Analog Behavioral

Community

Arcadia-1

analog-behavioral

Architect Phase 2: build Verilog-A behavioral models and validate system architecture. Use after analog-decompose to verify top-level specs are achievable before transistor design.

Overview

Publisher	Arcadia-1
Repository	`analog-agents`
Skill name	`analog-behavioral`
Stars	84
Forks	19
Bundled files	Instructions only
Links	Skill source Repository Homepage

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/Arcadia-1/analog-agents/tree/main/skills/analog-behavioral
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.

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-behavioral .claude/skills/analog-behavioral

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analog Behavioral 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 Behavioral 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 Behavioral 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-behavioral

Architect Phase 2 -- behavioral validation. After /analog-decompose produces the architecture and sub-block specs, this skill builds Verilog-A behavioral models for each sub-block, runs system-level simulation, and confirms the top-level specs are achievable before committing to transistor-level design.

Effort Gating

Read effort level per shared-references/effort-contract.md. Print at startup:


[effort: <level>] behavioral=<mode>

Effort	Behavioral validation
lite	Skip entirely -- return immediately with "skipped by effort level"
standard	Quick check: key specs only (e.g., gain, bandwidth for amplifier; ENOB for ADC)
intensive	Full: all top-level specs verified at behavioral level
exhaustive	Full + sensitivity sweep (vary each sub-block parameter +/-10%, report spec degradation)

Inputs

Artifact	Source
`architecture.md`	`/analog-decompose` output
`blocks/*/spec.yml`	`/analog-decompose` output (one per sub-block)
Top-level `spec.yml`	Project root

All inputs are required. Do not proceed if architecture.md or any sub-block spec.yml is missing.

Outputs

Artifact	Location
Verilog-A behavioral model per sub-block	`blocks/<name>/behavioral.va`
System-level testbench	`testbench_system.scs` (or equivalent)
Behavioral simulation results	Documented in console output with quantified margins

Procedure

1. Build Verilog-A Behavioral Models

For each sub-block defined in architecture.md:

Read blocks/<name>/spec.yml to understand the sub-block's interface and specs.
Use the veriloga skill to write blocks/<name>/behavioral.va.
The model must capture key non-idealities relevant to system performance:
- Amplifier/OTA: finite gain, bandwidth, slew rate, output swing limits
- Comparator: offset, decision delay, metastability region
- DAC: INL/DNL, settling time, glitch energy
- ADC sub-blocks: quantization, sampling jitter
- Oscillator: phase noise, frequency pulling
Use the evas-sim skill to verify each behavioral model individually against its sub-block spec before system integration.

2. System-Level Behavioral Simulation

Create a system-level testbench that instantiates all behavioral models together according to the connectivity in architecture.md.
Use the evas-sim skill to run system-level simulation:
- For ADC: FFT-based ENOB/SNDR measurement
- For PLL: lock time, phase noise, loop stability
- For amplifier chains: end-to-end gain, bandwidth, noise
- For data converters: full signal-chain INL/DNL
Compare results against top-level spec.yml targets.
Document results with measured value, target, and margin for every spec.

3. Sensitivity Sweep (exhaustive only)

At exhaustive effort, sweep each sub-block's key parameters +/-10% and report:

Which top-level specs degrade and by how much
Which sub-blocks are the tightest bottleneck
Recommended margin allocation adjustments for budget.md

Gate: Behavioral Validation Must Pass

If behavioral simulation fails any top-level spec:

Identify which sub-block(s) or budget allocation caused the failure.
Return to /analog-decompose to revise the architecture or budget.
Do NOT proceed to transistor-level design with a broken architecture.

This gate prevents wasting design effort on an architecture that cannot meet spec.

Handoff Acceptance Criteria

Phase 2 is complete when (from prompts/architect-prompt.md):

Every sub-block has blocks/<name>/behavioral.va that passes individual test
System-level behavioral simulation meets top-level specs
Results documented with quantified margins

Reference

See prompts/architect-prompt.md Phase 2 section for the full architect prompt covering behavioral model requirements and system-level testbench construction.

Frequently asked questions

What does the Analog Behavioral AI skill do?

Architect Phase 2: build Verilog-A behavioral models and validate system architecture. Use after analog-decompose to verify top-level specs are achievable before transistor design.

Why use Analog Behavioral on TypingMind?

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

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

Which AI models can use Analog Behavioral?

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 Behavioral?

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

Is the Analog Behavioral 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

analog-behavioral

Effort Gating

Inputs

Outputs

Procedure

1. Build Verilog-A Behavioral Models

2. System-Level Behavioral Simulation

3. Sensitivity Sweep (exhaustive only)

Gate: Behavioral Validation Must Pass

Handoff Acceptance Criteria

Reference

Frequently asked questions

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