Automl Skill
LeoYeAI/openclaw-master-skills
AutoML 自动化机器学习技能 | Automated Machine Learning Skill. An agent skill from LeoYeAI/openclaw-master-skills.
A skill your agent uses when designing or auditing experiments for an ACL paper, covering tuned LLM baselines, multi-dataset and multilingual evaluation, statistical significance and variance, human…
Install Acl Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acl-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills acl-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/ACL-Skills/skills/acl-experiments .claude/skills/acl-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 "acl-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-experiments into .claude/skills/acl-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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/ACL-Skills/skills/acl-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 acl-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills acl-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/ACL-Skills/skills/acl-experiments .agents/skills/acl-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 "acl-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-experiments into .agents/skills/acl-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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 acl-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills acl-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/ACL-Skills/skills/acl-experiments .cursor/skills/acl-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 "acl-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-experiments into .cursor/skills/acl-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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 ACL-Skills/skills/acl-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 acl-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills acl-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/ACL-Skills/skills/acl-experiments .gemini/skills/acl-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 "acl-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-experiments into .gemini/skills/acl-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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 acl-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 acl-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/ACL-Skills/skills/acl-experiments .github/skills/acl-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 "acl-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-experiments into .github/skills/acl-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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 acl-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 acl-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/ACL-Skills/skills/acl-experiments .opencode/skills/acl-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 "acl-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-experiments into .opencode/skills/acl-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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
acl-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 634 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 experiments for an ACL paper, covering tuned LLM baselines, multi-dataset and multilingual evaluation, statistical significance and variance, human…
- Works in 4 steps: Sample failures (100-200) from the… → Induce 4-8 functional error categories;… → Report category frequencies for your… → …
- Auditing experiments for an ACL paper
- SKILL.md covers Baseline honesty, Evaluation design, Statistical floor and Contamination and validity…, plus 6 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Acl Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing experiments for an ACL paper, covering tuned LLM baselines, multi-dataset and multilingual evaluation, statistical significance and variance, human evaluation with agreement reporting, contamination and prompt-sensitivity controls, ablations, and error-analysis expectations in NLP reviewing.
Its SKILL.md is about 1.4k 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 AI & LLM Engineering, covering Natural language processing and A/B testing. 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 experiments for an ACL paper
- Covering tuned LLM baselines
- Multi-dataset and multilingual evaluation
- Statistical significance and variance
Example prompts
- “/acl-experiments”
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Sample failures (100-200) from the strongest configuration.
- Induce 4-8 functional error categories; double-annotate a subset and
- Report category frequencies for your method vs the best baseline —
- Feed the two most persistent categories into Limitations.
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
Acl Experiments loads about 1.4k tokens when it runs. Until then it costs about 85 tokens; SKILL.md has 634 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). 634 words, ~1,426 tokens.
Download SKILL.mdSave it as
.claude/skills/acl-experiments/SKILL.md (or your agent's skills folder).- name
- acl-experiments
- description
- Use when designing or auditing experiments for an ACL paper, covering tuned LLM baselines, multi-dataset and multilingual evaluation, statistical significance and variance, human evaluation with agreement reporting, contamination and prompt-sensitivity controls, ablations, and error-analysis expectations in NLP reviewing.
ACL Experiments
Use this while the experimental story can still change. The ACL evidence bar is not "beats the baseline once": it is a defensible measurement of a language capability, with the failure modes examined.
Baseline honesty
- Include the strongest cheap baseline: a well-prompted current LLM has become mandatory context for most tasks — a method beating only pre-LLM systems invites the "does this matter now?" review.
- Tune baselines with the same care as your method (same search budget, same data); reviewers explicitly probe for asymmetric tuning.
- Report the trivial baselines (majority class, copy input, retrieval-only) when they contextualize how hard the task actually is.
Evaluation design
- Breadth must match the claim: a "general" claim needs multiple datasets; a cross-lingual claim needs typologically distinct languages, not three Romance neighbors.
- Automatic metrics need justification for generation tasks — pair n-gram or embedding metrics with human or LLM-judge evaluation, and validate any LLM-judge against human labels before leaning on it.
- Fix the evaluation protocol before final runs: dev-set peeking on the test set via repeated submissions is unreportable and unrepairable.
Statistical floor
| Result flavor | Required rigor at ACL |
|---|---|
| Small deltas between systems | Significance test (bootstrap/permutation) or overlapping-interval honesty |
| Fine-tuning results | Multiple seeds; mean and deviation in the table, defined in the caption |
| Prompted-LLM results | Multiple prompt paraphrases and/or samples; sensitivity range reported |
| Human evaluation | Raters per item, agreement statistic (e.g., Krippendorff's alpha), pay disclosed |
| Correlation claims (metrics) | Confidence intervals and comparison against existing metric correlations |
The Responsible NLP checklist (Section C) asks for descriptive statistics and error bars — an experiment plan that cannot fill Section C truthfully is incomplete by construction.
Contamination and validity controls
- Reason explicitly about test-set membership in pretraining data: release dates vs model cutoffs, overlap scans, or held-back fresh test items.
- Watch prompt leakage: few-shot exemplars drawn from the test distribution, instructions embedding label hints.
