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rtl-acquire

rtl-acquire is a skill for Claude Code from ShenShan123/r2g-skills. It costs 107 tokens per session (3,522 once invoked), scanned A, original, MIT.

A workflow for finding RTL, meaning hardware design source code, and turning it into verified netlist graphs, which represent hardware connections. It handles discovery, synthesis, repair, validation, deduplication, quality scoring, and publishing.

In plain words
What is it for?
Use it to grow a certified corpus of hardware designs for graph-based research or training, before handing promising designs to place-and-route and signoff workflows.
Why use it?
It organizes large-scale collection of usable training data while filtering out duplicates and incomplete or low-quality designs.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python3 scripts/run_expansion_round.py \.

Part of the r2g-skills plugin — 4 skills shipped together

not rated 42repo today A scan SkillSpector: pass 107 tokens original MIT

Good fit Use it to grow a certified corpus of hardware designs for graph-based research or training, before handing promising designs to place-and-route and signoff workflows.

Compare 6 skills from other repositories ↓
View source ↗ ShenShan123/r2g-skills
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/ShenShan123/r2g-skills
agentmods
npx agentmods add skills/shenshan123/r2g-skills/rtl-acquire

Made for: Claude Code.

Or install r2g-skills, the plugin that ships this one along with the rest of its 4 skills.

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-acquire

README.md
[![agentmods](https://agentmods.dev/badge/skills/shenshan123/r2g-skills/rtl-acquire/github.svg)](https://agentmods.dev/skills/shenshan123/r2g-skills/rtl-acquire)
Your own site
<a href="https://agentmods.dev/skills/shenshan123/r2g-skills/rtl-acquire"><img src="https://agentmods.dev/badge/skills/shenshan123/r2g-skills/rtl-acquire/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for rtl-acquire

Your own site · 80×15
<a href="https://agentmods.dev/skills/shenshan123/r2g-skills/rtl-acquire"><img src="https://agentmods.dev/badge/skills/shenshan123/r2g-skills/rtl-acquire.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 107 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,522 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 $0.00107 $0.03522
Opus 5 $0.00053 $0.01761
Sonnet 5 $0.00021 $0.00704
Haiku 4.5 $0.00011 $0.00352

Measured 12d ago against content hash 9f52c3de21d6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

rtl-acquire 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 12d ago.

The scan reads SKILL.md. This mod also ships 27 executable files (scripts/acquire/__init__.py, scripts/acquire/build_external_synth_variant_candidates.py, scripts/acquire/clone_repo_manifest.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.

r2g-skills/rtl-acquire/SKILL.md · 271 lines

How it starts

The opening of the file, as written. The whole thing — 271 lines — stays where its author put it; the contents beside it link to each section on GitHub.

rtl-acquire — the RTL corpus supplier

Execute a staged, artifact-first corpus-expansion workflow for discovered RTL: acquire → expand (synth-only) → repair → validate → publish. Prefer deterministic scripts and policy files; treat the workspace ledgers and manifests as the source of truth.

For Internet-scale acquisition, use the embedded vendor/rtl-expander/ engine. It owns safe repository discovery, immutable revision acquisition, top/closure recovery, corpus certification, family deduplication, and scheduler statistics. R2G consumes only a CERTIFIED rtl-expander snapshot through scripts/acquire/import_expander_snapshot.py; never read its live frontier, queues, mutable manifests, or partially completed rounds directly.

Positioned upstream of the other r2g-skills: it feeds a stream of screened, synthesized, graph-converted designs. It never runs place/route or signoff — hand a promising design to signoff-loop for that.

The scoped-reuse contract (what this skill OWNS vs BORROWS)

OWNS (the heart — genuinely net-new for r2g):

  • acquire/ — discovery/search/clone/screen of candidate RTL at corpus scale (local _downloads trees, repo manifests, keyword search), RAM/macro exclusion, bundle-aware candidate CSVs, incremental scan ledgers.
  • corpus hygiene + publish — rtl/netlist signature dedup, repo/design quality scoring, publish eligibility gating, the merged corpus manifest.
  • repair/ — deterministic frontend repair (include dirs, stubs, memory limits, template materialization) + the JSON failure casebook (journal-side).

The embedded expander is an acquisition engine, not a fifth R2G sub-skill. After its certified snapshot is imported, the existing rtl-acquire expansion, repair, graph, publish, and signoff handoff contracts remain authoritative.

rtl-expander snapshot handoff

python3 scripts/run_expansion_round.py \
  --expander-corpus-root /path/to/rtl_corpus \
  --expander-view public_export_allowed \
  --priorities high medium

Read the full file on GitHub · 271 lines

Files

What ships with it

60 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.

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. 12d ago First seen · 271 lines · 107 tokens per session scan A 9f52c3de21d6

Subscribe to this mod's changes

rtl-acquire is a skill published in the GitHub repository ShenShan123/r2g-skills (42 stars, last pushed today), licensed MIT. It adds 107 tokens to every session and 3,522 once invoked, about $0.0005 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 2026-08-30.

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