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Analog Audit

Community
Arcadia-1
analog-audit

Audit analog circuit netlists for correctness, quality, and risks. Supports both pre-layout (schematic) and post-layout (extracted) netlists. Post-layout mode filters massive parasitic netlists before auditing. Works without EDA. TRIGGER on: "audit", "review netlist", "check this circuit", "design review", "pre-layout", "post-layout", "post-sim", "extracted netlist".

Overview

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

Use it in TypingMind

Enable Analog Audit 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 Audit 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 Audit 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-audit

Static audit service for analog circuit netlists. Supports two modes:

  • Pre-layout audit — clean schematic-level netlist (hundreds to thousands of lines)
  • Post-layout audit — extracted netlist with parasitics (tens of thousands to millions of lines)

No EDA required for either mode.

Detecting Audit Type

Auto-detect from netlist characteristics:

  • Lines > 50,000 OR parasitic R+C count > 10x active device count → post-layout
  • Otherwise → pre-layout

User can force: /analog-audit pre <netlist> or /analog-audit post <netlist>

Pre-Layout Audit

For schematic-level netlists. This is the standard audit flow.

Process

Input: <block>.scs (schematic netlist)
    |
    v
  1. Topology identification
     Parse structure: diff pairs, mirrors, cascodes, CMFB, etc.
     Classify topology.
    |
    v
  2. Checklist execution
     Auto-detect applicable checklists from topology.
     Run all checks per effort level.
    |
    v
  3. Hand-calculation audit
     Extract .param values, re-derive specs from first principles.
     Compare against rationale.md and spec.yml if available.
    |
    v
  4. Anti-pattern scan
     Match topology against wiki/anti-patterns/.
    |
    v
  5. Cross-model review (effort >= standard)
     Send to configured reviewers per review-protocol.md.
    |
    v
  6. Pre-layout audit report
     verifier-reports/audit-pre-layout.md

Pre-Layout Report Format

markdown
# Pre-Layout Audit — <netlist> — <date>

## Circuit Summary
- **Topology**: folded-cascode fully-differential OTA
- **Devices**: 14 MOSFETs, 2 resistors, 1 capacitor
- **Supply**: 0.9V
- **Estimated power**: 324uW

## Checklist Results
| Check | Severity | Result | Detail |
|-------|----------|--------|--------|
| ...   | ...      | ...    | ...    |

## Hand-Calculation Audit
| Spec | Estimated | Target | Status | Confidence |
|------|-----------|--------|--------|------------|
| ...  | ...       | ...    | ...    | ...        |

## Anti-Pattern Scan
- [anti-001] Diode-Load CMFB: NOT affected
- [anti-002] Vcm=VDD/2: NOT affected

## Cross-Model Review
(divergence analysis from /analog-review)

## Risk Summary
1. [CRITICAL] ...
2. [WARNING] ...

## Recommendations
...

Post-Layout Audit

For extracted netlists with parasitics. These netlists are typically 100K-10M+ lines. They CANNOT be audited directly — they must be filtered first.

Process

Input: <block>_extracted.scs (post-layout netlist, possibly millions of lines)
    |
    v
  1. Statistics scan
     Run: python3 tools/postlayout_filter.py stats <netlist>
     Report: total lines, device counts, parasitic R/C counts.
     Confirm this is indeed a post-layout netlist.
    |
    v
  2. Skeleton extraction
     Run: python3 tools/postlayout_filter.py extract <netlist> --threshold <cap_thresh>
     Produces: <block>_filtered.scs — active devices + significant parasitics only.
     Typically reduces 500K lines to 2-5K lines.
    |
    v
  3. Pre-vs-post comparison (if pre-layout netlist available)
     Run: python3 tools/postlayout_filter.py compare <pre>.scs <post>.scs
     Produces: device count diff, top parasitic caps, impact assessment.
    |
    v
  4. Filtered netlist audit
     Run standard audit flow (checklist + hand-calc) on the FILTERED netlist.
     Active device sizing should match pre-layout (if not, flag).
    |
    v
  5. Parasitic impact assessment
     Analyze significant parasitics:
     - Which high-impedance nodes have large parasitic caps?
       (These directly reduce gain and bandwidth)
     - Which signal paths have significant parasitic R?
       (These add noise and IR drop)
     - Are there unexpected coupling caps between sensitive nodes?
       (e.g., clock-to-analog coupling, input-to-output coupling)
     - Total added capacitance at each critical node vs original CL.
    |
    v
  6. Post-layout audit report
     verifier-reports/audit-post-layout.md

