Academic Paper Reproduction Methodology
xjtulyc/MedgeClaw
Six-phase process for reproducing a published paper's results from provided data, from variable mapping and sample filtering through regression tables and a written report.
A skill your agent uses when stress-testing ICML experimental evidence before submission or rebuttal, including strong tuned baselines, mechanism-isolating ablations, seed variance and confidence…
Install Icml Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icml-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icml-experiments --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/ICML-Skills/skills/icml-experiments .claude/skills/icml-experiments && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "icml-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICML-Skills/skills/icml-experiments into .claude/skills/icml-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icml-experiments", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICML-Skills/skills/icml-experimentsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icml-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icml-experiments --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/ICML-Skills/skills/icml-experiments .agents/skills/icml-experiments && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "icml-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICML-Skills/skills/icml-experiments into .agents/skills/icml-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icml-experiments", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icml-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icml-experiments --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/ICML-Skills/skills/icml-experiments .cursor/skills/icml-experiments && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "icml-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICML-Skills/skills/icml-experiments into .cursor/skills/icml-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icml-experiments", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path ICML-Skills/skills/icml-experiments--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icml-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icml-experiments --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/ICML-Skills/skills/icml-experiments .gemini/skills/icml-experiments && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "icml-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICML-Skills/skills/icml-experiments into .gemini/skills/icml-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icml-experiments", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icml-experimentsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icml-experiments -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/ICML-Skills/skills/icml-experiments .github/skills/icml-experiments && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "icml-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICML-Skills/skills/icml-experiments into .github/skills/icml-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icml-experiments", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icml-experiments -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icml-experiments --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/ICML-Skills/skills/icml-experiments .opencode/skills/icml-experiments && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "icml-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICML-Skills/skills/icml-experiments into .opencode/skills/icml-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icml-experiments", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
icml-experiments- GitHub stars
- 1.2k
- Token cost
- ~843 tokens
- SKILL.md length
- 380 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when stress-testing ICML experimental evidence before submission or rebuttal, including strong tuned baselines, mechanism-isolating ablations, seed variance and confidence…
- Stress-testing ICML experimental evidence before submission
- SKILL.md covers Experiment audit, Reviewer-pushback patterns and…, Worked vignette: optimizer… and Rebuttal-ready result, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Including strong tuned baselines
What it does
Icml Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when stress-testing ICML experimental evidence before submission or rebuttal, including strong tuned baselines, mechanism-isolating ablations, seed variance and confidence intervals, compute disclosure, data leakage and split construction, reproducibility, negative results, and fit to ICML soundness, originality, and significance scoring.
Its SKILL.md is about 840 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 Statistics, Load testing and 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
- Stress-testing ICML experimental evidence before submission
- Including strong tuned baselines
- Mechanism-isolating ablations
- Seed variance and confidence intervals
Example prompts
- “/icml-experiments”
What it can do on your machine
Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Icml Experiments loads about 843 tokens when it runs. Until then it costs about 90 tokens; SKILL.md has 380 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). 380 words, ~843 tokens.
Download SKILL.mdSave it as
.claude/skills/icml-experiments/SKILL.md (or your agent's skills folder).- name
- icml-experiments
- description
- Use when stress-testing ICML experimental evidence before submission or rebuttal, including strong tuned baselines, mechanism-isolating ablations, seed variance and confidence intervals, compute disclosure, data leakage and split construction, reproducibility, negative results, and fit to ICML soundness, originality, and significance scoring.
ICML Experiments
Use this before submission or rebuttal when the central issue is whether experiments are sound enough for ICML. The question is not just "does it win"; it is whether the evidence supports the ML claim under fair comparison.
Experiment audit
- Baselines: current, strong, tuned, and correctly implemented.
- Ablations: isolate mechanism, architecture, objective, data, or optimization change.
- Variance: report seeds, confidence intervals, standard deviations, or a reason variance is not meaningful.
- Data: check leakage, split construction, duplication, filtering, licensing, and representative coverage.
- Compute: disclose hardware, training cost, inference cost, and comparison fairness.
- Scaling: show whether gains persist across model sizes, datasets, horizons, or domains when that supports the claim.
- Negative results: use failures to define boundaries rather than hide them.
- Appendix: put supporting detail there, but keep decisive evidence in the main 8 pages.
Reviewer-pushback patterns and the ICML fix
| Pushback | Why it lands at ICML | Fix |
|---|---|---|
| "Convergence guarantees under assumptions the experiments violate" | Theory paper asserts a rate under smoothness or bounded variance, but the deep-learning runs break it | State assumptions honestly, add a figure showing the rate holds empirically in-regime, flag where it does not |
| "Missing strong, tuned baselines" | The leaderboard win used an undertuned competitor | Re-tune the baseline with matched budget, report the search protocol |
| "No variance, single seed" | One run cannot separate signal from noise | Report seeds with confidence intervals or justify determinism |
| "Compute not disclosed" | ICML expects hardware and training-cost transparency | Add a compute table and confirm comparison fairness |
Show full SKILL.md (135 more words)Show less
Worked vignette: optimizer claim audit
A paper claims a new adaptive step-size method beats Adam with a non-convex convergence guarantee. The audit asks: is Adam tuned with the same budget, do the benchmark losses actually satisfy the proof's assumptions, and do gains survive across seeds and model sizes? If the win shrinks under a tuned baseline or the assumptions hold only on toy quadratics, the right move is to narrow the claim to the regime where both theory and experiments agree, rather than overclaim a universal speedup.
