codex — global install (macOS / Linux):

git clone https://github.com/aklofas/kicad-happy.git
cd kicad-happy
mkdir -p ~/.codex/skills
for skill in kicad spice emc datasheets bom digikey mouser lcsc element14 jlcpcb pcbway kidoc; do
  ln -sf "$(pwd)/skills/$skill" ~/.codex/skills/$skill
done

Windows PowerShell (Codex):

git clone https://github.com/aklofas/kicad-happy.git
cd kicad-happy
New-Item -ItemType Directory -Force "$HOME\.codex\skills" | Out-Null
"kicad","spice","emc","datasheets","bom","digikey","mouser","lcsc","element14","jlcpcb","pcbway","kidoc" | ForEach-Object {
  New-Item -ItemType SymbolicLink -Path "$HOME\.codex\skills\$_" -Target "$(Get-Location)\skills\$_" -Force | Out-Null
}

Note: Windows symlinks may require Developer Mode or elevated privileges.

The analysis scripts are pure Python 3.10+ with zero required dependencies. No pip install, no Docker, no KiCad installation needed.

⚙️ GitHub Action

Also available as a GitHub Action for automated PR reviews. Every push and PR that touches KiCad files gets a commit status check and a structured review comment — power tree, SPICE results, EMC risk, thermal analysis, and more. Optionally chain with Claude for AI-powered natural-language reviews.

See the GitHub Action setup guide for workflow examples, diff-based PR reviews, and AI-powered review configuration.

📦 Skills