How do I install the Eacl Experiments skill?

Run npx skillmds add thedixitjain/eacl-experiments in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Eacl Experiments safe to use?

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Which AI agents work with Eacl Experiments?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Eacl Experiments free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Eacl Experiments?

thedixitjain (@thedixitjain) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Eacl Experiments by thedixitjain — Agent Skill

Name: Eacl Experiments
Rating: 4.2 (5 reviews)
Author: thedixitjain

Eacl Experiments

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.

thedixitjain fd0e094 3.6 KB Updated Sep 8, 2026 2 repo stars

File contents

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

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

[ ] 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

[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>

Source: brycewang-stanford/Awesome-Journal-Skills → EACL-Skills/skills/eacl-experiments/SKILL.md

---name: eacl-experimentsdescription: 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 ExperimentsUse this to make an EACL paper's evidence hold up under NLP review. EACL rewards **well-scopedquestions answered with careful controls** over leaderboard maximalism — its best papers includeanalyses and critiques, not only state-of-the-art systems (see`../../resources/exemplars/library.md`). Design the evidence to match the claim exactly, nobroader.## 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 — themorphology-across-57-languages exemplar shows the bar.## Significance and variance floor```textEvidence 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>```---**Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `EACL-Skills/skills/eacl-experiments/SKILL.md`

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