Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
A skill your agent uses when hardening the reproducibility of a CIKM paper — pinning the pipeline stages where IR, mining, and knowledge-management results silently diverge, documenting KGs and…
Install Cikm Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cikm-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cikm-reproducibility --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-reproducibility .claude/skills/cikm-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CIKM-Skills/skills/cikm-reproducibility into .claude/skills/cikm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cikm-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 install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CIKM-Skills/skills/cikm-reproducibilityType 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-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cikm-reproducibility --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-reproducibility .agents/skills/cikm-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CIKM-Skills/skills/cikm-reproducibility into .agents/skills/cikm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cikm-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.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cikm-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cikm-reproducibility --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-reproducibility .cursor/skills/cikm-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CIKM-Skills/skills/cikm-reproducibility into .cursor/skills/cikm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cikm-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 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path CIKM-Skills/skills/cikm-reproducibility--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-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cikm-reproducibility --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-reproducibility .gemini/skills/cikm-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CIKM-Skills/skills/cikm-reproducibility into .gemini/skills/cikm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cikm-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.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cikm-reproducibilityInstalls 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-reproducibility -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-reproducibility .github/skills/cikm-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CIKM-Skills/skills/cikm-reproducibility into .github/skills/cikm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cikm-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.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cikm-reproducibility -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-reproducibility --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-reproducibility .opencode/skills/cikm-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CIKM-Skills/skills/cikm-reproducibility into .opencode/skills/cikm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cikm-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
cikm-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 798 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when hardening the reproducibility of a CIKM paper — pinning the pipeline stages where IR, mining, and knowledge-management results silently diverge, documenting KGs and…
- Hardening the reproducibility of a CIKM paper — pinning the pipeline stages where IR
- SKILL.md covers Divergence map for chained…, Unreleasable data, releasable…, GenAI disclosure as a… and Environment capture, plus 5 more sections
- Calls make and git
- Knowledge-management results silently diverge
What it does
Cikm Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening the reproducibility of a CIKM paper — pinning the pipeline stages where IR, mining, and knowledge-management results silently diverge, documenting KGs and enterprise data that cannot be released, keeping the GenAI disclosure consistent with how code and data were produced, and preparing the post-acceptance release.
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 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
- Hardening the reproducibility of a CIKM paper — pinning the pipeline stages where IR
- Knowledge-management results silently diverge
- Documenting KGs and enterprise data that cannot be released
- Keeping the GenAI disclosure consistent with how code and data were produced
Example prompts
- “/cikm-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
Shell commands in SKILL.md call:
makegit
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.
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 Reproducibility loads about 1.7k tokens when it runs. Until then it costs about 89 tokens; SKILL.md has 798 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). 798 words, ~1,675 tokens.
Download SKILL.mdSave it as
.claude/skills/cikm-reproducibility/SKILL.md (or your agent's skills folder).- name
- cikm-reproducibility
- description
- Use when hardening the reproducibility of a CIKM paper — pinning the pipeline stages where IR, mining, and knowledge-management results silently diverge, documenting KGs and enterprise data that cannot be released, keeping the GenAI disclosure consistent with how code and data were produced, and preparing the post-acceptance release.
CIKM Reproducibility
Reproducibility at CIKM has a venue-specific difficulty: the typical paper chains components from different communities — an index, a graph, a model, a ruleset — and each link has its own silent-divergence habits. A reader who cannot rebuild the chain cannot attribute the result, and a blended review panel contains someone able to notice each weak link.
Divergence map for chained pipelines
| Chain link | How results silently drift | Pin |
|---|---|---|
| Text preprocessing / indexing | Tokenizer versions, stopword lists, index-time defaults differ across toolkits | Name toolkit + version + config file in the artifact |
| KG snapshot | Public KGs (Wikidata-class) change daily; entity counts drift | Freeze and state the dump date; ship the extracted subgraph if licensable |
| Candidate generation | Recall stage caps and thresholds rarely reported | Report every cutoff; they bound the final metrics |
| Training | Seeds, hardware nondeterminism, early-stopping criteria | Seed policy + selection rule in the protocol paragraph |
| Evaluation | Metric implementations disagree at tie-breaking and cutoffs | Name the evaluation library version; never hand-roll silently |
| LLM components | Model version/API drift; prompts unlogged | Pin model identifiers and dates; log prompts verbatim in the artifact |
The discipline: for each link, either the artifact pins it or the paper states it. A link pinned nowhere is where a failed replication will land.
Unreleasable data, releasable knowledge
CIKM's KM lane routinely involves enterprise corpora, clickstreams, or proprietary KGs that cannot ship. The venue-honest pattern:
- Describe the unreleasable data statistically (size, schema, class balance, collection window) at a level where a reader could construct a synthetic analog — then actually provide that analog generator when feasible.
- Run the public-data variant of every headline experiment, even if the effect is smaller; a result that exists only on invisible data asks the panel for faith.
- State the release position explicitly in the paper ("logs cannot be released; the sampling script and schema are in the artifact") rather than leaving the reader to discover the gap.
GenAI disclosure as a reproducibility document
CIKM 2026's mandatory GenAI Usage Disclosure covers code and data, not just prose (source map, 2026-07-08). Treat it as part of the methods record: if evaluation scripts, synthetic data, prompts, or labels were generated with AI assistance, the disclosure plus the artifact should together let a reader judge what that implies for the result. A disclosure that says "AI used for coding" while the artifact contains unexplained generated labels is an inconsistency automated compliance checks — which the conference reserves — or reviewers can catch.
