Adapt Pinscope to DeepSeek, auto datasheets, and a shared library.
Based on manvalan/pinscope main. Default LLM is DeepSeek with local skills and PDF ingest. Datasheets are fetched from LCSC/TI, stored in the component library, and review extracts abs-max with a deeper checklist. Adds scripts/update-pinscope.sh for the production host.
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Cursor Agent
@@ -1,9 +1,12 @@
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"""Async datasheet extraction using Claude API.
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"""Async datasheet extraction using the configured LLM provider.
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Ports the extraction steps from run_pipeline.py to async:
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- extract_pintable: Pin table + package info + taxonomy assignment
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- extract_pattern: Passive MPN pattern
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- extract_specs: Component specs (discrete, connectors, crystals, etc.)
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Skills (SKILL.md + validate.py) run locally for DeepSeek/Gemini. Anthropic
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can still use Console Skills when a skill_id is in skills_manifest.json.
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"""
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from __future__ import annotations
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@@ -55,7 +58,7 @@ from backend.services.llm import (
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PINTABLE_TOOL = {
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"name": "save_pintable",
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"description": "Save the extracted pin table, package info, and component subtype.",
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"description": "Save the extracted pin table, package info, absolute-maximum ratings, and component subtype.",
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"input_schema": {
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"type": "object",
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"properties": {
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@@ -94,6 +97,31 @@ PINTABLE_TOOL = {
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"required": ["number", "name"],
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},
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},
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"absolute_maximum_ratings": {
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"type": "array",
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"description": (
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"Rows from the Absolute Maximum Ratings table: supplies, "
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"pin voltages, current, temperature. Omit recommended-"
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"operating values. Empty array if the table is unreadable."
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),
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"items": {
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"type": "object",
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"properties": {
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"parameter": {
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"type": "string",
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"description": "As printed, e.g. 'VCC', 'VIN', 'Storage temperature'",
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},
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"min": {"type": ["number", "null"]},
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"max": {"type": ["number", "null"]},
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"unit": {"type": "string", "description": "V, mA, °C, …"},
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"source_page": {
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"type": "integer",
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"description": "1-based datasheet page of this row",
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},
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},
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"required": ["parameter", "unit", "source_page"],
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},
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},
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},
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"required": ["component_subtype", "component_subtype_description", "package_info", "pintable"],
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},
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@@ -208,14 +236,16 @@ SPECS_TOOL = {
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# ---------------------------------------------------------------------------
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_MAX_PDF_PAGES = 90
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_MAX_PDF_PAGES = 120
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log = logging.getLogger(__name__)
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# Keywords used to find relevant pages for each extraction stage.
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_PINTABLE_KEYWORDS = re.compile(
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r"pin\s*(out|diagram|configuration|description|assignment|function|name|table|map)"
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r"|ball\s*map|package\s*(pin|drawing|outline)|signal\s+description",
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r"|ball\s*map|package\s*(pin|drawing|outline)|signal\s+description"
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r"|absolute\s+maximum|recommended\s+operating|electrical\s+characteristics"
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r"|ordering\s+information|device\s+information",
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re.IGNORECASE,
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)
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@@ -280,6 +310,44 @@ def _to_tool(d: dict) -> ToolSchema:
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)
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def _coerce_abs_max(raw: object) -> list[dict]:
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"""Keep well-formed abs-max rows; drop garbage rather than failing extraction."""
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if not isinstance(raw, list):
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return []
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out: list[dict] = []
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for row in raw:
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if not isinstance(row, dict):
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continue
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parameter = str(row.get("parameter") or "").strip()
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unit = str(row.get("unit") or "").strip()
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page = row.get("source_page")
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if not parameter or not unit:
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continue
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try:
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source_page = int(page)
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except (TypeError, ValueError):
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continue
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if source_page < 1:
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continue
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def _num(v: object) -> float | None:
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if v is None or v == "":
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return None
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try:
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return float(v)
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except (TypeError, ValueError):
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return None
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out.append({
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"parameter": parameter,
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"min": _num(row.get("min")),
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"max": _num(row.get("max")),
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"unit": unit,
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"source_page": source_page,
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})
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return out
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_GENERATE_SPECS_TOOL = {
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"name": "save_specs_schema",
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"description": "Save the standardized parameter schema for a component type.",
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@@ -479,7 +547,7 @@ async def extract_pintable(
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taxonomy = format_for_prompt("ic", tax_dir)
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trimmed = _select_pages(pdf_path, _PINTABLE_KEYWORDS)
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skill_id, version = settings.get_skill("extract-pintable")
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skill_id, version = settings.get_skill_or_none("extract-pintable")
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system = (
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f"DYNAMIC CONTEXT FOR THIS EXTRACTION:\n"
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f"MPN: {mpn}\n\n"
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@@ -548,7 +616,9 @@ async def extract_pintable(
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component_subtype=subtype,
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package_info=result["package_info"],
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pintable=result["pintable"],
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absolute_maximum_ratings=[],
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absolute_maximum_ratings=_coerce_abs_max(
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result.get("absolute_maximum_ratings") or [],
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),
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rules=[],
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)
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@@ -576,7 +646,7 @@ async def extract_pattern(
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tax_dir = taxonomy_dir or settings.taxonomy_dir
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taxonomy = format_for_prompt("passive", tax_dir)
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skill_id, version = settings.get_skill("extract-pattern")
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skill_id, version = settings.get_skill_or_none("extract-pattern")
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system = (
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f"DYNAMIC CONTEXT FOR THIS EXTRACTION:\n\n"
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f"EXISTING PASSIVE TAXONOMY SUBTYPES:\n{taxonomy}\n\n"
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@@ -665,7 +735,7 @@ async def extract_specs(
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subtypes_text = format_for_prompt(component_type, tax_dir)
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specs_text = format_specs_for_prompt(component_type, tax_dir)
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skill_id, version = settings.get_skill("extract-specs")
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skill_id, version = settings.get_skill_or_none("extract-specs")
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system = (
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f"DYNAMIC CONTEXT FOR THIS EXTRACTION:\n"
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f"MPN: {mpn}\n"
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