What is signoff-loop?

A workflow for turning digital chip designs, written as RTL code, into GDSII layout files for manufacturing. It runs checks such as DRC, which finds layout-rule violations, and LVS, which checks that the layout matches the circuit design, while also checking timing.

Use it to run open-source chip implementation from RTL through final layout, diagnose DRC and LVS errors, improve timing, and produce signed-off design files. It can also prepare data for graph-based datasets through a companion skill.

It organizes many chip-design tools and repeated repair steps into one process, helping find layout and timing problems before signoff.

signoff-loop — Skill for Claude Code by ShenShan123

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/signoff-loop

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/signoff-loop/github.svg)](https://agentmods.dev/skills/shenshan123/r2g-skills/signoff-loop)

Your own site

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

Per session 170 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 12,920 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 warn 7 Sept 2026

SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

medium Rogue Agent · line 817
Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.

Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.

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	$0.00170	$0.12920
Opus 5	$0.00085	$0.06460
Sonnet 5	$0.00034	$0.02584
Haiku 4.5	$0.00017	$0.01292

Measured 11d ago against content hash 22112dafb4f4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

signoff-loop 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 28 executable files (knowledge/ab_runner.py, knowledge/action_domain.py, knowledge/analyze_execution.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/signoff-loop/SKILL.md · 841 lines

How it starts

The opening of the file, as written. The whole thing — 841 lines — stays where its author put it; the contents beside it link to each section on GitHub.

signoff-loop Skill

Execute a staged, artifact-first open-source EDA flow from specification to GDSII with full signoff checks using OpenROAD-flow-scripts (ORFS), and close the loop by learning from every run (the two memory DBs + engineer_loop) so DRC/LVS violations are eliminated and timing closes at the best Fmax. Prefer deterministic scripts for execution, keeping the agent focused on planning, generation, diagnosis, and iteration.

Dataset construction lives in the companion def-graph skill. This skill produces the clean, signed-off 6_final.def/.odb/.spef; converting those into PyG graph datasets (the five views b–f, tech-lib/LEF parser, feature + label extraction) is def-graph's job — see its SKILL.md.

Environment Setup

Every flow script sources scripts/flow/_env.sh on entry, which autodetects ORFS + tool paths and lets the user override any single value. You do not need to source anything manually.

Resolution order (first hit wins, per value)

Variable already set in the caller's environment — ORFS_ROOT=... run_orfs.sh ... wins unconditionally.
User env file — path in $R2G_ENV_FILE (if set).
In-skill override file — references/env.local.sh (copy from references/env.local.sh.template).
ORFS-provided env — $ORFS_ROOT/env.sh (once ORFS_ROOT is known).
System-wide env — /opt/openroad_tools_env.sh (if present).
Autodetect — command -v <tool> on $PATH, then a list of well-known install paths (e.g. $ORFS_ROOT/tools/install/OpenROAD/bin/openroad, $HOME/oss-cad-suite/bin/yosys, /usr/local/bin/klayout).

Checking what the skill found

bash scripts/flow/check_env.sh

Prints the resolved ORFS_ROOT, every tool binary it picked, and the platforms it can see. Exits non-zero if a required tool is missing.

Overriding just a few values

# One-off override for a single run
ORFS_ROOT=/opt/ORFS OPENROAD_EXE=/opt/openroad/bin/openroad \
  bash scripts/flow/run_orfs.sh design_cases/my_design nangate45

# Or persist overrides in a file
cp references/env.local.sh.template references/env.local.sh
# ...then edit the exports you care about; every subsequent flow picks them up.

Read the full file on GitHub · 841 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 · 841 lines · 170 tokens per session scan A 22112dafb4f4

Subscribe to this mod's changes

signoff-loop is a skill published in the GitHub repository ShenShan123/r2g-skills (42 stars, last pushed today), licensed MIT. It adds 170 tokens to every session and 12,920 once invoked, about $0.0009 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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