dac-experiments
Use when designing or auditing the empirical evaluation of an ACM/IEEE Design Automation Conference (DAC) Research Manuscript, covering standard EDA benchmark suites (ISPD, EPFL, ISCAS/ITC, TAU, CircuitNet, OpenROAD flows), fair state-of-the-art baselines, QoR/PPA reporting with runtime, per-benchmark honesty, ablations that isolate the mechanism, and contamination-aware ML-for-EDA evaluation.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-experiments --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# DAC Experiments Use this before the November deadline when the evaluation is not yet locked. At DAC the evaluation *is* the paper: reviewers are EDA practitioners who decide acceptance mostly on whether the **QoR comparison is fair, standard, and reproducible**. The organizing principle is **measured design quality against the strongest baseline on recognized benchmarks** — not novelty in the abstract. ## Evaluation audit - **Use standard benchmark suites.** Match the suite to the task: **ISPD** placement/routing contests, the **EPFL** combinational benchmark suite for logic synthesis, **ISCAS'85/'89** and **ITC'99** for test/verification, the **TAU** contests for timing, **CircuitNet/OpenABC-D** and similar for ML-for-EDA, and **OpenROAD / OpenROAD-flow-scripts** for full-flow experiments. A private-benchmark-only evaluation is a scored weakness. - **Compare against the true state of the art,** tuned with a documented, equal effort. An untuned or outdated baseline is the most common DAC reject cause; the assigned reviewer often *is* the author of the stronger tool you skipped. - **Report the whole suite, not a subset.** Per-benchmark tables with the full circuit set; a cherry-pi
- Evaluation audit
- Claim-to-evidence design table
- PPA and QoR reporting floor
- Contamination-aware ML-for-EDA evaluation
- Vignette: evaluating a new global router
- Output format
What does the dac-experiments skill do?
Use when designing or auditing the empirical evaluation of an ACM/IEEE Design Automation Conference (DAC) Research Manuscript, covering standard EDA benchmark suites (ISPD, EPFL, ISCAS/ITC, TAU, CircuitNet, OpenROAD flows), fair state-of-the-art baselines, QoR/PPA reporting with runtime, per-benchmark honesty, ablations that isolate the mechanism, and contamination-aware ML-for-EDA evaluation.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-experiments --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.