Rebuttal-ready result
During response, prefer a small decisive table, corrected baseline, missing ablation, or concise error analysis over a broad new experimental section. ICML gives one discussion round, so a single tuned-baseline row or in-regime variance plot moves a reviewer more than a sprawling new study.
Output format
[Evidence status] strong / adequate / weak
[Most vulnerable claim] <claim>
[Critical missing result] <baseline/ablation/variance/leakage/compute>
[Small response result] <feasible clarification>
[Claim narrowing] <text if evidence is not enough>© 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 ICML-Skills/skills/icml-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Icml Experiments next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Icml Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~843 | Automated safety check: Pass | MIT | |
| Academic Paper Reproduction Methodologyxjtulyc/MedgeClaw | 617 | 1 repos | ~1.3k | Automated safety check: Pass | None | |
| Challengepedrohcgs/claude-code-my-workflow | 1.6k | — | ~1.9k | Automated safety check: Notes | MIT | |
| Fcr Revision And Rebuttalfranklee16/academic-research-skills | 223 | 1 repos | ~1.1k | Automated safety check: Pass | None | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT | |
| Experiment AgentImbad0202/experiment-agent | 199 | — | ~3.1k | Automated safety check: Pass | CC-BY-NC-4.0 |
Similar skills
Challenge
pedrohcgs/claude-code-my-workflow
Stress-test a finding against the choices you did not make. An agent skill from pedrohcgs/claude-code-my-workflow.
Fcr Revision And Rebuttal
franklee16/academic-research-skills
A skill your agent uses when writing the response to a Field Crops Research (FCR) revision decision (major or minor) and revising the manuscript.
Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
Experiment Agent
Imbad0202/experiment-agent
Experiment executor and monitor for academic research. An agent skill from Imbad0202/experiment-agent.
Review Paper
pedrohcgs/claude-code-my-workflow
Comprehensive manuscript review with three modes: single-pass (default), --adversarial critic-fixer loop, and --peer [journal] simulated peer-review pipeline (editor + 2 dispositioned referees +…
More from brycewang-stanford/Awesome-Journal-Skills
All 2,387 skills in this repoAaag Data Analysis
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when running and reporting the analysis for an Annals of the American Association of Geographers manuscript — spatial statistics and modeling, remote-sensing accuracy, or…
Aaag Literature Positioning
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when positioning an Annals of the American Association of Geographers manuscript in the literature — engaging geographic scholarship across the relevant area and the…
Aaag Rebuttal
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when responding to an Annals of the American Association of Geographers decision letter (major/minor revision) — building a point-by-point response to the subject editor and…
Aaag Research Design
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when defending the research design of an Annals of the American Association of Geographers manuscript — spatial/quantitative analysis and GIScience, remote-sensing and…
Aaag Review Process
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when you need to understand how the Annals of the American Association of Geographers evaluates a manuscript — double-anonymous review routed through a subject editor by…
Aaag Submission
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when running the final pre-submission preflight for the Annals of the American Association of Geographers via ScholarOne Manuscripts — area/article-type selection…
Related
Questions about Icml Experiments
What does Icml Experiments do?
A skill your agent uses when stress-testing ICML experimental evidence before submission or rebuttal, including strong tuned baselines, mechanism-isolating ablations, seed variance and confidence…. Icml Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when stress-testing ICML experimental evidence before submission or rebuttal, including strong tuned baselines, mechanism-isolating ablations, seed variance and confidence intervals, compute disclosure, data leakage and split construction, reproducibility, negative results, and fit to ICML soundness, originality, and significance scoring.
When should I use Icml Experiments?
Icml Experiments fits situations like: stress-testing ICML experimental evidence before submission; including strong tuned baselines; mechanism-isolating ablations; seed variance and confidence intervals.
How do I install Icml Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icml-experiments -a claude-code`. Or copy the skill folder (ICML-Skills/skills/icml-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/icml-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Icml Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icml-experiments -a codex`. Or copy the skill folder (ICML-Skills/skills/icml-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/icml-experiments in your project. Codex loads it when a task matches its description.
Can I use Icml Experiments in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icml-experiments -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/icml-experiments, .gemini/skills/icml-experiments, .github/skills/icml-experiments and .opencode/skills/icml-experiments in your project.
What does Icml Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Icml Experiments is instructions for the agent only.
Does Icml Experiments access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Icml Experiments safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Icml Experiments use?
Icml Experiments is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Icml Experiments use?
About 843 tokens (SKILL.md is roughly 3.4k 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 Icml Experiments?
Skills that share tags, products or a category with Icml Experiments: Academic Paper Reproduction Methodology (xjtulyc/MedgeClaw, 617 stars), Challenge (pedrohcgs/claude-code-my-workflow, 1.6k stars), Fcr Revision And Rebuttal (franklee16/academic-research-skills, 223 stars) and Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Icml Experiments?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,219 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.