Agent skill

Eacl Experiments

by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills

A skill your agent uses when designing or auditing the empirical evidence for an EACL paper, covering tuned and LLM baselines, multilingual breadth matched to the claim, significance and variance…

MITAuto-check passed

Install Eacl Experiments

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eacl-experiments -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills eacl-experiments --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/EACL-Skills/skills/eacl-experiments .claude/skills/eacl-experiments && rm -rf skills-src

Use ~/.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/

Paste into Claude Code

Install the "eacl-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EACL-Skills/skills/eacl-experiments into .claude/skills/eacl-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eacl-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

$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EACL-Skills/skills/eacl-experiments

Type 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.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eacl-experiments -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills eacl-experiments --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/EACL-Skills/skills/eacl-experiments .agents/skills/eacl-experiments && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "eacl-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EACL-Skills/skills/eacl-experiments into .agents/skills/eacl-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eacl-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.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eacl-experiments -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills eacl-experiments --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/EACL-Skills/skills/eacl-experiments .cursor/skills/eacl-experiments && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "eacl-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EACL-Skills/skills/eacl-experiments into .cursor/skills/eacl-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eacl-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

$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path EACL-Skills/skills/eacl-experiments

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eacl-experiments -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills eacl-experiments --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/EACL-Skills/skills/eacl-experiments .gemini/skills/eacl-experiments && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "eacl-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EACL-Skills/skills/eacl-experiments into .gemini/skills/eacl-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eacl-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.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills eacl-experiments

Installs 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).

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eacl-experiments -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/EACL-Skills/skills/eacl-experiments .github/skills/eacl-experiments && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "eacl-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EACL-Skills/skills/eacl-experiments into .github/skills/eacl-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eacl-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.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eacl-experiments -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills eacl-experiments --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/EACL-Skills/skills/eacl-experiments .opencode/skills/eacl-experiments && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "eacl-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EACL-Skills/skills/eacl-experiments into .opencode/skills/eacl-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eacl-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: eacl-experiments
GitHub stars: 1.2k
Token cost: ~882 tokens
SKILL.md length: 284 words
Files: 1
Skills in repo: 2,387
Repo updated: Sep 27, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

A skill your agent uses when designing or auditing the empirical evidence for an EACL paper, covering tuned and LLM baselines, multilingual breadth matched to the claim, significance and variance…

Auditing the empirical evidence for an EACL paper
SKILL.md covers Baselines that make a…, Match breadth to the claim, Significance and variance floor and Contamination controls, plus 4 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
Covering tuned and LLM baselines

What it does

Eacl Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the empirical evidence for an EACL paper, covering tuned and LLM baselines, multilingual breadth matched to the claim, significance and variance floors, human-evaluation agreement, data-contamination controls, ablations, and error taxonomies, so that every stated result is measured rather than asserted.

Its SKILL.md is about 880 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

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 the empirical evidence for an EACL paper
Covering tuned and LLM baselines
Multilingual breadth matched to the claim
Significance and variance floors

Example prompts

“/eacl-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

Eacl Experiments loads about 882 tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 284 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~88
When it runs · the whole SKILL.md, loaded when a task matches: ~882

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). 284 words, ~882 tokens.

Download SKILL.mdSave it as .claude/skills/eacl-experiments/SKILL.md (or your agent's skills folder).

name: eacl-experiments
description: Use when designing or auditing the empirical evidence for an EACL paper, covering tuned and LLM baselines, multilingual breadth matched to the claim, significance and variance floors, human-evaluation agreement, data-contamination controls, ablations, and error taxonomies, so that every stated result is measured rather than asserted.

EACL Experiments

Use this to make an EACL paper's evidence hold up under NLP review. EACL rewards well-scoped questions answered with careful controls over leaderboard maximalism — its best papers include analyses and critiques, not only state-of-the-art systems (see ../../resources/exemplars/library.md). Design the evidence to match the claim exactly, no broader.

Baselines that make a comparison fair

Include a tuned baseline, not a strawman: an under-tuned competitor makes a win meaningless. State the search space for both your method and the baselines.
For LLM-based work, include the obvious prompt/few-shot baseline and report its prompts and decoding settings; a gain over an unreported baseline is not credible.

Match breadth to the claim

Claim	Required breadth
"Works for language L"	Solid results on L, honestly scoped
"Cross-lingual / multilingual"	Enough languages across resource levels; per-language results
"General method"	Multiple tasks/datasets, not one convenient benchmark
"Robust"	Stress tests / shifts, not just in-distribution

A multilingual claim backed by two high-resource languages is the classic EACL over-reach — the morphology-across-57-languages exemplar shows the bar.

