eda plugins

by aklofas

KiCad electronics design review. Parse schematics and PCB layouts, run EMC pre-compliance and SPICE simulation, extract structured specs from datasheet PDFs, source components from major distributors, and prep boards for fabrication. Catches real design bugs before you order.

by Jananthan Paramsothy

Data-first ML discipline: forces coding agents through a strict 16-step pipeline — inspect and understand the raw data, define the prediction problem, clean, engineer, and split it BEFORE any model training — with explicit user-permission gates, marimo notebooks, and matplotlib visuals at every step.

by aklofas

KiCad electronics design review. Parse schematics and PCB layouts, run EMC pre-compliance and SPICE simulation, extract structured specs from datasheet PDFs, source components from major distributors, and prep boards for fabrication. Catches real design bugs before you order.

by VibeIC.AI

AI-Native IC Design plugin for Claude Code — one install bundles and auto-registers the MCP-EDA server that wraps the open-source EDA toolchain and lab hardware. Two entry points into one Phase 1: (A) natural-language prompt/dialogue, or (B) existing design docs → layered design JSON (L-docs). Phase 2: RTL → lint → sim → formal → FPGA SOF + on-board test. Phase 3: synth → STA → DFT → PnR → DRC/LVS → GDS → tapeout. Analog A1-A9 (sizing → layout → hardmacro) and Mixed-signal M1-M4 (merge → AMS co-sim → PV) interleaved per phase. Strict canonical-flow compliance gate plus an anti-fabrication doctrine enforced as deterministic gates (provenance audit-chain, chip-AGNOSTIC source guard, real-artifact + SHA256 attestation, gate self-assertion, benchmark-path consistency) — no fabrication, no hallucinated PASS.

by BeckhamLabs

Generate JLCPCB-ready KiCad PCB projects from descriptions, BOMs, or existing projects. Sources LCSC parts (basic-tier preferred), generates schematics, and packages routed boards into Gerber/drill/CPL/BOM zips ready to upload to JLCPCB.

by Matt Corbett

Exploratory data science & reproducible research team — agents (exploratory-data-scientist, feature-and-modeling-engineer, research-reproducibility-engineer) for the analysis layer between raw data and a defensible result: data profiling/cleaning, EDA, hypothesis generation, communicating findings with uncertainty; feature engineering, classical modeling (regression, trees, boosting), model selection, evaluation (cross-validation, metrics) and leakage avoidance; and the reproducibility spine — notebook hygiene, pinned environments, experiment tracking, data/version control, seeds, and reproducible pipelines. best-practices, decision-tree knowledge bank, skills, commands, templates, a hook, scenarios bank, and stdlib-only ds_calc.py (classification/regression metrics, split-check). Seams: is-the-result-statistically-real -> applied-statistics; productionize/serve/monitor -> ml-engineering; data pipelines/warehouse -> data-platform. Requires ravenclaude-core@>=0.7.0.