Filtering Parameters

Default thresholds (adjustable):

  • Capacitor threshold: 1fF — keep caps >= 1fF, discard smaller
  • Resistor threshold: 1Mohm — keep R <= 1M (low-R paths matter for IR drop)

For advanced filtering:

  • --threshold 10f — stricter, only keep significant caps (faster audit, less detail)
  • --threshold 0.1f — looser, keep more parasitics (slower but more thorough)

Post-Layout Report Format

markdown
# Post-Layout Audit — <netlist> — <date>

## Netlist Statistics
- **Original**: 523,847 lines
- **Filtered**: 3,241 lines (0.6% of original)
- **Active devices**: 14 MOSFETs (matches pre-layout)
- **Parasitic caps**: 187,432 total, 847 significant (>= 1fF)
- **Parasitic resistors**: 203,561 total, 1,204 significant (<= 1Mohm)
- **Total parasitic capacitance**: 2.3 pF

## Pre-Layout vs Post-Layout Comparison
(if pre-layout netlist provided)

| Type | Pre-Layout | Post-Layout | Delta |
|------|-----------|-------------|-------|
| MOSFET | 14 | 14 | 0 |
| Resistor | 2 | 203,563 | +203,561 parasitic |
| Capacitor | 1 | 187,433 | +187,432 parasitic |

## Top Parasitic Capacitors
| Rank | Nodes | Value | Impact |
|------|-------|-------|--------|
| 1 | VOUTP — VSS | 340 fF | Adds to output load, reduces UGBW |
| 2 | VOUTN — VSS | 335 fF | Symmetric, expected |
| 3 | net_fold — VSS | 120 fF | On high-Z fold node, reduces gain |
| ...

## Parasitic Impact on Specs
| Spec | Pre-Layout Est. | Post-Layout Est. | Degradation | Severity |
|------|----------------|------------------|-------------|----------|
| UGBW | 500 MHz | ~380 MHz | -24% | WARNING |
| DC gain | 62 dB | ~58 dB | -4 dB | WARNING |
| Phase margin | 65 deg | ~60 deg | -5 deg | OK (still meeting) |
| Settling | 2 ns | ~3.2 ns | +60% | CRITICAL |

## Coupling Analysis
- VINP — VOUTP: 2.3 fF coupling cap (may affect PSRR)
- CLK — net_analog: 0.8 fF (monitor for clock feedthrough)

## Active Device Verification
All 14 MOSFETs match pre-layout sizing (W, L, nf, multi).
No unexpected devices added or removed.

## Checklist Results (on filtered netlist)
(same format as pre-layout)

## Risk Summary
1. [CRITICAL] Settling time degraded 60% — parasitic cap at output nodes
2. [WARNING] UGBW reduced 24% — may fail spec at SS corner
3. [INFO] 2.3fF input-output coupling — verify PSRR impact

## Recommendations
1. Add shielding between VINP and VOUTP routing
2. Minimize routing over fold nodes (net_fold has 120fF parasitic)
3. Re-simulate post-layout to confirm margin estimates

Subcommands

CommandDescription
/analog-audit <netlist>Auto-detect pre/post layout, run appropriate audit
/analog-audit pre <netlist>Force pre-layout audit
/analog-audit post <netlist>Force post-layout audit
/analog-audit post <netlist> --pre <pre_netlist>Post-layout audit with pre-layout comparison
/analog-audit stats <netlist>Quick statistics only (no full audit)

Effort Interaction

EffortPre-LayoutPost-Layout
liteChecklist (structural) + topology IDStats + skeleton extract only
standardFull checklist + hand-calcStats + extract + filtered audit
intensive+ 2-model cross-review+ pre-vs-post comparison + parasitic impact
exhaustive+ full cross-review + wiki scan+ coupling analysis + all cross-review

Standalone Usage

Fully standalone. Does not require analog-pipeline or prior design activity.

Use the analog-audit skill to review circuit/ota.scs
Use the analog-audit skill to audit post-layout extracted_ota.scs --pre circuit/ota.scs

Frequently asked questions

What does the Analog Audit AI skill do?

Audit analog circuit netlists for correctness, quality, and risks. Supports both pre-layout (schematic) and post-layout (extracted) netlists. Post-layout mode filters massive parasitic netlists before auditing. Works without EDA. TRIGGER on: "audit", "review netlist", "check this circuit", "design review", "pre-layout", "post-layout", "post-sim", "extracted netlist".

Why use Analog Audit on TypingMind?

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

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

Which AI models can use Analog Audit?

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 Audit?

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

Is the Analog Audit 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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