Significance and variance floor

text

Evidence floor for a headline comparison:
  seeds:        >= 3-5 runs
  report:       mean +/- CI (or std), never a lone run
  significance: a test when systems are close
  ablations:    isolate each component's contribution

Contamination controls

For any benchmark evaluated with LLMs, address whether the test data could have leaked. Report an overlap/decontamination check where feasible, or bound the risk honestly for closed models. This is a live EACL concern, not a formality.

Human evaluation done properly

If human judgments are a result, report the number of annotators, guidelines, pay, and inter-annotator agreement — an unmeasured human eval is a soft target for reviewers.
Release the annotation materials (see eacl-artifact-evaluation).

Error analysis as a first-class result

A quantified error taxonomy ("X% agreement errors, Y% named-entity errors, examples in Table N") often carries more scientific weight than another decimal of accuracy, and plays to EACL's analysis-friendly reviewing.

Audit checklist

text

[ ] Baselines tuned, search spaces stated
[ ] LLM baselines with verbatim prompts + decoding
[ ] Breadth matches the claim (per-language results if multilingual)
[ ] >= 3-5 seeds; variance/CIs reported
[ ] Significance test where systems are close
[ ] Ablations isolate each component
[ ] Contamination addressed
[ ] Human eval: annotators, agreement, pay reported
[ ] Error analysis quantified

Output format

text

[Evidence strength] Strong / Adequate / Weak
[Baseline fairness] <tuned? LLM baseline reported?>
[Breadth vs claim] <matched / over-reaching>
[Variance + significance] <seeds, CIs, tests>
[Contamination + human eval] <controls present?>
[Fix order] <experiments to add/scope before the cycle deadline>

Files

Just SKILL.md in EACL-Skills/skills/eacl-experiments of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Eacl 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.

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Questions about Eacl Experiments

What does Eacl Experiments do?

A skill your agent uses when designing or auditing the empirical evidence for an EACL paper, covering tuned and LLM baselines, multilingual breadth matched to the claim, significance and variance…. Eacl Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the empirical evidence for an EACL paper, covering tuned and LLM baselines, multilingual breadth matched to the claim, significance and variance floors, human-evaluation agreement, data-contamination controls, ablations, and error taxonomies, so that every stated result is measured rather than asserted.

When should I use Eacl Experiments?

Eacl Experiments fits situations like: auditing the empirical evidence for an EACL paper; covering tuned and LLM baselines; multilingual breadth matched to the claim; significance and variance floors.

How do I install Eacl Experiments in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eacl-experiments -a claude-code`. Or copy the skill folder (EACL-Skills/skills/eacl-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/eacl-experiments in your project. Claude Code loads it when a task matches its description.

How do I install Eacl Experiments in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eacl-experiments -a codex`. Or copy the skill folder (EACL-Skills/skills/eacl-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/eacl-experiments in your project. Codex loads it when a task matches its description.

Can I use Eacl 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 eacl-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/eacl-experiments, .gemini/skills/eacl-experiments, .github/skills/eacl-experiments and .opencode/skills/eacl-experiments in your project.

What does Eacl Experiments need to run?

SKILL.md names no scripts, command-line tools or credentials: Eacl Experiments is instructions for the agent only.

Does Eacl 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 Eacl 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 Eacl Experiments use?

Eacl 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 Eacl Experiments use?

About 882 tokens (SKILL.md is roughly 3.5k 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 Eacl Experiments?

Skills that share tags, products or a category with Eacl Experiments: Design Audit Against Rams' Principles (thedotmack/claude-mem, 99k stars), Experiment Audit (wanshuiyin/Auto-claude-code-research-in-sleep, 17k stars), Experiment Audit (wanshuiyin/Auto-claude-code-research-in-sleep, 17k stars) and Experiment Designer (alirezarezvani/claude-skills, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Eacl Experiments?

brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,228 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.

Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Eacl Experiments

Facts

At a glance

What it does

When your agent uses it

Example prompts

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

EACL Experiments

Baselines that make a comparison fair

Match breadth to the claim

Significance and variance floor

Contamination controls

Human evaluation done properly

Error analysis as a first-class result

Audit checklist

Output format

Files

Compare with similar skills

Similar skills

More from brycewang-stanford/Awesome-Journal-Skills

Questions about Eacl Experiments