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Analog Explore

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Arcadia-1
analog-explore

Explore analog design space without simulation. Compare topologies, sweep parameters with hand calculations, find theoretical limits and Pareto tradeoffs. Use for architecture selection, initial sizing, or understanding design space before committing to EDA time. TRIGGER on: "compare topologies", "explore", "design space", "what are my options", "tradeoff analysis", "Pareto".

Overview

PublisherArcadia-1
Repositoryanalog-agents
Skill nameanalog-explore
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 Explore 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-explore
  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-explore .claude/skills/analog-explore
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analog Explore 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 Explore 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 Explore 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-explore

Design space exploration through hand calculations. No EDA required. Rapidly evaluate architectures, sweep parameters, and map tradeoff surfaces before committing simulation time.

When to Use

  • Choosing between architectures before detailed design
  • Understanding theoretical limits of a topology
  • Quick parameter sensitivity analysis
  • Generating initial sizing for multiple candidates
  • Estimating feasibility of a spec before starting design

Capabilities

Architecture Comparison

Compare 2-4 candidate topologies for a given spec:

/analog-explore compare --spec spec.yml --topologies "folded-cascode, telescopic, two-stage-miller"

For each topology, estimate from hand calculations:

  • Achievable gain (intrinsic gain × number of gain stages)
  • Bandwidth (gm/CL for single-stage, GBW for multi-stage)
  • Power (minimum current for gm requirement × VDD)
  • Output swing (VDD minus Vdsat stack)
  • Noise (input-referred: 8kT/(3gm) × noise factor)
  • Area (rough: proportional to total W×L)

Output: comparison table + recommendation with reasoning.

markdown
## Architecture Comparison — OTA for 60dB gain, 500MHz UGBW

| Metric | Folded Cascode | Telescopic | Two-Stage Miller |
|--------|---------------|------------|-----------------|
| Gain   | ~55-70 dB     | ~60-80 dB  | ~70-90 dB       |
| UGBW   | gm1/CL        | gm1/CL     | gm1/Cc          |
| Power  | 2× (folded)   | 1× (base)  | 2× (two stages) |
| Swing  | VDD - 4Vdsat  | VDD - 4Vdsat| VDD - 2Vdsat   |
| Noise  | moderate      | best       | moderate         |
| Risk   | CMFB needed   | limited Vcm| compensation     |

Recommendation: Folded cascode. 60dB is achievable in single stage with cascode,
avoids compensation complexity of two-stage, and handles flexible Vcm.
Telescopic has better noise but Vcm range is too restrictive for this application.

Parameter Sweep (Hand-Calc)

Sweep a design parameter and estimate its effect on all specs:

/analog-explore sweep --param "W_input:1u,2u,4u,8u,16u" --spec spec.yml --topology folded-cascode

For each value, calculate: gm, gm/Id, gain contribution, bandwidth impact, noise contribution, power, headroom.

Output: sweep table showing tradeoff surface.

markdown
## Parameter Sweep — W_input (folded cascode, Id_tail=200uA)

| W_input | gm/Id | gm (mS) | Gain (dB) | UGBW (MHz) | Noise (nV/rtHz) | Headroom |
|---------|-------|---------|-----------|------------|-----------------|----------|
| 1u      | 8     | 0.8     | 48        | 127        | 5.6             | OK       |
| 2u      | 11    | 1.1     | 52        | 175        | 4.8             | OK       |
| 4u      | 15    | 1.5     | 55        | 239        | 4.1             | OK       |
| 8u      | 19    | 1.9     | 57        | 302        | 3.6             | tight    |
| 16u     | 23    | 2.3     | 58        | 366        | 3.3             | FAIL     |

Observation: W=4u-8u is the sweet spot. Beyond 8u, gm/Id enters weak inversion
(>20), headroom collapses, and gain improvement diminishes.

Feasibility Check

Quick spec feasibility without full design:

/analog-explore feasibility --spec spec.yml

For each spec, estimate the minimum resource (current, area, bandwidth) needed:

  • Gain: how many gain stages? What intrinsic gain per stage?
  • Bandwidth: minimum gm for target UGBW? Minimum current for that gm?
  • Noise: minimum gm (and thus current) for noise floor?
  • Power: sum of minimum currents × VDD
  • Settling: minimum UGBW for settling spec?

Flag impossible specs: "60dB gain AND 2GHz UGBW in 180nm with 0.5mW — NOT FEASIBLE. Gain requires cascode (limits speed), UGBW requires high gm (requires high current)."

Output: feasibility report with go/no-go per spec and minimum resource estimates.

Sensitivity Analysis

Which parameter has the biggest impact on which spec?

/analog-explore sensitivity --netlist circuit/ota.scs --spec spec.yml

Perturb each .param by ±20%, re-derive all specs from hand calculations. Output: sensitivity matrix.

markdown
| Param  | dc_gain | ugbw  | phase_margin | noise | power |
|--------|---------|-------|-------------|-------|-------|
| W1     | +0.3    | +0.5  | -0.1        | -0.4  | 0     |
| L1     | +0.8    | -0.6  | +0.2        | +0.1  | 0     |
| Ibias  | +0.1    | +0.5  | +0.3        | -0.5  | +1.0  |
| Cc     | 0       | -0.3  | +0.9        | 0     | 0     |

(Normalized elasticity: +1.0 means +20% param → +20% spec)

Wiki Interaction

  • Calls /analog-wiki consult for topology reference data
  • Writes strategy entries when a sweep reveals non-obvious tradeoffs
  • Writes corner-lesson entries when feasibility check reveals process-dependent limits

Effort Interaction

Not effort-gated. Exploration is always available.

Output

All outputs go to explore/ directory:

  • explore/comparison-<date>.md — architecture comparison
  • explore/sweep-<param>-<date>.md — parameter sweep
  • explore/feasibility-<date>.md — spec feasibility report
  • explore/sensitivity-<date>.md — sensitivity matrix

Frequently asked questions

What does the Analog Explore AI skill do?

Explore analog design space without simulation. Compare topologies, sweep parameters with hand calculations, find theoretical limits and Pareto tradeoffs. Use for architecture selection, initial sizing, or understanding design space before committing to EDA time. TRIGGER on: "compare topologies", "explore", "design space", "what are my options", "tradeoff analysis", "Pareto".

Why use Analog Explore on TypingMind?

Because you install it once and use it with any model. Analog Explore 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 Explore in TypingMind?

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

Which AI models can use Analog Explore?

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 Explore?

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

Is the Analog Explore 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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