Pinscope open-source core
Agentic schematic validation: datasheet extraction via Claude Console Skills, netlist/BOM design graph, per-IC direct datasheet review with page citations, capacitor derating, Next.js report UI. Extracted from the Pinscope cloud codebase. Auth and billing live in the private gateway repo behind stable seams (billing_hook.py, adapter files listed in CLAUDE.md).
This commit is contained in:
Siddharth Kothari
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---
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skill_name: extract-pattern
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description: Extract passive component MPN pattern (resistor, capacitor, inductor) from a datasheet PDF. Returns structured data via the save_pattern tool.
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---
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# Extract Passive Component Pattern
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Extract the part numbering system from a passive component datasheet (resistor, capacitor, inductor) and return it as structured JSON via the `save_pattern` tool.
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## Steps
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### 1. Read the datasheet PDF
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The datasheet PDF is provided in the user message. Focus on finding the **Part Numbering System**, **Ordering Information**, or **Explanation of Part No.** section — every passive component datasheet has one. This section shows:
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- A diagram or table breaking the MPN into positional fields
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- The meaning of each field position
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- Lookup tables mapping codes to values (sizes, tolerances, voltage ratings, etc.)
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- An example part number with decoded fields
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Also identify from the front page:
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- **Manufacturer name** (e.g., "Uniroyal", "Samsung Electro-Mechanics")
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- **Component type** — must be one of: `resistor`, `capacitor`, `inductor`
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- **Series/product name** (e.g., "Thick Film Chip Resistors", "CL Series MLCC")
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### 2. Extract each field
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For every field in the part number format, extract:
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- **name** — a short snake_case identifier matching the regex group name. Use these standard names where applicable:
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- `size` — package size code
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- `tolerance` — value tolerance
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- `resistance` — resistance value digits (for resistors)
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- `capacitance` — capacitance value digits (for capacitors)
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- `inductance` — inductance value digits (for inductors)
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- `voltage` — rated voltage
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- `wattage` — power rating (resistors)
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- `dielectric` — temperature characteristic / dielectric type (capacitors)
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- `packing_type` — tape/reel vs bulk
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- `packing_qty` — quantity per reel
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- `series` — product series prefix
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- `special` — special features
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- `thickness` — component thickness
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- `reserved` — reserved/unused codes
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- **position** — 0-based character offset in the MPN string
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- **length** — number of characters
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- **description** — human-readable description from the datasheet
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- **lookup** — complete mapping of code -> meaning extracted from the datasheet. For the primary value field (resistance/capacitance/inductance), leave lookup as `{}` since it's decoded algorithmically.
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### 3. Determine the value decoder
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Based on the component type and how the value field works, select the decoder type:
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**For capacitors** using 3-digit EIA code in picofarads (e.g., "106" = 10x10^6 pF = 10uF):
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```json
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{
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"type": "eia3_pf",
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"base_unit": "pF",
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"output_unit": "F",
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"letter_multipliers": {},
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"zero_code": null,
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"conditional_on": null
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}
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```
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**For resistors** using 4-digit code where the digit layout depends on tolerance:
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```json
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{
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"type": "eia4_ohm_conditional",
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"base_unit": "ohm",
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"output_unit": "ohm",
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"letter_multipliers": {"J": -1, "K": -2, "L": -3, "M": -4, "N": -5, "P": -6},
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"zero_code": "0000",
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"conditional_on": {
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"field": "tolerance",
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"high_tolerance": ["J"],
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"high_tolerance_layout": {
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"significant_start": 1,
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"significant_count": 2,
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"multiplier_index": 3
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},
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"low_tolerance_layout": {
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"significant_start": 0,
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"significant_count": 3,
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"multiplier_index": 3
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}
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}
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}
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```
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Read the datasheet carefully for:
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- Which tolerance codes use 3 vs 2 significant digits (the `high_tolerance` list)
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- Whether letter multiplier codes are supported (J, K, L, etc.) and their exponent values
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- Whether there's a special zero/jumper code
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If the datasheet describes a different encoding scheme, adapt the decoder accordingly.
