Acm Ieee Joint Conference On Digital Libraries
franklee16/academic-research-skills
A skill your agent uses when targeting ACM/IEEE Joint Conference on Digital Libraries (JCDL) or deciding whether a computer-science manuscript fits this venue.
A skill your agent uses when reasoning about how an ACM/IEEE Design Automation Conference (DAC) Research Manuscript is evaluated, covering double-blind TPC review, the novelty-plus-QoR decision…
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Install Dac Review Process
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-review-process -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills dac-review-process --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-review-process .claude/skills/dac-review-process && 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-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/DAC-Skills/skills/dac-review-process into .claude/skills/dac-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dac-review-process", 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-review-processType 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-review-process -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills dac-review-process --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-review-process .agents/skills/dac-review-process && 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-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/DAC-Skills/skills/dac-review-process into .agents/skills/dac-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dac-review-process", 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-review-process -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills dac-review-process --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-review-process .cursor/skills/dac-review-process && 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-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/DAC-Skills/skills/dac-review-process into .cursor/skills/dac-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dac-review-process", 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-review-process--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-review-process -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills dac-review-process --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-review-process .gemini/skills/dac-review-process && 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-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/DAC-Skills/skills/dac-review-process into .gemini/skills/dac-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dac-review-process", 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-review-processInstalls 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-review-process -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-review-process .github/skills/dac-review-process && 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-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/DAC-Skills/skills/dac-review-process into .github/skills/dac-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dac-review-process", 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-review-process -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-review-process --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-review-process .opencode/skills/dac-review-process && 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-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/DAC-Skills/skills/dac-review-process into .opencode/skills/dac-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dac-review-process", 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-review-process- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 674 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when reasoning about how an ACM/IEEE Design Automation Conference (DAC) Research Manuscript is evaluated, covering double-blind TPC review, the novelty-plus-QoR decision…
- Reasoning about how an ACM/IEEE Design Automation Conference (DAC) Research Manuscript is evaluated
- SKILL.md covers Process model, Reading a decision against the…, Novelty-plus-QoR: the DAC bar and How DAC differs from its…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering double-blind TPC review
What it does
Dac Review Process is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when reasoning about how an ACM/IEEE Design Automation Conference (DAC) Research Manuscript is evaluated, covering double-blind TPC review, the novelty-plus-QoR decision criteria, program-committee discussion, the accept/reject (no major-revision) outcome, the ~20-25% selectivity, and how DAC's industry-facing, single-shot process differs from the architecture venues' rebuttal-and-revision cycles.
Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
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
- Reasoning about how an ACM/IEEE Design Automation Conference (DAC) Research Manuscript is evaluated
- Covering double-blind TPC review
- The novelty-plus-QoR decision criteria
- Program-committee discussion
Example prompts
- “/dac-review-process”
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 Review Process loads about 1.6k tokens when it runs. Until then it costs about 106 tokens; SKILL.md has 674 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). 674 words, ~1,565 tokens.
Download SKILL.mdSave it as
.claude/skills/dac-review-process/SKILL.md (or your agent's skills folder).- name
- dac-review-process
- description
- Use when reasoning about how an ACM/IEEE Design Automation Conference (DAC) Research Manuscript is evaluated, covering double-blind TPC review, the novelty-plus-QoR decision criteria, program-committee discussion, the accept/reject (no major-revision) outcome, the ~20-25% selectivity, and how DAC's industry-facing, single-shot process differs from the architecture venues' rebuttal-and-revision cycles.
DAC Review Process
Model the pipeline before interpreting any single review. DAC's Research-Manuscript review is
double-blind, Technical-Program-Committee-driven, and single-shot: papers are reviewed against
novelty and measured design-quality impact, discussed by the committee, and get a binary
accept/reject — there is no journal-style Major Revision round. Anchor to the DAC 2026 cycle
facts in resources/official-source-map.md.
