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.
npx skills add babyworm/rtl-agent-team --skill rtl-model-consistencygit clone --depth 1 https://github.com/babyworm/rtl-agent-teamWrote 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.
[](https://agentmods.dev/skills/babyworm/rtl-agent-team/rtl-model-consistency)<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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.
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) orrtl-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_modelbuilt and executable.bfm/build/bfm_smokebuilt 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>
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/outputs_consistent/bfm_output.hex 220 B
- examples/outputs_consistent/ref_output.hex 221 B
- examples/outputs_consistent/rtl_output.hex 217 B
- examples/outputs_rtl_drift/bfm_output.hex 220 B
- examples/outputs_rtl_drift/ref_output.hex 221 B
- examples/outputs_rtl_drift/rtl_output.hex 229 B
- examples/README.md 1.9 KB
- examples/vectors/test_vectors.hex 221 B
- references/.gitkeep 0 B
- references/model-consistency-conventions.md 3.8 KB
- scripts/compare_3way.py 4.0 KB runs code
- templates/consistency-report.md 1021 B
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.
- 5d ago First seen · 122 lines · 42 tokens per session scan A d0cd19a6905c
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.
Other skills, from other repositories
tuyaopen/usr-board
A workflow for adding a custom peripheral to a TuyaOpen project without changing the software development kit. A peripheral is an attached hardware device, and a TDD is a hardware driver interface used to control it.
vtestgen
Scaffold a Makefile-driven, CPU-bus-controlled testbench project for an IP and generate a comprehensive set of self-checking testcases, validate each with make, and write a testcase list.
vtestrun
Run all IP-level testcases from tclist.md through the bench Makefile, capture results, and write per-failure issue reports with no fix suggestions.
agent-optimization
Improve an Agent State through versioned scores and score-linked Traces from a frozen Benchmark.
holohub-app-lifecycle
Use for non-failing HoloHub app work with ./holohub: scaffold, build, run, test, visual evidence, lint, and flow benchmarking.
verification-strategy
Thorough verification of completed work before declaring done.