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.
npx skills add hdl-tools/analog-chip-design-agents --skill em-modelinggit clone --depth 1 https://github.com/hdl-tools/analog-chip-design-agentsWrote 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.
[](https://agentmods.dev/skills/hdl-tools/analog-chip-design-agents/em-modeling)<a href="https://agentmods.dev/skills/hdl-tools/analog-chip-design-agents/em-modeling"><img src="https://agentmods.dev/badge/skills/hdl-tools/analog-chip-design-agents/em-modeling/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.
<a href="https://agentmods.dev/skills/hdl-tools/analog-chip-design-agents/em-modeling"><img src="https://agentmods.dev/badge/skills/hdl-tools/analog-chip-design-agents/em-modeling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00119 | $0.03211 |
| Opus 5 | $0.00060 | $0.01605 |
| Sonnet 5 | $0.00024 | $0.00642 |
| Haiku 4.5 | $0.00012 | $0.00321 |
Grade A, and why
em-modeling 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.
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.
How it starts
The opening of the file, as written. The whole thing — 298 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: EM Modeling
Invocation
- If invoked by a user presenting an EM-modeling task: immediately spawn the
analog-chip-design-agents:em-modeling-orchestratoragent and pass the full user request and any available context. Do not execute stages directly. - If invoked by the
em-modeling-orchestratormid-flow (including fix_request re-solves): do not spawn a new agent. Treat this file as read-only — return the requested stage rules, sign-off criteria, or loop-back guidance.
Spawning the orchestrator from within an active orchestrator run causes recursive delegation and must never happen.
Pre-run Context
Before executing or advising on any stage, read the following if they exist:
memory/em/knowledge.md— known meshing recipes, passivity/fit fixes, de-embedding patterns, solver-selection rules, and PDK/tool quirks. Incorporate its guidance into every stage.memory/em/run_state.md— current run identity for resume-after-interruption.
Purpose
Solve the electromagnetics of an on-chip passive or antenna, extract a converged, passive
S-parameter model, and fit a lumped equivalent for circuit-level RF simulation. Seven stages with
explicit QoR gates. EM modeling is a data-dependency producer and a cross-domain servicer: it
writes a Touchstone S-parameter model + fitted lumped model into design_state.em that rf-design
reads as a passive input. Its loop-backs are stage-local (passivity/fit fail → meshing /
geometry_definition, max 2×); a fundamental geometry/stack-up gap escalates to the user. EM
modeling does not open fix_requests, but it services rf-design-raised ones
(route_to: em-modeling): when dispatched with a fix_request.id it re-solves the passive toward a
higher-Q / higher-SRF target and closes the entry with a circuit_response so the
pipeline-orchestrator re-validates RF.
Supported EDA Tools
Open-Source
- openEMS (
openEMS) — FDTD full-wave solver (distributed passives, antennas, mmWave) - FastHenry (
fasthenry) / FastCap (fastcap) — quasi-static RL / C field solvers - gmsh (
gmsh) — mesh generation - scikit-rf (Python
skrf) — passivity / causality checks, fitting, de-embedding
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.
- 10d ago First seen · 298 lines · 119 tokens per session scan A e80284b477bd
em-modeling is a skill published in the GitHub repository hdl-tools/analog-chip-design-agents (22 stars, last pushed 3mo ago), licensed MIT. It adds 119 tokens to every session and 3,211 once invoked, about $0.0006 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.
Other skills, from other repositories
neuroskill-bci
Use live BCI cognitive and mood state from NeuroSkill.
ruview-advanced-sensing
Advanced RuView capabilities — RuvSense multistatic sensing (attention-weighted fusion, geometric diversity, persistent field model), cross-viewpoint fusion across multiple nodes, RF tomography (ISTA L1 solver, voxel grids), longitudinal biomechanics drift, pre-movement intention signals, adversarial signal detection…
ruview-applications
Run RuView sensing applications — presence/occupancy, breathing & heart rate, activity & fall detection, 17-keypoint pose estimation (WiFlow), sleep monitoring & apnea screening, environment mapping, Mass Casualty Assessment (MAT), and the 3D point-cloud fusion demo. Use when someone wants to actually do something…
lab-hardware-cad
Design custom laboratory hardware as parametric build123d models and export fabrication-ready STEP, STL, and DXF files - microfluidic chips and molds, optomechanical mounts and breadboard adapters, cuvette and microplate holders, tube racks, animal-behavior rigs, and 3D-printed instrument fixtures. Use when a research…
opentrons-integration
Author, review, migrate, simulate, and troubleshoot official Opentrons Python Protocol API v2 protocols for Flex and OT-2 robots. Use for robot-specific liquid handling, deck and labware setup, pipettes, modules, runtime parameters, liquid classes, and Opentrons App analysis. Use pylabrobot instead when one workflow…
pylabrobot
Develop and review PyLabRobot lab-automation resources, liquid-handling plans, offline simulations, and supported-device integrations. Use for PyLabRobot protocols or API questions; keep physical execution behind an explicit operator safety gate.