Pinscope

Agentic schematic validation — catch hardware design errors before you fab.

Pinscope reviews your schematic against every component datasheet. It parses your netlist and BOM into a queryable design graph, extracts pin tables and specs from datasheet PDFs with Claude, and runs an agentic per-IC review that compares your circuit neighborhood to the datasheet's reference application — flagging wrong pull-ups, missing decoupling, voltage-domain violations, swapped signals, and derating failures, each finding cited back to the datasheet page that backs it up.

Pinscope is the open-source core of pinscope.ai (same code, hosted, with team accounts). Self-hosting it like this is fully supported: everything runs locally against your own Anthropic API key, no account or cloud services required.

How it works

Upload BOM + netlist + datasheets
        │
        ▼
Parse BOM ─ Extract IC pintables ─ Extract passives ─ DigiKey/value fallback
        │
        ▼
Build design graph  (bipartite: components ⇄ nets, queryable)
        │
        ▼
Per-IC direct datasheet review  (Claude reads the PDF + circuit neighborhood,
        │                        queries the graph, cites pages for findings)
        ▼
Report + BOM summary + capacitor derating table

• Netlists: PADS-PCB ASCII (.asc) and EDIF 2.0.0 (.edn) — exportable from KiCad, Altium, OrCAD, Cadence, Xpedition, EasyEDA, EAGLE
• BOM: CSV or XLSX
• Datasheets: PDF per MPN (DigiKey auto-fetch supported with API keys)
• Deterministic where possible: graph build, BOM collation, and capacitor voltage derating are exact computations, no AI
• Extraction is cached: every extracted pintable/spec lands in a shared library/ so an MPN is only ever paid for once

Quickstart

Prereqs: Python 3.12+, Node 20+, an Anthropic API key.

git clone https://github.com/Faradworks/Pinscope.git
cd Pinscope

# 1. Backend
python3 -m venv .venv && source .venv/bin/activate
pip install -r backend/requirements.txt
cp backend/.env.example .env          # then set ANTHROPIC_API_KEY

# 2. Extraction skills (one-time): uploads the three extraction prompts in
#    skills/ to YOUR Anthropic Console account and writes their IDs into
#    backend/skills_manifest.json
python3 scripts/upload_skills.py --update

# 3. Run
python3 -m uvicorn backend.main:app --reload   # http://localhost:8000
cd frontend && npm install && npm run dev      # http://localhost:3000

Open http://localhost:3000, create a project, and upload the files from simple_project/ (an MSPM0G3507 + CH340E reference design) to see a full run end-to-end. Projects and the extraction library are stored in data/; you run as a local admin user — no login.

What's in the box

Layer	Where	What
Core library	backend/pinscopex/	Pydantic models, netlist/BOM parsers, graph builder, agentic validator, passive resolvers, taxonomy, derating
Backend	backend/	FastAPI — async pipeline with SSE progress, project + library storage, per-call API cost logging
Frontend	frontend/	Next.js 16 — dashboard, pipeline progress, report viewer with datasheet citations, derating table, admin console
Extraction skills	skills/	Claude Console Skills for pintable / passive-pattern / specs extraction
Taxonomy	taxonomy/	Living component taxonomy with per-subtype specs schemas

See CLAUDE.md for architecture details and development guidelines.

Configuration

Everything is env-driven (see backend/.env.example):

• ANTHROPIC_API_KEY — required; extraction and review models are configurable per stage
• DIGIKEY_CLIENT_ID / DIGIKEY_CLIENT_SECRET — optional; enables datasheet auto-fetch and parameter-based passive auto-resolve
• GCS_BUCKET — optional; swaps local data/ storage for Google Cloud Storage
• GEMINI_API_KEY + PROVIDER_* — optional; route individual stages to Gemini

Tests

pip install pytest pytest-asyncio
pytest tests/ -q

Tests run against simple_project/ — it's the ground-truth reference design.

License

AGPL-3.0. Commercial licensing is available — contact dev@faradworks.com.