Peer Review
spacering-net/codeg
Structured manuscript/grant review with checklist-based evaluation.
A skill your agent uses when reasoning about CIKM peer review — the EasyChair double-blind pipeline, the mixed IR/data-mining/knowledge-management reviewer pool, per-track evaluation criteria, the…
Install Cikm Review Process
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cikm-review-process -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cikm-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/CIKM-Skills/skills/cikm-review-process .claude/skills/cikm-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 "cikm-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CIKM-Skills/skills/cikm-review-process into .claude/skills/cikm-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cikm-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/CIKM-Skills/skills/cikm-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 cikm-review-process -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cikm-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/CIKM-Skills/skills/cikm-review-process .agents/skills/cikm-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 "cikm-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CIKM-Skills/skills/cikm-review-process into .agents/skills/cikm-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cikm-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 cikm-review-process -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cikm-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/CIKM-Skills/skills/cikm-review-process .cursor/skills/cikm-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 "cikm-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CIKM-Skills/skills/cikm-review-process into .cursor/skills/cikm-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cikm-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 CIKM-Skills/skills/cikm-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 cikm-review-process -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cikm-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/CIKM-Skills/skills/cikm-review-process .gemini/skills/cikm-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 "cikm-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CIKM-Skills/skills/cikm-review-process into .gemini/skills/cikm-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cikm-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 cikm-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 cikm-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/CIKM-Skills/skills/cikm-review-process .github/skills/cikm-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 "cikm-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CIKM-Skills/skills/cikm-review-process into .github/skills/cikm-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cikm-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 cikm-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 cikm-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/CIKM-Skills/skills/cikm-review-process .opencode/skills/cikm-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 "cikm-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CIKM-Skills/skills/cikm-review-process into .opencode/skills/cikm-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cikm-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
cikm-review-process- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 824 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 CIKM peer review — the EasyChair double-blind pipeline, the mixed IR/data-mining/knowledge-management reviewer pool, per-track evaluation criteria, the…
- Reasoning about CIKM peer review — the EasyChair double-blind pipeline
- SKILL.md covers The blended-pool effect, Per-track criteria shift, What moves a borderline and Timeline realism for the live…, plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- The mixed IR/data-mining/knowledge-management reviewer pool
What it does
Cikm Review Process is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when reasoning about CIKM peer review — the EasyChair double-blind pipeline, the mixed IR/data-mining/knowledge-management reviewer pool, per-track evaluation criteria, the ACM Peer Review Policy including the no-AI-written-reviews rule, notification timing, and what actually moves borderline decisions.
Its SKILL.md is about 1.7k 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 Peer 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
- Reasoning about CIKM peer review — the EasyChair double-blind pipeline
- The mixed IR/data-mining/knowledge-management reviewer pool
- Per-track evaluation criteria
- The ACM Peer Review Policy including the no-AI-written-reviews rule
Example prompts
- “/cikm-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
Cikm Review Process loads about 1.7k tokens when it runs. Until then it costs about 82 tokens; SKILL.md has 824 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). 824 words, ~1,716 tokens.
Download SKILL.mdSave it as
.claude/skills/cikm-review-process/SKILL.md (or your agent's skills folder).- name
- cikm-review-process
- description
- Use when reasoning about CIKM peer review — the EasyChair double-blind pipeline, the mixed IR/data-mining/knowledge-management reviewer pool, per-track evaluation criteria, the ACM Peer Review Policy including the no-AI-written-reviews rule, notification timing, and what actually moves borderline decisions.
CIKM Review Process
CIKM review runs on EasyChair under double-blind rules, inside the ACM Peer Review Policy, and — its defining feature — in front of a reviewer pool drawn from three communities at once. Verified 2026 mechanics (source map, 2026-07-08): submissions closed in May/June, notification lands August 7, and referees are explicitly barred from using AI systems to write reviews. Whether 2026 includes an author response window is unconfirmed in either direction (待核实); plan without assuming one.
