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A skill your agent uses when designing or auditing ICLR experiments, including baselines, ablations, scaling laws, robustness, statistics, benchmarks, human evaluation, and compute reporting.
Install Iclr Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iclr-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iclr-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/ICLR-Skills/skills/iclr-experiments .claude/skills/iclr-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 "iclr-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICLR-Skills/skills/iclr-experiments into .claude/skills/iclr-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iclr-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/ICLR-Skills/skills/iclr-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 iclr-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iclr-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/ICLR-Skills/skills/iclr-experiments .agents/skills/iclr-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 "iclr-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICLR-Skills/skills/iclr-experiments into .agents/skills/iclr-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iclr-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 iclr-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iclr-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/ICLR-Skills/skills/iclr-experiments .cursor/skills/iclr-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 "iclr-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICLR-Skills/skills/iclr-experiments into .cursor/skills/iclr-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iclr-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 ICLR-Skills/skills/iclr-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 iclr-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iclr-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/ICLR-Skills/skills/iclr-experiments .gemini/skills/iclr-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 "iclr-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICLR-Skills/skills/iclr-experiments into .gemini/skills/iclr-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iclr-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 iclr-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 iclr-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/ICLR-Skills/skills/iclr-experiments .github/skills/iclr-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 "iclr-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICLR-Skills/skills/iclr-experiments into .github/skills/iclr-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iclr-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 iclr-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 iclr-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/ICLR-Skills/skills/iclr-experiments .opencode/skills/iclr-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 "iclr-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICLR-Skills/skills/iclr-experiments into .opencode/skills/iclr-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iclr-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
iclr-experiments- GitHub stars
- 1.2k
- Token cost
- ~927 tokens
- SKILL.md length
- 401 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when designing or auditing ICLR experiments, including baselines, ablations, scaling laws, robustness, statistics, benchmarks, human evaluation, and compute reporting.
- Auditing ICLR experiments
- SKILL.md covers Experiment audit, Reviewer questions to pre-answer, What ICLR reviewers reward in… and Worked vignette, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Including baselines
What it does
Iclr Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing ICLR experiments, including baselines, ablations, scaling laws, robustness, statistics, benchmarks, human evaluation, and compute reporting. Use when a reviewer questions whether a representation-learning or model gain is real, when you must isolate one mechanism with an ablation, or when preparing a small compute-matched control that can be posted inline during the public discussion period.
Its SKILL.md is about 930 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 Data & Analytics, covering Statistics. 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
- Auditing ICLR experiments
- Including baselines
- Human evaluation
- Compute reporting
Example prompts
- “/iclr-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
Iclr Experiments loads about 927 tokens when it runs. Until then it costs about 111 tokens; SKILL.md has 401 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). 401 words, ~927 tokens.
Download SKILL.mdSave it as
.claude/skills/iclr-experiments/SKILL.md (or your agent's skills folder).- name
- iclr-experiments
- description
- Use when designing or auditing ICLR experiments, including baselines, ablations, scaling laws, robustness, statistics, benchmarks, human evaluation, and compute reporting. Use when a reviewer questions whether a representation-learning or model gain is real, when you must isolate one mechanism with an ablation, or when preparing a small compute-matched control that can be posted inline during the public discussion period.
ICLR Experiments
Use this before submission or during a revision pass to stress-test empirical claims. ICLR experiments should answer the scientific question, not merely assemble a leaderboard.
Experiment audit
- Match each experiment to a claim in the introduction.
- Compare against current strong baselines, open-source systems, and the most relevant recent OpenReview/arXiv papers.
- Add ablations that isolate one mechanism at a time.
- Report variance across seeds or runs when randomness can change conclusions.
- Include robustness checks for dataset shift, prompt changes, architecture variants, hyperparameter sensitivity, or compute scale when those affect the claim.
- State compute budget, hardware, training time, inference cost, and environmental or access limits where relevant.
- For human evaluation, document task, annotator instructions, aggregation, quality control, and IRB or ethics status when needed.
Reviewer questions to pre-answer
- Is the baseline tuned fairly?
- Does the method win because of more compute, data, parameters, or prompt search?
- Does the effect persist outside the easiest benchmark?
- Are negative results hidden?
