rtl-model-consistency

rtl-model-consistency is a skill for Claude Code from babyworm/rtl-agent-team. It costs 42 tokens per session (1,920 once invoked), scanned A, original, MIT.

A verification check that compares a C reference model, a SystemC bus-functional model, and RTL using the same test inputs. It identifies which one differs when their results do not match.

In plain words
What is it for?
It helps validate hardware models after independent changes, check a shared baseline, and investigate mismatched simulation results.
Why use it?
Two-way comparisons can show a disagreement but not which model is wrong. The three-way comparison isolates the model that has drifted before regression testing.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the rtl-agent-team plugin — 47 skills, 99 agents, 6 hooks shipped together

Good fit It helps validate hardware models after independent changes, check a shared baseline, and investigate mismatched simulation results.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/babyworm/rtl-agent-team/rtl-model-consistency
Install

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.

Any agent
npx skills add babyworm/rtl-agent-team --skill rtl-model-consistency
Clone the repo
git clone --depth 1 https://github.com/babyworm/rtl-agent-team

Made for: Claude Code.

Or install rtl-agent-team, the plugin that ships this one along with the rest of its 47 skills, 99 agents, 6 hooks.

Wrote 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.

agentmods badge for rtl-model-consistency

README.md
[![agentmods](https://agentmods.dev/badge/skills/babyworm/rtl-agent-team/rtl-model-consistency.svg)](https://agentmods.dev/skills/babyworm/rtl-agent-team/rtl-model-consistency)
Your own site
<a href="https://agentmods.dev/skills/babyworm/rtl-agent-team/rtl-model-consistency"><img src="https://agentmods.dev/badge/skills/babyworm/rtl-agent-team/rtl-model-consistency.svg" alt="Measured on agentmods" height="20"></a>
Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,920 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00042 $0.01920
Opus 5 $0.00021 $0.00960
Sonnet 5 $0.00008 $0.00384
Haiku 4.5 $0.00004 $0.00192

Measured 5d ago against content hash d0cd19a6905c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

rtl-model-consistency 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 5d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/compare_3way.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/rtl-model-consistency/SKILL.md · 122 lines

How it starts

The opening of the file, as written. The whole thing — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.

<Use_When>

  • All three models exist (refc/, bfm/, rtl/) and need consistency validation.
  • Checking for drift between models after independent updates.
  • A pre-regression gate is needed to confirm baseline consistency.
  • A discrepancy between two models requires the third to arbitrate. </Use_When>

<Do_Not_Use_When>

  • Only two models exist → use rtl-p5s-func-verify (RTL vs ref) or rtl-p5s-perf-verify (RTL vs BFM).
  • Models are known to be out of sync — fix the diverging model first, then re-run.
  • Only functional RTL verification is needed → use rtl-p5s-func-verify. </Do_Not_Use_When>

<Why_This_Exists> When ref model, BFM, and RTL are developed independently, silent divergence accumulates. 3-way comparison isolates which model is wrong: ref == BFM != RTL means RTL has a bug; ref != BFM == RTL means the ref model diverged; ref == RTL != BFM means BFM has an issue. Two-model comparisons cannot make this determination. </Why_This_Exists>

Prerequisites

  • refc/build/ref_model built and executable.
  • bfm/build/bfm_smoke built and executable.
  • RTL files present under rtl/ with simulation infrastructure (iverilog or cocotb).
  • Test vectors at sim/consistency/test_vectors.bin (or will be generated — minimum 10 vectors).

If any model binary is missing: WARNING — run that model's build skill first; proceed with 2-way comparison and note the absent model in the report.

<Responsibility_Boundary>

  • Scripts (compare_3way.py) handle deterministic pairwise binary comparison and first-divergence extraction.
  • LLM handles diagnosis (which model is the likely source of error), report narrative, and escalation decisions.
  • Contract surface: all three models must run on identical input vectors; bitexact is the default criterion unless tolerance is documented. </Responsibility_Boundary>

Read the full file on GitHub · 122 lines

Changes

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.

  1. 5d ago First seen · 122 lines · 42 tokens per session scan A d0cd19a6905c

Subscribe to this mod's changes

rtl-model-consistency is a skill published in the GitHub repository babyworm/rtl-agent-team (51 stars, last pushed 15d ago), licensed MIT. It adds 42 tokens to every session and 1,920 once invoked, about $0.0002 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 2026-09-03.