- For annotation-based data, quantify label quality before measuring models against it; models are now frequently better than noisy gold labels.
Ablations and the mechanism claim
- Each component the abstract credits needs an ablation row; each ablation row needs the same variance treatment as the headline number.
- Prefer ablations that test the explanation (e.g., "gains come from the retrieval step") over combinatorial component sweeps.
- Scale ablation: if a claim is "method X helps," show it at two model sizes or state the single-scale limitation explicitly.
Show full SKILL.md (245 more words)Show less
Error analysis as a deliverable
The distinctive ACL expectation: a quantitative error analysis with named categories.
- Sample failures (100-200) from the strongest configuration.
- Induce 4-8 functional error categories; double-annotate a subset and report agreement.
- Report category frequencies for your method vs the best baseline — where do gains actually come from?
- Feed the two most persistent categories into Limitations.
Pre-run design worksheet
Claim: <one sentence>
Datasets: <n, why these, language list>
Baselines: <incl. tuned LLM baseline + trivial floor>
Runs/variance: <seeds or prompt paraphrases; interval type>
Significance: <test, when applied>
Human eval: <items, raters, agreement plan, pay>
Contamination: <audit method>
Ablations: <component -> table row>
Error analysis:<sample size, category plan>Common evidence failures seen in ARR reviews
- Averaging over languages to hide that one language regressed — report the per-language block; reviewers open the appendix table first when a claim says "multilingual."
- Comparing your tuned method against baseline numbers copied from papers that used different preprocessing or splits.
- Treating an LLM judge as ground truth without reporting its agreement with humans on a calibration subset.
- Claiming efficiency without wall-clock, memory, or cost on matched hardware.
- Running the significance test only on the comparison that wins.
- Reporting the best seed as the headline and the mean in the appendix — reviewers call this out by name.
When compute is the constraint
- Pre-register (internally) which single configuration gets the full multi-seed treatment, and make it the headline setting.
- Use paired designs — same items, both systems — so smaller samples still yield tight comparisons and permutation tests apply cleanly.
- Prefer breadth at small scale plus depth at one large scale over a thin sweep of everything; state the choice in the setup section.
- Cache and release intermediate outputs so ablations re-score rather than re-run.
Output format
[Evidence verdict] convincing / thin / misaligned-with-claim
[Baseline gaps] <missing or under-tuned comparators>
[Statistical gaps] <variance/significance/agreement omissions>
[Validity threats] <contamination/leakage/label-quality>
[Highest-value next run] <one experiment>© 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 ACL-Skills/skills/acl-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Acl 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 |
|---|---|---|---|---|---|---|
| Acl Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Automl SkillLeoYeAI/openclaw-master-skills | 2.2k | — | ~3.6k | Automated safety check: Pass | MIT | |
| Explore Runlllllllama/RigorPilot-Skills | 497 | 2 repos | ~833 | Automated safety check: Pass | MIT | |
| Hugging Face TokenizersOrchestra-Research/AI-Research-SKILLs | 13k | 7 repos | ~3.4k | Automated safety check: Pass | MIT | |
| OpenMed Model Card Writermaziyarpanahi/openmed | 5.5k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| Gptqmodel Tokenizer NormalizationModelCloud/GPTQModel | 1.3k | — | ~1.1k | Automated safety check: Pass | Custom licence |
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Questions about Acl Experiments
What does Acl Experiments do?
A skill your agent uses when designing or auditing experiments for an ACL paper, covering tuned LLM baselines, multi-dataset and multilingual evaluation, statistical significance and variance, human…. Acl Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing experiments for an ACL paper, covering tuned LLM baselines, multi-dataset and multilingual evaluation, statistical significance and variance, human evaluation with agreement reporting, contamination and prompt-sensitivity controls, ablations, and error-analysis expectations in NLP reviewing.
When should I use Acl Experiments?
Acl Experiments fits situations like: auditing experiments for an ACL paper; covering tuned LLM baselines; multi-dataset and multilingual evaluation; statistical significance and variance.
How do I install Acl Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acl-experiments -a claude-code`. Or copy the skill folder (ACL-Skills/skills/acl-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/acl-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Acl Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acl-experiments -a codex`. Or copy the skill folder (ACL-Skills/skills/acl-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/acl-experiments in your project. Codex loads it when a task matches its description.
Can I use Acl 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 acl-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/acl-experiments, .gemini/skills/acl-experiments, .github/skills/acl-experiments and .opencode/skills/acl-experiments in your project.
What does Acl Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Acl Experiments is instructions for the agent only.
Does Acl 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 Acl 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 Acl Experiments use?
Acl 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 Acl Experiments use?
About 1.4k tokens (SKILL.md is roughly 5.7k 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 Acl Experiments?
Skills that share tags, products or a category with Acl Experiments: Automl Skill (LeoYeAI/openclaw-master-skills, 2.2k stars), Explore Run (lllllllama/RigorPilot-Skills, 497 stars), Hugging Face Tokenizers (Orchestra-Research/AI-Research-SKILLs, 13k stars) and OpenMed Model Card Writer (maziyarpanahi/openmed, 5.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Acl 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.