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Analog Learn

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Arcadia-1
analog-learn

Interactive analog design learning companion. Explains circuit design decisions step by step with underlying physics. Use when learning analog design, studying a topology, or wanting detailed explanations of design tradeoffs. TRIGGER on: "teach me", "explain", "why does", "how does", "learn", "tutorial", "walk me through", or any educational analog design question.

Overview

PublisherArcadia-1
Repositoryanalog-agents
Skill nameanalog-learn
Stars
84
Forks
19
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Arcadia-1 on GitHub. Read the source before you install it.

Installation

Install the Analog Learn AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Arcadia-1/analog-agents/tree/main/skills/analog-learn
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Arcadia-1/analog-agents.git /tmp/analog-agents
mkdir -p .claude/skills
cp -r /tmp/analog-agents/skills/analog-learn .claude/skills/analog-learn
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analog Learn in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Analog Learn on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Analog Learn is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

analog-learn

Interactive teaching companion for analog circuit design. Works entirely without EDA tools — all explanations use hand calculations and physical reasoning.

When to Use

  • Learning a new topology ("teach me folded cascode")
  • Understanding a design decision ("why gm/Id = 15 for input pair?")
  • Walking through a complete design from spec to netlist
  • Studying an existing netlist to understand how it works

Modes

Guided Design Walkthrough

When the user says "teach me how to design a ":

  1. Start from the spec — explain what each spec means physically

    • "DC gain >= 60dB means the amplifier reduces error by 1000x in feedback"
    • "Phase margin >= 60 deg means the loop won't oscillate or ring excessively"
  2. Architecture selection — explain WHY, not just WHAT

    • Compare 2-3 candidates with physical intuition, not just spec tables
    • "Folded cascode gives high gain in one stage because it stacks two high-impedance nodes. Telescopic is faster but can't handle rail-to-rail input."
  3. Sizing step by step — show the physics behind every number

    • For each transistor: what spec does it serve? what equation sets its size?
    • "M1 input pair: we need gm = 1mS for the bandwidth spec. gm = 2Id/Vov. If we pick Vov = 200mV (moderate inversion), Id = gmVov/2 = 100uA per side."
    • Draw the headroom stack: "From VDD to VSS, we need to fit: Vds_tail + Vsg_input + Vds_cascode_n + Vds_cascode_p + Vds_load. That's 5 transistors. At 100mV each, we need 500mV minimum. With VDD=0.9V, we have 400mV of swing. Tight."
  4. Common mistakes — teach through anti-patterns from wiki

    • Pull relevant wiki/anti-patterns/ entries
    • "A classic mistake: setting Vcm = VDD/2 with PMOS input. Let me show you why that kills the tail headroom..."
  5. Produce a design notebook — learn/design-notebook.md

    • Not just a netlist, but a complete learning artifact
    • Every equation, every decision, every "what if we changed this"
    • Exercises: "What happens if we double the tail current? Calculate the new gain."

Topology Explainer

When the user says "explain " or asks about a specific circuit:

  1. Read the netlist (if provided) or describe the topology from wiki
  2. Trace signal path: input → gain stages → output
  3. Explain each transistor's role in plain language
  4. Show small-signal equivalent circuit (text description)
  5. Derive key specs from first principles
  6. List the critical design knobs and what they affect

Study Existing Netlist

When the user provides a .scs netlist and asks to understand it:

  1. Parse the netlist structure (subcircuits, instances, connections)
  2. Identify the topology (diff pair, cascode, mirror patterns)
  3. Annotate each device: "M1 is the input NMOS, gate connected to VINP"
  4. Estimate operating point from sizing: "W=10u, L=200n at Id=100uA → gm/Id ≈ 15"
  5. Estimate key specs from hand calculations
  6. Flag potential issues (same as checklist, but explained pedagogically)

Wiki Interaction

  • Pull wiki/topologies/ entries for reference designs
  • Pull wiki/anti-patterns/ for "common mistakes" teaching
  • Pull wiki/strategies/ for methodology explanations
  • On completion: suggest adding new insights to wiki

Output Format

All outputs go to learn/ directory:

  • learn/design-notebook.md — step-by-step design walkthrough
  • learn/topology-explainer.md — topology analysis
  • learn/netlist-study.md — existing netlist annotation

Effort Interaction

Not effort-gated. Learning is always available at full depth.

Tone

  • Patient, thorough, builds intuition before equations
  • Uses physical analogies: "A current mirror is like a photocopier for current"
  • Shows the calculation, then explains what it means
  • Anticipates confusion points: "You might wonder why we use PMOS for the input pair when NMOS has higher mobility. The reason is..."
  • Never skips steps. If a student needs to see 2*Id/Vov, write it out.

Frequently asked questions

What does the Analog Learn AI skill do?

Interactive analog design learning companion. Explains circuit design decisions step by step with underlying physics. Use when learning analog design, studying a topology, or wanting detailed explanations of design tradeoffs. TRIGGER on: "teach me", "explain", "why does", "how does", "learn", "tutorial", "walk me through", or any educational analog design question.

Why use Analog Learn on TypingMind?

Because you install it once and use it with any model. Analog Learn is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Analog Learn in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Arcadia-1/analog-agents/tree/main/skills/analog-learn. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Analog Learn?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Analog Learn?

As many as you like. As long as a model supports skills, you can use Analog Learn with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Analog Learn AI skill free?

It is published on GitHub by Arcadia-1. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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