The blended-pool effect
A CIKM paper is typically read by people whose default standards differ:
| Reviewer's home lane | What they instinctively grade | Complaint they file most |
|---|---|---|
| Information retrieval | Evaluation design, baselines, metric discipline | "Baselines are stale / significance untested" |
| Data mining | Mechanism novelty, scalability, ablation logic | "Delta over the nearest KDD-line method unclear" |
| KM / databases | Data model, integration cost, system realism | "Would not survive real schema/scale/noise" |
The practical consequence: a paper optimized for one lane can draw its harshest
review from another. Write the submission so each lane finds its own checklist
satisfied — a defensible evaluation, an isolated mechanism, and a credible data
story — or explicitly scope the claim to the lanes it serves. This is also why the
reviewer-pool breadth rewards two-lane papers (see cikm-topic-selection): they
give more of the panel a reason to champion.
Per-track criteria shift
The five tracks are judged against different success definitions. Full Research is graded on novelty plus evidence depth; Short Research on the sharpness of a single finding, not breadth; Applied Research on the credibility of deployment evidence (launch, data release) and transferable lessons; Resource on documentation, licensing, and likely reuse; Demonstration on what a visitor can actually do with the prototype. Reviewing a submission against the wrong track's bar — the most common self-review error — produces both false confidence and false alarm.
What moves a borderline
- A champion, not an average. With three lanes in the room, a decided advocate who says "my community needs this" outweighs a slightly higher mean score.
- Unanswered lane-specific objections sink. A mining reviewer's unaddressed scalability question reads as a gap even if both IR reviewers scored high.
- Compliance is upstream of merit. The 2026 desk-reject set — missed reviewer nomination, undeclared public version, budget or anonymity violations, missing GenAI disclosure — removes papers before any lane weighs in.
- Chairs calibrate across tracks. Meta-decisions reconcile the lanes; a review that misread the track's bar can be discounted at that level, which is why a polite confidential-comments note on track fit (where the form allows one) is occasionally decisive.
Timeline realism for the live cycle
Between the June close and the August 7 notification there is no author-visible
activity by default. Do not read silence as signal; do not email chairs for status;
do use the window as cikm-workflow Mode A prescribes (artifact readiness,
camera-ready pre-drafting). If a response phase is announced mid-cycle, it will be
short — pre-agree within the team who drafts and who signs off.
Show full SKILL.md (342 more words)Show less
Reading a CIKM review packet
When reviews arrive, decode them by lane before reacting:
For each review:
1. Identify the lane from the vocabulary
("nDCG/baselines/collections" → IR; "novelty/ablation/scale" → mining;
"schema/provenance/real data" → KM-DB)
2. Separate lane-standard demands (must answer) from
lane-mismatch complaints (may be a track/framing misread)
3. Rank objections by whether a chair would treat them as blocking:
correctness > missing decisive evidence > positioning > polishTwo panel patterns worth recognizing. Split-by-lane scores (one lane high, one
low) usually mean the paper is legible to only part of the panel — a framing
problem more than an evidence problem, fixable at the next venue with
cikm-writing-style. Uniform middling scores usually mean the contribution
is understood and judged thin — an evidence problem no rewrite fixes.
Confidentiality and integrity boundaries
The ACM Peer Review Policy governs both directions. Authors must not attempt reviewer identification, contact reviewers, or publicize review text with intent to pressure; reviewers must keep submissions confidential and — a 2026-explicit rule — must not have AI systems write their reviews. If a review appears AI-generated or plainly template-pasted, the recourse is a factual, unemotional note to the program chairs, not a public thread. Chairs can and do recalibrate around a defective review; they cannot around an author who breached process.
After the decision
Accepted papers move to cikm-camera-ready with an August 20 camera-ready gate —
thirteen days after notification, one of the tightest turnarounds in the family.
Rejected papers should mine the tri-lane reviews for routing information: lane-
specific objections point to the venue whose community wrote them, which is exactly
the input cikm-workflow's fallback ring consumes.