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### 4. Build the regex pattern
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Build a Python regex with named capture groups, one per field. The regex must:
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- Start with `^` and end with `$` (full MPN match)
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- Use `(?P<name>...)` syntax for each field
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- Be as specific as possible — enumerate known codes in alternation groups (e.g., `(?P<size>0603|0805|1206)`) rather than broad patterns like `\d{4}`
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- Handle the value field with appropriate character classes (digits + any letter multiplier codes)
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### 5. Assign component subtype (taxonomy)
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The existing passive taxonomy subtypes are provided in the system prompt under `EXISTING PASSIVE TAXONOMY SUBTYPES`. Pick the most specific matching subtype.
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If no existing subtype fits, propose a new one following the dot-notation convention (`passive.{type}.{specific}`).
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### 6. Quality checks
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Before producing output, verify:
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- The regex matches ALL example MPNs (provided in the system prompt as BOM MPNs)
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- Every field has position + length that sum correctly across the full MPN
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- No field positions overlap
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- The primary value field (resistance/capacitance) has an empty `lookup` dict (it's decoded algorithmically)
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- All other fields have non-empty lookup dicts with codes extracted from the datasheet
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- The value decoder type is appropriate for the component type
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### 7. Validate and output
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Validate your extraction against the output schema:
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```bash
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python3 /skills/extract-pattern/validate.py '<your JSON here>'
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```
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If validation passes, call the `save_pattern` tool with the structured result.
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Do NOT write files to disk — use the tool.
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{
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"type": "object",
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"properties": {
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"manufacturer": {"type": "string"},
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"series": {"type": "string"},
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"component_type": {
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"type": "string",
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"enum": ["resistor", "capacitor", "inductor"]
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},
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"component_subtype": {"type": "string"},
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"description": {"type": "string"},
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"regex": {"type": "string"},
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"fields": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"name": {"type": "string"},
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"position": {"type": "integer"},
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"length": {"type": "integer"},
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"description": {"type": "string"},
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"lookup": {"type": "object"}
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},
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"required": ["name", "position", "length", "description"]
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}
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},
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"value_decoder": {"type": "object"},
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"example_mpns": {
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"type": "array",
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"items": {"type": "string"}
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}
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},
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"required": [
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"manufacturer", "series", "component_type", "component_subtype",
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"description", "regex", "fields", "value_decoder", "example_mpns"
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]
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}
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#!/usr/bin/env python3
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"""Validate extraction output against the pattern schema."""
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import json
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import re
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import sys
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from pathlib import Path
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SCHEMA_PATH = Path(__file__).parent / "schema.json"
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def validate(data: dict) -> list[str]:
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"""Return list of validation errors (empty = valid)."""
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errors = []
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schema = json.loads(SCHEMA_PATH.read_text())
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for field in schema.get("required", []):
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if field not in data:
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errors.append(f"Missing required field: {field}")
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if "component_type" in data:
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ct = data["component_type"]
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if ct not in ("resistor", "capacitor", "inductor"):
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errors.append(f"component_type must be resistor/capacitor/inductor, got: {ct!r}")
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if "regex" in data:
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try:
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pattern = re.compile(data["regex"])
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except re.error as e:
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errors.append(f"Invalid regex: {e}")
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pattern = None
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if pattern and "example_mpns" in data:
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for mpn in data["example_mpns"]:
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if not pattern.match(mpn):
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errors.append(f"Regex does not match example MPN: {mpn!r}")
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if "fields" in data:
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fields = data["fields"]
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if not isinstance(fields, list) or len(fields) == 0:
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errors.append("fields must be a non-empty array")
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else:
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for i, field in enumerate(fields):
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for f in ["name", "position", "length", "description"]:
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if f not in field:
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errors.append(f"fields[{i}] missing: {f}")
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if "value_decoder" in data:
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vd = data["value_decoder"]
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if not isinstance(vd, dict) or "type" not in vd:
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errors.append("value_decoder must be an object with a 'type' field")
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return errors
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if __name__ == "__main__":
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if len(sys.argv) < 2:
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print("Usage: python3 validate.py '<json string>'")
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sys.exit(1)
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try:
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data = json.loads(sys.argv[1])
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except json.JSONDecodeError as e:
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print(f"INVALID JSON: {e}")
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sys.exit(1)
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errors = validate(data)
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if errors:
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print("VALIDATION FAILED:")
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for err in errors:
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print(f" - {err}")
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sys.exit(1)
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else:
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print("VALIDATION PASSED")
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