Borrowing it
Nothing to install: this file belongs to ShenShan123/r2g-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ShenShan123/r2g-skills/main/CLAUDE.mdgit clone --depth 1 https://github.com/ShenShan123/r2g-skillsWrote 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/instructions/shenshan123/r2g-skills/claude-md)<a href="https://agentmods.dev/instructions/shenshan123/r2g-skills/claude-md"><img src="https://agentmods.dev/badge/instructions/shenshan123/r2g-skills/claude-md/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.
<a href="https://agentmods.dev/instructions/shenshan123/r2g-skills/claude-md"><img src="https://agentmods.dev/badge/instructions/shenshan123/r2g-skills/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.11254 | $0.11254 |
| Opus 5 | $0.05627 | $0.05627 |
| Sonnet 5 | $0.02251 | $0.02251 |
| Haiku 4.5 | $0.01125 | $0.01125 |
Grade B, and why
r2g-skills CLAUDE.md scanned grade B with 1 finding 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.
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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
`bootstrap.sh` (detect → plan → install → pin `env.local.sh` → verify); no-sudo conda path by default. How it starts
The opening of the file, as written. The whole thing — 482 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent-with-OpenROAD — Project Guide
AI-driven open-source EDA flow: natural-language spec → GDSII via OpenROAD-flow-scripts
(ORFS), with full signoff (DRC, LVS, RCX), then a training-ready graph dataset for GNN
predictors. Implemented as the r2g-skills Claude Code skill collection — four sub-skills
(signoff-loop + def-graph from the 2026-07-07 split, see
docs/superpowers/plans/r2g-skills-split-2026-07-07.md; eda-install added 2026-07-08, see
docs/superpowers/plans/r2g-skills-bootstrap-2026-07-08.md; rtl-acquire ingested 2026-07-09, see
docs/superpowers/plans/rtl-acquire-ingestion-2026-07-09.md):
eda-install— detects the machine and installs + verifies the toolchain the others run (ORFS + openroad/yosys, iverilog, klayout, magic/netgen, sky130A PDK, torch venv). One command,bootstrap.sh(detect → plan → install → pinenv.local.sh→ verify); no-sudo conda path by default.rtl-acquire— the RTL corpus supplier, UPSTREAM of the others: discovers/screens/acquires RTL at corpus scale (local trees, repo manifests, keyword search) and expands it synth-only into pre-layoutnetlist_graph.ptgraphs with dedup, quality scoring, and publish gating. Owns acquire + corpus publish + the one-click promote of a synth-proven candidate into a signoff-loop full-flow project (scripts/promote/promote_candidates.py, 2026-07-10); BORROWS env (_env.sh), synth (run_orfs.sh,ORFS_STAGES=synth), the graph format (def-graphnetlist_graph.py), and failure learning (knowledge.sqlite, runs stampedflow_scope='synth_only'; frontend classes land assynth-frontend-*events).signoff-loop— drives the flow RTL→GDS with full signoff and the self-improvement loop (the two memory DBs +engineer_loop) that eliminates DRC/LVS violations and closes timing at Fmax.def-graph— converts the clean, signed-off physical design (the ORFS6_final.odb/.def/.spef+ platform liberty/LEF) into PyTorch-Geometric graph datasets: five graph views (b–f), emitted asHeteroDataby default (2026-07-16;R2G_GRAPH_KIND=homofor the legacy flat tensors), the shared tech-lib/LEF/DEF parser, and feature (X) / label (Y) extraction — labels are congestion, wirelength, per-path timing slack, IR drop, and SPEF-derived RC parasitics (the last ay[N,6]node label + a separaterc_edge_*parasitic edge set, merged 2026-07-07).
Each skill has ONE heart; everything else is plumbing — read the two ⭐ sections below:
signoff-loop→ The Closed Learning Loop — the two memory DBs (knowledge.sqlite= what resulted,journal.sqlite= what was done) +engineer_loop, the autonomous driver that closes the wheel unattended (flow → fix → learn → A/B-promote) and learns repair recipes that transfer across designs/platforms.def-graph→ The Dataset-Construction Pipeline — three composable stages (labels → features → graphs) keyed to the same DEF so X and Y join, whose failure mode is a plausible CSV with silently wrong values.
This file is orientation; the skills document how to run/debug/tune. Don't duplicate skill content
or per-run results here — when you fix a bug, update the relevant sub-skill under r2g-skills/
(signoff-loop/ for flow/signoff/learning, def-graph/ for dataset construction), not this file. Prefer
editing existing scripts/ over adding new ones; use the documented steps, not ad-hoc shell, in production.
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 · 482 lines · 11,254 tokens per session scan B 5f2b43e12365
r2g-skills CLAUDE.md is an instructions file published in the GitHub repository ShenShan123/r2g-skills (42 stars, last pushed today), licensed MIT. It adds 11,254 tokens to every session, about $0.0563 per session on Opus 5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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