Agent skill

Dac Reproducibility

by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills

Install Dac Reproducibility

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-reproducibility -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills dac-reproducibility --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ 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-reproducibility .claude/skills/dac-reproducibility && rm -rf skills-src

Use ~/.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/

Paste into Claude Code

Install the "dac-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/DAC-Skills/skills/dac-reproducibility into .claude/skills/dac-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dac-reproducibility", 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

$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/DAC-Skills/skills/dac-reproducibility

Type 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.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-reproducibility -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills dac-reproducibility --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ 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-reproducibility .agents/skills/dac-reproducibility && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "dac-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/DAC-Skills/skills/dac-reproducibility into .agents/skills/dac-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dac-reproducibility", 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.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-reproducibility -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills dac-reproducibility --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ 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-reproducibility .cursor/skills/dac-reproducibility && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "dac-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/DAC-Skills/skills/dac-reproducibility into .cursor/skills/dac-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dac-reproducibility", 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

$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path DAC-Skills/skills/dac-reproducibility

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-reproducibility -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills dac-reproducibility --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ 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-reproducibility .gemini/skills/dac-reproducibility && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "dac-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/DAC-Skills/skills/dac-reproducibility into .gemini/skills/dac-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dac-reproducibility", 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.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills dac-reproducibility

Installs 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).

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-reproducibility -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ 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-reproducibility .github/skills/dac-reproducibility && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "dac-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/DAC-Skills/skills/dac-reproducibility into .github/skills/dac-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dac-reproducibility", 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.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-reproducibility -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills dac-reproducibility --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ 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-reproducibility .opencode/skills/dac-reproducibility && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "dac-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/DAC-Skills/skills/dac-reproducibility into .opencode/skills/dac-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dac-reproducibility", 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-reproducibility
GitHub stars: 1.2k
Token cost: ~1.2k tokens
SKILL.md length: 513 words
Files: 1
Skills in repo: 2,387
Repo updated: Sep 27, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Building the reproducibility story for an ACM/IEEE Design Automation Conference (DAC) Research Manuscript
SKILL.md covers The EDA reproducibility floor, Seeds, variance, and…, The anonymized-then-public… and Claim-to-reproduction mapping, plus 2 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
Covering pinned EDA benchmark suites and versions (ISPD

What it does

Dac Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when building the reproducibility story for an ACM/IEEE Design Automation Conference (DAC) Research Manuscript, covering pinned EDA benchmark suites and versions (ISPD, EPFL, ISCAS/ITC, TAU, CircuitNet), open-source flow provenance (OpenROAD, ABC, Yosys), PDK/library and tool-version disclosure, seed/variance reporting for stochastic and ML flows, and the anonymized-then-public repository path — absent a formal DAC artifact-badging track.

Its SKILL.md is about 1.2k 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 Reproducible research. 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

Building the reproducibility story for an ACM/IEEE Design Automation Conference (DAC) Research Manuscript
Covering pinned EDA benchmark suites and versions (ISPD
Open-source flow provenance (OpenROAD
PDK/library and tool-version disclosure

Example prompts

“/dac-reproducibility”

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 Reproducibility loads about 1.2k tokens when it runs. Until then it costs about 117 tokens; SKILL.md has 513 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~117
When it runs · the whole SKILL.md, loaded when a task matches: ~1.2k

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). 513 words, ~1,219 tokens.

Download SKILL.mdSave it as .claude/skills/dac-reproducibility/SKILL.md (or your agent's skills folder).

name: dac-reproducibility
description: Use when building the reproducibility story for an ACM/IEEE Design Automation Conference (DAC) Research Manuscript, covering pinned EDA benchmark suites and versions (ISPD, EPFL, ISCAS/ITC, TAU, CircuitNet), open-source flow provenance (OpenROAD, ABC, Yosys), PDK/library and tool-version disclosure, seed/variance reporting for stochastic and ML flows, and the anonymized-then-public repository path — absent a formal DAC artifact-badging track.

