Nature Paper Card
Yuan1z0825/nature-skills
Builds a structured deep-reading card for one scientific paper, covering methods, how experiments support claims, limitations and research ideas, with a script to prepare the source.
A skill your agent uses when designing or auditing the empirical evaluation of an ACM/IEEE Design Automation Conference (DAC) Research Manuscript, covering standard EDA benchmark suites (ISPD, EPFL…
Install Dac Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills dac-experiments --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/DAC-Skills/skills/dac-experiments .claude/skills/dac-experiments && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "dac-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/DAC-Skills/skills/dac-experiments into .claude/skills/dac-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dac-experiments", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/DAC-Skills/skills/dac-experimentsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills dac-experiments --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/DAC-Skills/skills/dac-experiments .agents/skills/dac-experiments && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "dac-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/DAC-Skills/skills/dac-experiments into .agents/skills/dac-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dac-experiments", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills dac-experiments --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/DAC-Skills/skills/dac-experiments .cursor/skills/dac-experiments && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "dac-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/DAC-Skills/skills/dac-experiments into .cursor/skills/dac-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dac-experiments", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path DAC-Skills/skills/dac-experiments--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills dac-experiments --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/DAC-Skills/skills/dac-experiments .gemini/skills/dac-experiments && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "dac-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/DAC-Skills/skills/dac-experiments into .gemini/skills/dac-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dac-experiments", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills dac-experimentsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-experiments -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/DAC-Skills/skills/dac-experiments .github/skills/dac-experiments && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "dac-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/DAC-Skills/skills/dac-experiments into .github/skills/dac-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dac-experiments", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-experiments -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills dac-experiments --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/DAC-Skills/skills/dac-experiments .opencode/skills/dac-experiments && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "dac-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/DAC-Skills/skills/dac-experiments into .opencode/skills/dac-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dac-experiments", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
dac-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 538 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when designing or auditing the empirical evaluation of an ACM/IEEE Design Automation Conference (DAC) Research Manuscript, covering standard EDA benchmark suites (ISPD, EPFL…
- Auditing the empirical evaluation of an ACM/IEEE Design Automation Conference (DAC) Research Manuscript
- SKILL.md covers Evaluation audit, Claim-to-evidence design table, PPA and QoR reporting floor and Contamination-aware ML-for-EDA…, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering standard EDA benchmark suites (ISPD
What it does
Dac Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Research & Science, covering Literature review. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- Auditing the empirical evaluation of an ACM/IEEE Design Automation Conference (DAC) Research Manuscript
- Covering standard EDA benchmark suites (ISPD
- OpenROAD flows)
- Fair state-of-the-art baselines
Example prompts
- “/dac-experiments”
What it can do on your machine
Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Dac Experiments loads about 1.4k tokens when it runs. Until then it costs about 103 tokens; SKILL.md has 538 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 538 words, ~1,428 tokens.
Download SKILL.mdSave it as
.claude/skills/dac-experiments/SKILL.md (or your agent's skills folder).- name
- dac-experiments
- description
- 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.
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-picked average invites "what happened on the circuits you dropped?"
- Report runtime and scalability, not just quality. EDA reviewers care whether the method scales to realistic design sizes (millions of cells), so include the largest benchmarks and the compute used.
- Ablate the mechanism. Isolate where the gain comes from — remove your key component and show the QoR degrade — so the reviewer can attribute the improvement to your idea, not to tuning.
- Design threats in. Know before you run which designs, PDKs, or corners limit generality, and instrument to bound them.
Claim-to-evidence design table
| DAC claim | Matching evidence | Reject pattern avoided |
|---|---|---|
| "Reduces wirelength / congestion" | Per-benchmark WL/DRC on ISPD vs a tuned SOTA placer | "Only averages; weak baseline" |
| "Closes timing better" | WNS/TNS across TAU/real designs, equal area/power | "Improved slack by hurting area silently" |
| "Fewer verification escapes / more coverage" | Coverage/bug-find on ISCAS/ITC or real RTL vs prior tool | "Toy circuits only" |
| "Scales to large designs" | Runtime/memory at million-cell scale | "Small benchmarks; scalability asserted" |
| "ML method predicts QoR" | Held-out designs, error metrics vs analytical/prior-ML baseline | "Trained and tested on the same designs" |
| "The new component drives the gain" | Ablation removing it | "Contribution and tuning entangled" |
Show full SKILL.md (183 more words)Show less
PPA and QoR reporting floor
- Report the full PPA picture: a wirelength or timing win that silently costs area or power is not a win — show the trade-off.
- Give per-benchmark numbers and the aggregate; state the geometric-mean convention you use.
- Report runtime for both your method and the baseline on the same hardware, and the hardware.
- For stochastic methods (simulated annealing, RL-based flows) report variance across seeds/runs, not a single lucky run.