- Can a reviewer reproduce the headline table from the supplement or artifact?
What ICLR reviewers reward in evidence
ICLR's empirical culture prizes honest ablations and mechanism over leaderboard position. A clean ablation that explains why a representation works often outscores a larger raw number.
| Claim type | Evidence that convinces ICLR reviewers | Common reject trigger |
|---|---|---|
| New objective helps | Ablate the objective with everything else fixed | Gains confounded with extra tuning |
| Method scales | Several model sizes/tasks with a trend | One large run, no scaling curve |
| Robust representation | Tests across shifts, seeds, prompts | Single-seed peak on one benchmark |
| Beats prior method | Tuned, current, open-source baseline | Stale or under-tuned baseline |
Show full SKILL.md (137 more words)Show less
Worked vignette
A paper claims a new self-supervised pretext task yields better linear-probe accuracy. Reviewers ask whether the gain is the pretext task or simply longer pretraining. The author audit: hold total pretraining compute fixed, swap only the pretext objective, and report linear-probe accuracy with error bars over five seeds. The compute-matched ablation isolates the mechanism and is small enough to post inline during discussion, where the table becomes part of the permanent public record.
Reviewer-pushback patterns
- "You win because of more compute." Add a compute-matched control; report FLOPs, not just wall time.
- "Only one seed." Report mean and spread across seeds; an unstable benchmark needs variance.
- "Baseline is weak." Cite the baseline's own recommended settings and show you matched them.
- "Ablation removes two things at once." Split into single-mechanism ablations a reviewer can read.
Output format
[Claim] <paper claim>
[Experiment evidence] sufficient / needs baseline / needs ablation / needs robustness
[Fairness issue] <compute, tuning, data, prompt, metric>
[Fast fix] <experiment or analysis feasible before deadline>
[Appendix placement] <what can move out of main text>© 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 ICLR-Skills/skills/iclr-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Iclr 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 |
|---|---|---|---|---|---|---|
| Iclr Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~927 | Automated safety check: Pass | MIT | |
| Sandbox Benchvercel/next.js | 143k | — | ~4.1k | Automated safety check: Pass | MIT | |
| Statistical Analysisspacering-net/codeg | 3.8k | 4 repos | ~5k | Automated safety check: Pass | MIT | |
| StatsmodelszLanqing/codex-claude-academic-skills | 4.6k | 16 repos | ~4.9k | Automated safety check: Pass | BSD-3-Clause | |
| Statistical Powerspacering-net/codeg | 3.8k | 2 repos | ~3.6k | Automated safety check: Notes | MIT | |
| AI Daily DigestvigorX777/ai-daily-digest | 1.6k | — | ~1.3k | Automated safety check: Pass | None |
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Questions about Iclr Experiments
What does Iclr Experiments do?
A skill your agent uses when designing or auditing ICLR experiments, including baselines, ablations, scaling laws, robustness, statistics, benchmarks, human evaluation, and compute reporting. Iclr Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing ICLR experiments, including baselines, ablations, scaling laws, robustness, statistics, benchmarks, human evaluation, and compute reporting.
When should I use Iclr Experiments?
Iclr Experiments fits situations like: auditing ICLR experiments; including baselines; human evaluation; compute reporting.
How do I install Iclr Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iclr-experiments -a claude-code`. Or copy the skill folder (ICLR-Skills/skills/iclr-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/iclr-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Iclr Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iclr-experiments -a codex`. Or copy the skill folder (ICLR-Skills/skills/iclr-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/iclr-experiments in your project. Codex loads it when a task matches its description.
Can I use Iclr 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 iclr-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/iclr-experiments, .gemini/skills/iclr-experiments, .github/skills/iclr-experiments and .opencode/skills/iclr-experiments in your project.
What does Iclr Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Iclr Experiments is instructions for the agent only.
Does Iclr 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 Iclr 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 Iclr Experiments use?
Iclr 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 Iclr Experiments use?
About 927 tokens (SKILL.md is roughly 3.7k 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 Iclr Experiments?
Skills that share tags, products or a category with Iclr Experiments: Sandbox Bench (vercel/next.js, 143k stars), Statistical Analysis (spacering-net/codeg, 3.8k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.6k stars) and Statistical Power (spacering-net/codeg, 3.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Iclr 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.