Process model
- Submission and review run on Softconf/START with double-blind anonymity: reviewers do not see author identities, and the manuscript must be scrubbed of identifying content.
- Each paper is read by multiple TPC members drawn from the relevant subcommittee (physical design, logic synthesis, verification/test, ML-for-EDA, security, embedded, etc.). Reviewers weigh novelty over prior art, technical soundness, the strength and fairness of the QoR evidence, relevance/impact to design automation, and clarity.
- The committee discusses borderline papers to reach the final verdict; a strong advocate who can answer the objections carries a paper through discussion.
- Decisions are essentially accept or reject (a fraction may be steered to a poster/LBR-style outcome per cycle — 待核实). There is no revise-and-resubmit within the cycle; a rejected paper reroutes to ICCAD/DATE/ASP-DAC or a journal.
- Research selectivity is historically ~20-25% (verify each cycle).
Reading a decision against the criteria
| Signal in the reviews | Underlying criterion | Author reality |
|---|---|---|
| "Incremental over [prior tool]" | Novelty | Structural; the delta must be reframed or the idea extended before reroute |
| "Baseline is weak / untuned" | Evidence fairness | Often fatal at DAC — the QoR comparison is the paper |
| "Only private benchmarks" | Evidence credibility | Add a recognized suite; results on toy circuits do not persuade |
| "Runtime/scalability unclear" | Soundness/impact | EDA reviewers care about scaling to realistic design sizes |
| "Unclear where the gain comes from" | Soundness | Missing ablation isolating the contribution |
Novelty-plus-QoR: the DAC bar
DAC is an engineering research venue: a beautiful idea with no measured QoR advantage rarely survives, and a large QoR number with thin novelty gets read as an engineering result, not a research contribution. Winning papers pair a genuinely new mechanism with a fair, benchmark-grounded QoR gain (PPA, wirelength, timing slack, coverage, or runtime) over the strongest prior technique. The most common reject cause is not a broken idea but an unconvincing comparison — a baseline the reviewer does not accept as state of the art or as fairly tuned.
Show full SKILL.md (306 more words)Show less
How DAC differs from its siblings
- vs. ISCA / MICRO / HPCA (architecture): those venues run author rebuttals and, in some
years, revision rounds, and reward microarchitectural novelty. DAC's research review has
historically been TPC-driven without a standing author-response period (待核实 for DAC
- and rewards design-automation novelty measured in QoR. Do not carry an architecture rebuttal playbook into DAC.
- vs. FSE / ICSE (software): no journal-style Major Revision, no ACM artifact-badging track, and a much tighter 6+1-page budget. DAC evidence is QoR on EDA benchmarks, not empirical-SE studies.
- vs. ICCAD / DATE / ASP-DAC (sibling EDA): overlapping reviewer pools and criteria but different calendars and committees — a DAC reject is a natural ICCAD/DATE/ASP-DAC candidate, but never assume shared deadlines or that the same reviewers see it.
Who reads you
Expect subarea-matched EDA experts who will check whether your baseline is the real state of the art, whether the benchmarks are standard and reported honestly (all circuits, not a cherry-picked subset), whether runtime and scalability are credible for realistic designs, and whether an ablation shows the gain comes from your mechanism. Vague "we improve QoR" claims without per-benchmark tables get caught, not skimmed.
Where author leverage actually exists
[Before submission] topic/subcommittee tags + a real abstract -> reviewer pool (largest lever)
[Manuscript] a fair, tuned, state-of-the-art baseline on standard benchmarks + an ablation
[Discussion] a champion reviewer who can answer the objections carries the paper
[After reject] no appeal; reroute to ICCAD/DATE/ASP-DAC or TCAD/TODAES with the reviews addressedBecause DAC has historically had no author rebuttal, the leverage is almost entirely front-loaded: you cannot talk a reviewer out of a weak-baseline finding after submission, so the baseline and benchmark choices must be unimpeachable before the November deadline.
