datasheets

datasheets is a skill for Claude Code from aklofas/kicad-happy. It costs 156 tokens per session (2,464 once invoked), scanned A, original, MIT.

A guide for extracting structured technical information from electronic component datasheet PDFs, such as pin connections, electrical limits, peripherals, and package details.

In plain words
What is it for?
Use it to extract or verify a specific component part number and supply information to schematic, circuit simulation, electromagnetic-compatibility, bill-of-materials, or thermal analysis.
Why use it?
It turns hard-to-search PDF specifications into data that other electronics analysis tools can use when checking designs.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the kicad-happy plugin — 11 skills shipped together

Good fit Use it to extract or verify a specific component part number and supply information to schematic, circuit simulation, electromagnetic-compatibility, bill-of-materials, or thermal analysis.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/aklofas/kicad-happy/datasheets
About the project

kicad-happy is a collection of coding-agent skills and tools that inspect KiCad schematics, PCB layouts, and Gerber files for electronics design issues. It is for hardware designers who want automated reviews, simulations, datasheet checks, component sourcing, and fabrication preparation. The catalogue entries are its agent skills, rules, plugin, and instruction for integrating this workflow with coding agents.

aklofas/kicad-happy · 1,166 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add aklofas/kicad-happy --skill datasheets
Clone the repo
git clone --depth 1 https://github.com/aklofas/kicad-happy

Made for: Claude Code.

Or install kicad-happy, the plugin that ships this one along with the rest of its 11 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for datasheets

README.md
[![agentmods](https://agentmods.dev/badge/skills/aklofas/kicad-happy/datasheets/github.svg)](https://agentmods.dev/skills/aklofas/kicad-happy/datasheets)
Your own site
<a href="https://agentmods.dev/skills/aklofas/kicad-happy/datasheets"><img src="https://agentmods.dev/badge/skills/aklofas/kicad-happy/datasheets/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for datasheets

Your own site · 80×15
<a href="https://agentmods.dev/skills/aklofas/kicad-happy/datasheets"><img src="https://agentmods.dev/badge/skills/aklofas/kicad-happy/datasheets.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 156 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,464 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 16 Apr 2026
  • Snyk warn 16 Apr 2026
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium analysis-evasion · line 1
    Suspicious Unicode normalization or mixed-script content
    Fix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00156 $0.02464
Opus 5 $0.00078 $0.01232
Sonnet 5 $0.00031 $0.00493
Haiku 4.5 $0.00016 $0.00246

Measured 10d ago against content hash 84fb369c6ff1, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

datasheets scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

The scan reads SKILL.md. This mod also ships 19 executable files (datasheet_types/__init__.py, datasheet_types/base_block.py, datasheet_types/codec.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/datasheets/SKILL.md · 120 lines

How it starts

The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Datasheets Skill

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 APIsscripts/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.
  • Verificationdatasheet_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).

Read the full file on GitHub · 120 lines

Files

What ships with it

59 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 120 lines · 156 tokens per session scan A 84fb369c6ff1

Subscribe to this mod's changes

datasheets is a skill published in the GitHub repository aklofas/kicad-happy (1,166 stars, last pushed 8d ago), licensed MIT. It adds 156 tokens to every session and 2,464 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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