Scale realities
CIKM is one of the largest venues in its family — its proceedings run to many
hundreds of papers across tracks (exact counts per edition: check the ACM DL
record; 2026 statistics 待核实) — which shapes review dynamics in ways small
selective venues do not: reviewer load is high, so the first page carries even more of the
verdict (cikm-writing-style); topical match between paper and reviewer is
looser than at a single-community venue, which is why the abstract's
lane-vocabulary steers bidding; and per-track sub-committees (applied, resource,
demo) apply genuinely different rubrics rather than one program committee's
taste. Acceptance statistics for the current cycle were not verifiable
(待核实) — do not quote a rate to calibrate hopes; calibrate on whether each
lane's blocking question has an answer inside the PDF.
Output format
[Stage] pre-notification / notified-accept / notified-reject / response-window(if any)
[Lane read] IR / mining / KM-DB — likely stance of each on this paper
[Sharpest objection] <the one unanswered question most able to sink it>
[Compliance state] clean / at-risk (which trigger)
[Next move] <single highest-leverage action given the stage>© 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 CIKM-Skills/skills/cikm-review-process of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Cikm 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 |
|---|---|---|---|---|---|---|
| Cikm Review Process this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Peer Reviewspacering-net/codeg | 3.9k | 17 repos | ~5.9k | Automated safety check: Notes | MIT | |
| Scholar Evaluationspacering-net/codeg | 3.9k | 11 repos | ~3.2k | Automated safety check: Pass | MIT | |
| Academic Paper Writing PipelineImbad0202/academic-research-skills | 51k | — | ~16k | Automated safety check: Pass | Custom licence | |
| Academic Paper ReviewerImbad0202/academic-research-skills | 51k | — | ~11k | Automated safety check: Pass | Custom licence | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT |
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Simulates a journal peer review of a manuscript with a five-seat reviewer panel, an editorial synthesizer and several review modes.
Peer Review
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Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
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Orchestrates a ten-stage academic workflow from research to finished manuscript, including integrity checks, two rounds of peer review and revision.
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Categories
Questions about Cikm Review Process
What does Cikm Review Process do?
A skill your agent uses when reasoning about CIKM peer review — the EasyChair double-blind pipeline, the mixed IR/data-mining/knowledge-management reviewer pool, per-track evaluation criteria, the…. Cikm Review Process is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when reasoning about CIKM peer review — the EasyChair double-blind pipeline, the mixed IR/data-mining/knowledge-management reviewer pool, per-track evaluation criteria, the ACM Peer Review Policy including the no-AI-written-reviews rule, notification timing, and what actually moves borderline decisions.
When should I use Cikm Review Process?
Cikm Review Process fits situations like: reasoning about CIKM peer review — the EasyChair double-blind pipeline; the mixed IR/data-mining/knowledge-management reviewer pool; per-track evaluation criteria; the ACM Peer Review Policy including the no-AI-written-reviews rule.
How do I install Cikm Review Process in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cikm-review-process -a claude-code`. Or copy the skill folder (CIKM-Skills/skills/cikm-review-process in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/cikm-review-process in your project. Claude Code loads it when a task matches its description.
How do I install Cikm Review Process in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cikm-review-process -a codex`. Or copy the skill folder (CIKM-Skills/skills/cikm-review-process in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/cikm-review-process in your project. Codex loads it when a task matches its description.
Can I use Cikm 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 cikm-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/cikm-review-process, .gemini/skills/cikm-review-process, .github/skills/cikm-review-process and .opencode/skills/cikm-review-process in your project.
What does Cikm Review Process need to run?
SKILL.md names no scripts, command-line tools or credentials: Cikm Review Process is instructions for the agent only.
Does Cikm 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 Cikm 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 Cikm Review Process use?
Cikm 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 Cikm Review Process use?
About 1.7k tokens (SKILL.md is roughly 6.9k 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 Cikm Review Process?
Skills that share tags, products or a category with Cikm Review Process: Peer Review (spacering-net/codeg, 3.9k stars), Scholar Evaluation (spacering-net/codeg, 3.9k stars), Academic Paper Writing Pipeline (Imbad0202/academic-research-skills, 51k stars) and Academic Paper Reviewer (Imbad0202/academic-research-skills, 51k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Cikm 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.