Datasheets Skill

Related Skills

Skill	Relationship
`digikey` / `mouser` / `lcsc` / `element14`	Producers — download the PDFs under `<project>/datasheets/` that this skill extracts from
`kicad`	Primary consumer — VM-001/PU-001/FS-001/PP-001/LR-001/XT-001 + Phase 4b lookup detectors (AM-001/OV-001/TJ-001/FT-001/EX-001) query extractions via `lookup(mpn)` for verified-IC knowledge
`emc`	Consumer — switching-frequency, package-Rθ_JA, and operating-voltage data sharpen EMC heuristics
`spice`	Consumer — SPICE model presence + IBIS data feed simulation-readiness checks
`thermal`	Consumer — package Rθ_JA + junction temperature limits drive Tj estimates (TS-001..TJ-001)
`bom`	Indirect — coverage of structured extractions affects BOM verification confidence

Handoff guidance: This skill is consumer infrastructure. The typical flow is

distributor skill downloads PDF → datasheets skill extracts → analyzer skill queries

. Use this skill directly when (a) the user asks to extract or verify a specific MPN, (b) an analyzer reports

trust_level: low

and the gap is per-MPN extraction quality, or (c) a new MPN was added to the BOM and downstream detectors should pick up its verified specs. Don't run this skill in isolation if the user just wants a design review — call it from the kicad workflow at the "Sync datasheets" step instead.

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 schemas — canonical JSON structures for per-MPN specs. v1.4 ships 6 JSON Schema Draft 2020-12 schemas under
```
schemas/
```
(
```
base
```
,
```
pinout
```
,
```
spec_value
```
,
```
regulator
```
,
```
extraction
```
,
```
manifest
```
) plus 5 v1.4 category extensions (diode, transistor, opamp, mcu, crystal). v1.3 cache format (
```
EXTRACTION_VERSION
```
in
```
scripts/datasheet_extract_cache.py
```
) is still read for compat.
Typed access layer (v1.4) —
```
datasheet_types/
```
package exposes
```
DatasheetFacts
```
,
```
SpecValue
```
,
```
Pin
```
,
```
Pinout
```
,
```
lookup()
```
,
```
best()
```
,
```
trusted()
```
,
```
has_data()
```
. Recommended for all new consumers.
PDF page selection — heuristics to pick pages most likely to contain pinouts, e-chars, applications, SPICE models.
Quality scoring — v1.4 uses a three-dimension rubric (pinout completeness, base completeness, category-extension completeness, 0–100 scale). v1.3 5-dimension weighted rubric still applies to legacy caches.
Consumer APIs —
```
scripts/datasheet_lookup.py
```
for v1.4 typed access;
```
scripts/datasheet_features.py
```
for the v1.3 dict-shaped helpers (
```
get_regulator_features
```
,
```
get_mcu_features
```
,
```
get_pin_function
```
) — the v1.3 helpers dual-read v1.4 caches and translate to v1.3 dict shape for legacy detector code. Sunset planned for v1.6.
Verification —
```
datasheet_verify.py
```
(v1.3, schema-vs-usage cross-check) plus
```
datasheet_verify_v14_extraction
```
(v1.4, power_domain references resolve, recommended ≤ absolute, regulator pin references exist).

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
```
references/cache-layout.md
```
— v1.4 cache directory convention (per-MPN files,
```
_families/
```
reservation, staleness rules)

Entry-point scripts

```
scripts/datasheet_extract_cache.py
```
— v1.3 cache manager, resolver, indexer
```
scripts/datasheet_page_selector.py
```
— page selection heuristics (used by both v1.3 and v1.4 pipelines)
```
scripts/datasheet_score.py
```
— v1.3 extraction quality scoring
```
scripts/datasheet_verify.py
```
— cross-check extraction vs schematic usage (v1.3 + v1.4
```
verify_v14_extraction
```
mode)

scripts/datasheet_lookup.py

— v1.4 typed

lookup(mpn) → DatasheetFacts

facade with staleness detection

```
scripts/datasheet_features.py
```
— v1.3 consumer helper API (dual-reads v1.4 caches via
```
_derive_*_v14
```
translators)
```
scripts/plan_extraction.py
```
— v1.4 orchestration plan generator (Phase 3 extraction pipeline)
```
scripts/merge_results.py
```
— v1.4 per-task result validator + merger

datasheet_types/

— v1.4 typed access layer package (

DatasheetFacts

SpecValue

Pin

Pinout

lookup

best

trusted

has_data

)

Extraction workflow

Run

python3 skills/datasheets/scripts/plan_extraction.py <project>

to generate an orchestration plan, then

merge_results.py

to validate and merge per-task outputs. Full scout→plan→dispatch→merge procedure:

references/extraction-pipeline.md

Consuming extractions (v1.4 typed API)

The recommended consumer surface is the typed

lookup(mpn, cache_dir=...)

facade plus the trust-gating helpers from

datasheet_types

. Import like:

python

import sys, pathlib
sys.path.insert(0, str(pathlib.Path(__file__).parent.parent / "datasheets"))
from datasheet_types import lookup, has_data, best, trusted

# Returns Optional[DatasheetFacts]. None on cache miss / stale PDF / low quality.
facts = lookup("TPS61023DRLR", cache_dir=pathlib.Path("datasheets/extracted"))
if facts is None:
    return  # heuristic-only path; no datasheet evidence available

# Field-level trust gating — every SpecValue list runs through has_data() / best() / trusted().
pu_range = facts.base.recommended_pullup_range  # Optional[list[SpecValue]]
if has_data(pu_range):
    # Most-trusted single value (first SpecValue meeting threshold, preserves extractor order).
    rec = best(pu_range, min_confidence="medium")  # Optional[SpecValue]
    if rec is not None and rec.min is not None:
        ...  # use rec.min, rec.max, rec.typ, rec.unit, rec.evidence.{page,section,confidence}

# All SpecValues at threshold (for multi-value fields like absolute_max).
hi_conf = trusted(facts.base.absolute_max.get("VDD", []), min_confidence="high")

Defensive patterns (mirrors

kicad/SKILL.md

§ "Probing Analyzer JSON"):

```
lookup()
```
returns
```
None
```
on cache miss, stale PDF (PDF newer than extraction), or quality score below the configured floor. Always guard with
```
if facts is None: return
```
.
Category extensions are optional on
```
DatasheetFacts
```
.
```
facts.regulator
```
is
```
None
```
when the part isn't in the
```
regulator
```
category — check before dereferencing.
SpecValue lists can be
```
None
```
(field not extracted),
```
[]
```
(extracted but empty), or
```
list[SpecValue]
```
.
```
has_data()
```
collapses the first two to
```
False
```
; pair with
```
best()
```
/
```
trusted()
```
for confidence gating.
```
SpecValue.min
```
/
```
.max
```
/
```
.typ
```
are each
```
Optional[float]
```
. A SpecValue carrying only
```
typ
```
(no range) makes
```
>
```
/
```
<
```
comparisons against
```
.min
```
/
```
.max
```
raise
```
TypeError
```
— guard with explicit
```
is not None
```
chains on every numeric access.

confidence

is one of

"low"

"medium"

"high"

. Calling

best()

trusted()

with any other string raises

ValueError

v1.3 compat shim

Legacy detectors still call

get_regulator_features(mpn)

get_mcu_features(mpn)

get_pin_function(mpn, pin)

from

scripts/datasheet_features.py

. These dual-read v1.4 caches and translate to the v1.3 dict shape. Sunset planned for v1.6 — new code should use

lookup()

directly.

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.

datasheets

NPX Install

Tags

SKILL.md Content