A skill for converting completed chip physical-design files into graph datasets for machine learning. Files such as DEF, LEF, Liberty, and SPEF describe layout, technology, timing, and connectivity information.

Use it after a signoff run to parse final DEF and related files and produce five graph views; it does not perform placement or routing.

It turns the output of a placement-and-routing run into node and edge features, labels, and graph structures ready for PyTorch Geometric.

def-graph — Skill for Claude Code by ShenShan123

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 ShenShan123/r2g-skills --skill def-graph

Clone the repo

git clone --depth 1 https://github.com/ShenShan123/r2g-skills

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/shenshan123/r2g-skills/def-graph/github.svg)](https://agentmods.dev/skills/shenshan123/r2g-skills/def-graph)

Your own site

<a href="https://agentmods.dev/skills/shenshan123/r2g-skills/def-graph"><img src="https://agentmods.dev/badge/skills/shenshan123/r2g-skills/def-graph/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/shenshan123/r2g-skills/def-graph"><img src="https://agentmods.dev/badge/skills/shenshan123/r2g-skills/def-graph.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 4,972 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.

Model	Per session	Once invoked
Fable 5.1	$0.00000	$0.04972
Opus 5	$0.00000	$0.02486
Sonnet 5	$0.00000	$0.00994
Haiku 4.5	$0.00000	$0.00497

Measured 11d ago against content hash 1614737a08db, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

def-graph 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 11d ago.

The scan reads SKILL.md. This mod also ships 47 executable files (scripts/extract/features/case_paths.py, scripts/extract/features/compute_feature_stats.py, scripts/extract/features/edges_gate_pin.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/def-graph/SKILL.md · 269 lines

How it starts

The opening of the file, as written. The whole thing — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.

def-graph Skill

Turn a completed, signed-off backend run into a training-ready graph dataset. This is the X/Y dataset-construction half of the RTL→GDS→Graph pipeline: it reads the physical-design artifacts a signoff flow produces (6_final.def, 6_final.odb, optional 6_final.spef, plus the platform liberty/LEF) and emits per-node/per-edge features (X), per-cell/per-net labels (Y), and five PyTorch-Geometric graph topologies (b–f).

Produce the inputs with the signoff-loop skill (or any ORFS run that leaves a 6_final.def); this skill consumes them. It never runs place-and-route.

Environment Setup

Every stage sources scripts/flow/_env.sh on entry, which autodetects ORFS + tool paths (shared resolver, identical contract to the signoff-loop skill). Nothing to source manually. Resolution order (first hit wins, per value): caller env → $R2G_ENV_FILE → in-skill references/env.local.sh → $ORFS_ROOT/env.sh → /opt/openroad_tools_env.sh → autodetect.

The graph-assembly stage additionally needs a torch venv (torch + torch_geometric + pandas). Point R2G_GRAPH_PYTHON at its bin/python; the label/feature/techlib stages do not need it. Install on /proj, never $HOME:

python3 -m venv /proj/<you>/pyenvs/r2g-graph
/proj/<you>/pyenvs/r2g-graph/bin/pip install torch --index-url https://download.pytorch.org/whl/cpu
/proj/<you>/pyenvs/r2g-graph/bin/pip install torch_geometric pandas

Workflow

The three stages compose. Each takes a <project-dir> that already holds a signed-off backend run (a 6_final.def reachable via ORFS results or $R2G_DEF) and [platform].

Platform authority (failure-patterns.md #30): explicit [platform] arg > build provenance > config.mk. When the arg is omitted, each stage consults the discovered backend's run-meta.json (via the shared scripts/flow/_provenance.sh) before trusting constraints/config.mk — a campaign re-point (setup_rtl_designs.py --platform X --force) rewrites config.mk for the whole corpus, and cell_type_id/*_type_id vocabularies are per-platform, so keying an existing DEF to the re-pointed platform is a silent-value defect. build_graphs.py stamps the resolved platform into graph_manifest.json; tools/verify_graph_dataset.py trusts manifest > run-meta.json > config.mk.

Read the full file on GitHub · 269 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.

11d ago First seen · 269 lines · 0 tokens per session scan A 1614737a08db

Subscribe to this mod's changes

def-graph is a skill published in the GitHub repository ShenShan123/r2g-skills (42 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,972 tokens. 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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