DAC Reproducibility

Build the reproducibility story into the evaluation, not onto it. DAC does not run a formal, badge-issuing artifact-evaluation track for research manuscripts (待核实 per cycle), so reproducibility at DAC is not a review checkbox — it is what makes your QoR numbers credible to a skeptical EDA reviewer and usable by the community that cites you. The currency is pinned benchmarks, disclosed tool versions, and re-runnable flows.

The EDA reproducibility floor

Pin the benchmark suite and version. Name the exact suite (ISPD 2005/2015 contests, the EPFL combinational suite, ISCAS'85/'89, ITC'99, a TAU contest set, CircuitNet/OpenABC-D) and the specific release. "Standard benchmarks" without a version is not reproducible.
Disclose the flow and tool versions. State the EDA tools and versions used — open (OpenROAD, ABC, Yosys, KLayout) or commercial — because QoR depends heavily on the flow. If a commercial tool or PDK is under NDA, say which class of tool it is and give what you can.
Name the PDK / technology / library. QoR numbers are meaningless without the technology node and standard-cell library context (e.g., an open Nangate/ASAP7 PDK, or a named foundry node under NDA). Report the node and library or the reason you cannot.
Report the hardware and runtime. The machine, core count, and memory for every runtime number; a runtime with no hardware context cannot be compared.
Pin data provenance for ML-for-EDA. Dataset name and version, the train/test design split, and cached generated data — a model that needs re-generated data or per-design retraining must say so.

Seeds, variance, and stochastic flows

Many EDA flows are stochastic (simulated annealing, partitioning, RL-based placement/routing). A single run is not reproducible evidence:

text

[Seeds]      report the seeds used and fix them where the tool allows
[Runs]       multiple runs; report mean and variance/spread, not a lucky best
[Determinism] note where the flow is nondeterministic (threading, tie-breaking) and how you handled it
[Environment] container or pinned dependency list so a re-runner gets the same tool behavior

Show full SKILL.md (243 more words)Show less

The anonymized-then-public repository path

At submission (double-blind): if you link code/data, anonymize it exactly like the PDF — no author/lab names, no personal GitHub, no cluster paths, no vendor fingerprints (../dac-submission). Reviewers may not open it, so it strengthens but cannot rescue the paper.
After acceptance: publish the de-anonymized repository, ideally with a DOI-issuing archive (Zenodo/Software Heritage) for a stable citation, an OSI license, and a README that maps each paper claim to the script and benchmark that produce it. This is community goodwill and citation insurance, not a DAC badge.

Claim-to-reproduction mapping

Even without a review requirement, build the mapping that makes your numbers checkable:

Paper claim	What reproduces it
"X% wirelength on ISPD"	The exact ISPD release + your tool version + the run script + seeds
"Y% timing improvement"	The design set + PDK/library + STA tool version + the flow script
"ML predicts IR-drop with error E"	The dataset version + train/test split + the model checkpoint
"Runs in Z hours at N cells"	The hardware spec + the largest-benchmark log

What DAC-specific reproducibility is not

It is not an ACM artifact-badging exercise (DAC has no standing badge track); do not design for a badge that does not exist.
It is not empirical-SE data availability; DAC evidence is QoR on circuits, so provenance means benchmark/PDK/tool versions and seeds, not human-subject protocols.
It is not optional for credibility: an EDA reviewer distrusts a QoR claim that no one else could reproduce, even where no rule compels an artifact.

Output format

text

[Reproducibility readiness] strong / adequate / weak
[Benchmarks pinned]   suite + version named? yes/no
[Flow disclosed]      tool versions + PDK/library + hardware reported? yes/no
[Stochasticity]       seeds + variance across runs reported? yes/no
[ML provenance]       dataset version + train/test split + cached data? yes/no/NA
[Repo path]           anonymized at review / DOI-archived + licensed after accept? planned? yes/no

Files

Just SKILL.md in DAC-Skills/skills/dac-reproducibility of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Dac Reproducibility 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.

