datasheets

Datasheets Skill

Purpose

Extract structured, machine-readable specifications from component datasheet PDFs and make them available to analyzer skills. Works on whatever PDFs are downloaded under <project>/datasheets/ (downloads are owned by distributor skills like digikey, mouser, lcsc, element14).

Scope

This skill owns:

• Extraction schema — the canonical JSON structure for per-MPN specs. Versioned via EXTRACTION_VERSION in scripts/datasheet_extract_cache.py.
• PDF page selection — heuristics to pick pages most likely to contain pinouts, e-chars, applications, SPICE models.
• Quality scoring — weighted rubric (pin coverage, voltage ratings, application info, electrical chars, SPICE specs).
• Consumer API — helpers in scripts/datasheet_features.py for other skills to query specific fields (e.g., get_regulator_features(mpn), get_mcu_features(mpn)).
• Verification — consistency checks between extracted data and schematic/PCB usage.

Non-goals

• No PDF downloading. That is owned by distributor skills (digikey, mouser, lcsc, element14).
• No global library. Each project's extractions live in <project>/datasheets/extracted/. There is no shared cross-project cache.

Cache location

<project>/
  design.kicad_sch
  datasheets/
    TPS61023DRLR.pdf        # downloaded by distributor skills
    extracted/
      manifest.json         # extraction manifest (legacy name: index.json)
      TPS61023DRLR.json     # structured extraction (this skill's output)

Reference guides

• references/extraction-schema.md — canonical schema, every field defined
• references/field-extraction-guide.md — how to find each field in datasheets from common vendors (TI, ST, NXP, Espressif, Microchip)
• references/quality-scoring.md — rubric details, score thresholds
• references/consumer-api.md — how kicad/emc/spice/thermal consume extractions

Entry-point scripts

• scripts/datasheet_extract_cache.py — cache manager, resolver, indexer
• scripts/datasheet_page_selector.py — page selection heuristics
• scripts/datasheet_score.py — extraction quality scoring
• scripts/datasheet_verify.py — cross-check extraction vs schematic usage
• scripts/datasheet_features.py — consumer helper API (new in v1.3)

Extraction workflow

1. User runs an analyzer or requests extraction.
2. This skill checks the cache (<project>/datasheets/extracted/<MPN>.json).
3. On cache miss / stale / low score: Claude reads selected PDF pages and extracts structured data.
4. Extraction is scored; if score ≥ 6.0, cached.
5. Consumers query via datasheet_features.py.

When to trigger this skill

• Immediately after downloading datasheets via sync_datasheets_digikey.py, sync_datasheets_lcsc.py, or equivalent. Without extraction, IC-aware checks (VM-001 rail voltage, PS-001 power-good, PR-004 USB, DP-002 USB speed classification) fall back to heuristics on unknown ICs.
• Before running analyzers on a new project where datasheets are present but datasheets/extracted/ is empty — the analyzers won't produce the extractions themselves.
• When a review flags low trust level due to missing manufacturer evidence: extracting the ICs referenced by power regulators, MCUs, and high-speed peripherals typically flips trust_level: low → mixed or high.
• When a user asks for pin verification ("verify U1 pin names match datasheet") — this skill's cached extraction is the authoritative source.