Environment capture
Chained pipelines multiply environment surface, so capture it in layers:
| Layer | Capture mechanism |
|---|---|
| OS + system libraries | Container image or a documented base image tag |
| Language environments | Lockfiles (exact versions), not loose requirement ranges |
| Toolkit configs | The actual config files, committed — not "default settings" prose |
| Data inputs | Checksums + download scripts, or the frozen extraction (see KG row) |
| Hardware assumptions | GPU/CPU class and memory floor stated where results are timed |
The test is transferability: a lab-mate on a clean machine, without the authors in the room, reaches the headline table. Running that internal replication before submission is the single highest-yield reproducibility exercise — it finds the unpinned link while it can still be pinned.
Show full SKILL.md (286 more words)Show less
Where reproducibility pays at this venue
Three concrete CIKM payoffs beyond principle. First, the blended panel: whichever
lane doubts the result will probe its own link of the chain, so pinning every link
is defense in all three directions. Second, resource-track reviewers and readers
judge adoptability, which is reproducibility wearing its public face
(cikm-artifact-evaluation). Third, follow-up work: CIKM's back catalog shows
methods becoming standard baselines (DRMM, BERT4Rec); papers get that afterlife
only when third parties can run them — the reproducible version of a method is the
one that accumulates citations as a baseline.
Release timeline
Anonymized review artifact during submission (see cikm-supplementary for what the
budget permits); public repository at camera-ready, with license, versioned release
tag, and the exact commit that produced the proceedings numbers. The 2026
notification-to-camera-ready window is thirteen days — build the release during
the review wait (cikm-workflow Mode A), not inside that window.
Honest-failure disclosure
Chained pipelines rarely reproduce perfectly, and the venue-credible move is to say so first: a REPRODUCING.md that states which numbers regenerate exactly, which vary within a stated tolerance (GPU nondeterminism, sampling), and which depend on restricted inputs and therefore only regenerate in public-analog form. Declared tolerance reads as competence; discovered variance reads as concealment. The same document is where to state known environment sensitivities ("results verified on CUDA X; version Y shifts Table 3 by ±0.2") — the sentence that saves a replicator a week is the sentence that earns the citation.
One-command bar
# The replication target for a CIKM chained pipeline:
git clone <repo> && cd <repo>
make setup # pinned environment, data download or synthetic analog
make table2 # rebuilds the headline table end-to-end from the frozen inputsIf make table2 cannot exist because data is restricted, the repo must say so at
the top and offer the public-variant target instead. Silent partiality — a repo that
looks complete but is not runnable — costs more reviewer goodwill than an honest
scope statement.
Output format
[Chain audit] <link → pinned where (paper / artifact / nowhere)>
[Data position] <releasable / described+analog / public-variant-only>
[Disclosure consistency] <GenAI section vs. artifact contents>
[Release plan] <review artifact state → camera-ready repo state, dated>
[Weakest link] <the divergence a replicator would hit 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 CIKM-Skills/skills/cikm-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Cikm 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Cikm Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT | |
| CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw | 617 | 1 repos | ~1.8k | Automated safety check: Pass | None | |
| Compute Environment Setupaipoch/open-science | 5.5k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Figure Styleaipoch/open-science | 5.5k | — | ~5.1k | Automated safety check: Pass | Apache-2.0 | |
| Add Bactopia Toolbactopia/bactopia | 522 | — | ~4.1k | Automated safety check: Pass | MIT |
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CHARLS Paper Reproduction Guide
xjtulyc/MedgeClaw
Guides an agent through reproducing papers built on the CHARLS health and retirement survey, from variable mapping to cognition, depression and isolation scores.
Compute Environment Setup
aipoch/open-science
Prepares setup instructions and a named activation file for a user-managed software environment on an Open-Science SSH or Slurm compute host.
Figure Style
aipoch/open-science
Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots.
Add Bactopia Tool
bactopia/bactopia
Scaffold a complete Bactopia Tool across all three tiers -- module, subworkflow, and workflow entry point under workflows/bactopia-tools/.
Modeling Code and Result Contracts
yushui2022/MathModel-Skill
Generates result-evidence contracts, tables and runnable q1 to q3 modeling code scaffolds for a math modeling paper from a model route, a data plan and cleaned data.
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Categories
Questions about Cikm Reproducibility
What does Cikm Reproducibility do?
A skill your agent uses when hardening the reproducibility of a CIKM paper — pinning the pipeline stages where IR, mining, and knowledge-management results silently diverge, documenting KGs and…. Cikm Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening the reproducibility of a CIKM paper — pinning the pipeline stages where IR, mining, and knowledge-management results silently diverge, documenting KGs and enterprise data that cannot be released, keeping the GenAI disclosure consistent with how code and data were produced, and preparing the post-acceptance release.
When should I use Cikm Reproducibility?
Cikm Reproducibility fits situations like: hardening the reproducibility of a CIKM paper — pinning the pipeline stages where IR; knowledge-management results silently diverge; documenting KGs and enterprise data that cannot be released; keeping the GenAI disclosure consistent with how code and data were produced.
How do I install Cikm Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cikm-reproducibility -a claude-code`. Or copy the skill folder (CIKM-Skills/skills/cikm-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/cikm-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Cikm Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cikm-reproducibility -a codex`. Or copy the skill folder (CIKM-Skills/skills/cikm-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/cikm-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Cikm 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 cikm-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/cikm-reproducibility, .gemini/skills/cikm-reproducibility, .github/skills/cikm-reproducibility and .opencode/skills/cikm-reproducibility in your project.
What does Cikm Reproducibility need to run?
Going by SKILL.md and its folder, Cikm Reproducibility needs the command-line tools its instructions call (make and git).
Does Cikm Reproducibility access the network?
SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Cikm 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 Cikm Reproducibility use?
Cikm 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 Cikm Reproducibility use?
About 1.7k tokens (SKILL.md is roughly 6.7k 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 Cikm Reproducibility?
Skills that share tags, products or a category with Cikm 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 Cikm 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.