Dac Reproducibility compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Dac Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills	1.2k	—	~1.2k	Automated safety check: Pass	MIT	13 days ago
Peer ReviewK-Dense-AI/claude-scientific-writer	2.4k	2 repos	~3.1k	Automated safety check: Notes	MIT	yesterday
CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw	617	1 repos	~1.8k	Automated safety check: Pass	None	7 mo ago
Compute Environment Setupaipoch/open-science	5.5k	—	~2.6k	Automated safety check: Pass	Apache-2.0	today
Figure Styleaipoch/open-science	5.5k	—	~5.1k	Automated safety check: Pass	Apache-2.0	today
Add Bactopia Toolbactopia/bactopia	522	—	~4.1k	Automated safety check: Pass	MIT	2 mo ago

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2.4k GitHub starsUsed in 2 repos~3.1k tokens
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CHARLS Paper Reproduction Guide
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Guides an agent through reproducing papers built on the CHARLS health and retirement survey, from variable mapping to cognition, depression and isolation scores.
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Questions about Dac Reproducibility

What does Dac Reproducibility do?

A skill your agent uses when building the reproducibility story for an ACM/IEEE Design Automation Conference (DAC) Research Manuscript, covering pinned EDA benchmark suites and versions (ISPD, EPFL…. Dac Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when building the reproducibility story for an ACM/IEEE Design Automation Conference (DAC) Research Manuscript, covering pinned EDA benchmark suites and versions (ISPD, EPFL, ISCAS/ITC, TAU, CircuitNet), open-source flow provenance (OpenROAD, ABC, Yosys), PDK/library and tool-version disclosure, seed/variance reporting for stochastic and ML flows, and the anonymized-then-public repository path — absent a formal DAC artifact-badging track.

When should I use Dac Reproducibility?

Dac Reproducibility fits situations like: building the reproducibility story for an ACM/IEEE Design Automation Conference (DAC) Research Manuscript; covering pinned EDA benchmark suites and versions (ISPD; open-source flow provenance (OpenROAD; PDK/library and tool-version disclosure.

How do I install Dac Reproducibility in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-reproducibility -a claude-code`. Or copy the skill folder (DAC-Skills/skills/dac-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/dac-reproducibility in your project. Claude Code loads it when a task matches its description.

How do I install Dac Reproducibility in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-reproducibility -a codex`. Or copy the skill folder (DAC-Skills/skills/dac-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/dac-reproducibility in your project. Codex loads it when a task matches its description.

Can I use Dac Reproducibility 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-reproducibility -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-reproducibility, .gemini/skills/dac-reproducibility, .github/skills/dac-reproducibility and .opencode/skills/dac-reproducibility in your project.

What does Dac Reproducibility need to run?

SKILL.md names no scripts, command-line tools or credentials: Dac Reproducibility is instructions for the agent only.

Does Dac Reproducibility 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 Reproducibility 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 Reproducibility use?

Dac Reproducibility 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 Reproducibility use?

About 1.2k tokens (SKILL.md is roughly 4.9k 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 Reproducibility?

Skills that share tags, products or a category with Dac Reproducibility: Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars), CHARLS Paper Reproduction Guide (xjtulyc/MedgeClaw, 617 stars), Compute Environment Setup (aipoch/open-science, 5.5k stars) and Figure Style (aipoch/open-science, 5.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Dac Reproducibility?

brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 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.

Install Dac Reproducibility

Facts

At a glance

What it does

When your agent uses it

Example prompts

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

DAC Reproducibility

The EDA reproducibility floor

Seeds, variance, and stochastic flows

The anonymized-then-public repository path

Claim-to-reproduction mapping

What DAC-specific reproducibility is not

Output format

Files

Compare with similar skills

Similar skills

More from brycewang-stanford/Awesome-Journal-Skills

Related

Questions about Dac Reproducibility