Misreadings to avoid
- Expecting a rebuttal to save the paper — do not budget on a response window DAC may not run.
- Treating a big QoR number as sufficient — without novelty it reads as an Engineering-Track result.
- Assuming one champion is enough without evidence — the discussion turns on answers to the other reviewers' concrete objections, not enthusiasm.
- Projecting last year's process — deadline, selectivity, and whether any response step exists are decided per edition.
Output format
[Process stage] pre-submission / under review / decided
[Decision driver] novelty | evidence fairness | benchmark credibility | scalability | clarity
[Criterion map] each review point -> which criterion it invokes
[Leverage plan] the pre-submission action (baseline/benchmark/ablation) that would have moved it
[Reroute target] ICCAD / DATE / ASP-DAC / TCAD if rejected, with the fix to make first© 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-review-process of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Dac Review Process 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 Review Process this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.6k | Automated safety check: Pass | MIT | |
| Acm Ieee Joint Conference On Digital Librariesfranklee16/academic-research-skills | 223 | 1 repos | ~2k | Automated safety check: Pass | None | |
| Acm Ieee International Conference On Human Robot Interactionfranklee16/academic-research-skills | 223 | 1 repos | ~2k | Automated safety check: Pass | None | |
| Deepseek Reasonruvnet/ruflo | 74k | — | ~626 | Automated safety check: Notes | MIT | |
| Ieee Visualization Conferencefranklee16/academic-research-skills | 223 | 1 repos | ~2k | Automated safety check: Pass | None | |
| Ejentum Reasoning Harnesssickn33/agentic-awesome-skills | 47k | 1 repos | ~1.9k | Automated safety check: Pass | MIT |
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Questions about Dac Review Process
What does Dac Review Process do?
A skill your agent uses when reasoning about how an ACM/IEEE Design Automation Conference (DAC) Research Manuscript is evaluated, covering double-blind TPC review, the novelty-plus-QoR decision…. Dac Review Process is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when reasoning about how an ACM/IEEE Design Automation Conference (DAC) Research Manuscript is evaluated, covering double-blind TPC review, the novelty-plus-QoR decision criteria, program-committee discussion, the accept/reject (no major-revision) outcome, the ~20-25% selectivity, and how DAC's industry-facing, single-shot process differs from the architecture venues' rebuttal-and-revision cycles.
When should I use Dac Review Process?
Dac Review Process fits situations like: reasoning about how an ACM/IEEE Design Automation Conference (DAC) Research Manuscript is evaluated; covering double-blind TPC review; the novelty-plus-QoR decision criteria; program-committee discussion.
How do I install Dac Review Process in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-review-process -a claude-code`. Or copy the skill folder (DAC-Skills/skills/dac-review-process in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/dac-review-process in your project. Claude Code loads it when a task matches its description.
How do I install Dac Review Process in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-review-process -a codex`. Or copy the skill folder (DAC-Skills/skills/dac-review-process in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/dac-review-process in your project. Codex loads it when a task matches its description.
Can I use Dac Review Process 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-review-process -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-review-process, .gemini/skills/dac-review-process, .github/skills/dac-review-process and .opencode/skills/dac-review-process in your project.
What does Dac Review Process need to run?
SKILL.md names no scripts, command-line tools or credentials: Dac Review Process is instructions for the agent only.
Does Dac Review Process 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 Review Process 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 Review Process use?
Dac Review Process 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 Review Process use?
About 1.6k tokens (SKILL.md is roughly 6.3k 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 Review Process?
Skills that share tags, products or a category with Dac Review Process: Acm Ieee Joint Conference On Digital Libraries (franklee16/academic-research-skills, 223 stars), Acm Ieee International Conference On Human Robot Interaction (franklee16/academic-research-skills, 223 stars), Deepseek Reason (ruvnet/ruflo, 74k stars) and Ieee Visualization Conference (franklee16/academic-research-skills, 223 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Dac Review Process?
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.