Contamination-aware ML-for-EDA evaluation
When a learner is in the loop, the reviewer's first questions are about leakage and fairness:
[Split integrity] train and test on DIFFERENT designs/netlists; never leak a test design into
training. Report the split explicitly.
[Baseline] compare against the strong non-ML tool (analytic placer, classical STA) AND the
prior-ML method, not just an untrained control
[Generalization] evaluate on designs/technology nodes unseen in training; ML-for-EDA that only
works on its training distribution is a scored weakness
[Determinism] report seeds and variance; a single run is not evidence for an RL flow
[Cost honesty] report training cost and inference cost; a method needing per-design retraining
must say so
[Data provenance] name the dataset (CircuitNet, OpenABC-D) and version; cache generated dataVignette: evaluating a new global router
A paper claims a router that cuts congestion at equal wirelength. The matching plan: run on the full ISPD routing benchmark set (not a subset); compare against the strongest published router tuned to equal effort; report per-benchmark wirelength, DRC/overflow, and runtime on stated hardware; include the largest circuits to show scaling; ablate the congestion-aware component to show it, not parameter tuning, drives the gain; and state external validity (technology node, macro density) as a bounded threat — every number traceable to a logged, re-runnable flow.
Output format
[Evaluation readiness] strong / adequate / weak
[Benchmarks] standard suite(s) named + full set reported? yes/no
[Baseline fairness] strongest SOTA, tuned, equal effort, on same hardware? yes/no
[QoR completeness] full PPA + runtime + variance reported? yes/no
[Ablation] mechanism isolated from tuning? yes/no
[ML leakage] train/test designs disjoint + unseen-node generalization? yes/no/NA
[Decision-critical run] the one experiment that would most strengthen the case© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in DAC-Skills/skills/dac-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Dac Experiments next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Dac Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Nature Paper CardYuan1z0825/nature-skills | 47k | 2 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Systematic Review ScreenerImbad0202/academic-research-skills | 51k | — | ~8.4k | Automated safety check: Pass | Custom licence | |
| Literature Reviewneflibata-feng/MyArxiv-Agent | 126 | 20 repos | ~5.9k | Automated safety check: Notes | MIT | |
| Preprint Search on bioRxivLigphiDonk/Oh-my--paper | 738 | 12 repos | ~3.7k | Automated safety check: Pass | MIT | |
| Academic Paper Writing PipelineImbad0202/academic-research-skills | 51k | — | ~16k | Automated safety check: Pass | Custom licence |
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Systematic Review Screener
Imbad0202/academic-research-skills
Screens records for systematic, scoping and rapid reviews against fixed eligibility rules, using two blinded AI reviewers and a third adjudicator, with traceable PRISMA counts.
Literature Review
neflibata-feng/MyArxiv-Agent
Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.).
Preprint Search on bioRxiv
LigphiDonk/Oh-my--paper
Searches bioRxiv life sciences preprints by keyword, author, date range or category with a Python script, returning JSON metadata and optional PDF downloads.
Academic Paper Writing Pipeline
Imbad0202/academic-research-skills
Runs a 12-agent pipeline that plans, drafts, cites, reviews and formats academic papers, with modes for revision, rebuttals, abstracts and citation checks.
Deep Research Workflow
TokenRhythm/opensquilla
Runs multi-round research in three stages with a persisted state file, evidence tracking and a long-form report with per-claim citations.
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Questions about Dac Experiments
What does Dac Experiments do?
A skill your agent uses when designing or auditing the empirical evaluation of an ACM/IEEE Design Automation Conference (DAC) Research Manuscript, covering standard EDA benchmark suites (ISPD, EPFL…. Dac Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
When should I use Dac Experiments?
Dac Experiments fits situations like: auditing the empirical evaluation of an ACM/IEEE Design Automation Conference (DAC) Research Manuscript; covering standard EDA benchmark suites (ISPD; openROAD flows); fair state-of-the-art baselines.
How do I install Dac Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-experiments -a claude-code`. Or copy the skill folder (DAC-Skills/skills/dac-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/dac-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Dac Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-experiments -a codex`. Or copy the skill folder (DAC-Skills/skills/dac-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/dac-experiments in your project. Codex loads it when a task matches its description.
Can I use Dac Experiments in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-experiments -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dac-experiments, .gemini/skills/dac-experiments, .github/skills/dac-experiments and .opencode/skills/dac-experiments in your project.
What does Dac Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Dac Experiments is instructions for the agent only.
Does Dac Experiments access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Dac Experiments safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Dac Experiments use?
Dac Experiments is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Dac Experiments use?
About 1.4k tokens (SKILL.md is roughly 5.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Dac Experiments?
Skills that share tags, products or a category with Dac Experiments: Nature Paper Card (Yuan1z0825/nature-skills, 47k stars), Systematic Review Screener (Imbad0202/academic-research-skills, 51k stars), Literature Review (neflibata-feng/MyArxiv-Agent, 126 stars) and Preprint Search on bioRxiv (LigphiDonk/Oh-my--paper, 738 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Dac Experiments?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,228 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.
Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.