A skill your agent uses when a European Economic Review (EER) result must be shown to survive specification, sample, measurement, and inference changes — the robustness battery referees demand.

MITAuto-check passedResearch & Science

Install Eer Robustness

skills CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eer-robustness -a claude-code

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills eer-robustness --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/European-Economic-Review-Skills/skills/eer-robustness .claude/skills/eer-robustness && 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/

Facts

Skill name
eer-robustness
GitHub stars
1.2k
Token cost
~1.4k tokens
SKILL.md length
613 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when a European Economic Review (EER) result must be shown to survive specification, sample, measurement, and inference changes — the robustness battery referees demand.

  • Works in 5 steps: Pick the threats that could actually… → Lead with the most dangerous test, not… → Report a coefficient-stability table or… → …
  • A European Economic Review (EER) result must be shown to survive specification
  • SKILL.md covers When to trigger, The EER robustness bar, The robustness battery (choose… and How to organize it, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Eer Robustness is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when a European Economic Review (EER) result must be shown to survive specification, sample, measurement, and inference changes — the robustness battery referees demand. Builds the stress tests and organizes them; it does not establish the core identification or write the prose.

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 Research & Science, covering Load 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

  • A European Economic Review (EER) result must be shown to survive specification
  • Inference changes — the robustness battery referees demand

Example prompts

  • “/eer-robustness”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Pick the threats that could actually overturn the claim — tie each test to a specific objection.
  2. Lead with the most dangerous test, not the easiest one.
  3. Report a coefficient-stability table or specification curve so the reader sees the distribution of estimates.
  4. State the verdict honestly: "the estimate ranges X–Y across N specifications; it loses significance only when Z."
  5. Push the long tail to the Supplementary material, keep the load-bearing tests in-text.

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

Eer Robustness loads about 1.4k tokens when it runs. Until then it costs about 75 tokens; SKILL.md has 613 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~75
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

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). 613 words, ~1,439 tokens.

Download SKILL.mdSave it as .claude/skills/eer-robustness/SKILL.md (or your agent's skills folder).
name
eer-robustness
description
Use when a European Economic Review (EER) result must be shown to survive specification, sample, measurement, and inference changes — the robustness battery referees demand. Builds the stress tests and organizes them; it does not establish the core identification or write the prose.

Robustness & Sensitivity (eer-robustness)

When to trigger

  • The headline estimate exists but its fragility has not been probed
  • A referee (or co-author) suspects the result is driven by one sample/spec choice
  • Inference assumptions (clustering, dependence, multiple testing) are unexamined
  • A structural/quantitative result's sensitivity to parameters is not shown

The EER robustness bar

A general-interest result must be believable beyond the authors' favorite specification. EER referees — methods-aware under single-anonymized review — expect a disciplined battery, not a scattershot appendix: vary the things that could plausibly overturn the result, report them transparently, and say which (if any) move the estimate. The goal is a result that is robust where it matters and honest where it is fragile. Robustness is not infinite specification mining; choose tests with a reason.

The robustness battery (choose by design)

DimensionTestWhy it matters
Specificationadd/drop controls; alternative functional form; FE structureshows the estimate is not a control artifact
Sampleleave-one-out (unit/region/year); alternative windows; trimming outliersshows no single observation drives it
Measurementalternative outcome/treatment definitions; alternative data sourceshows it is not a coding choice
Estimatorheterogeneity-robust DiD vs TWFE; alternative IV/RDD bandwidthshows method-robustness
Inferenceclustering level; wild-cluster bootstrap (few clusters); spatial/cross-sectional dependence; randomization inferenceshows SEs are valid under real dependence
Multiple testingRomano–Wolf / Bonferroni–Holm across familiesguards against cherry-picked significance
Structuralparameter sensitivity; alternative calibration targets; grid/tuningshows quantity is not a tuning artifact
Pre-trendshonest-DiD sensitivity (Rambachan–Roth); placebo timingbounds violations of parallel trends

How to organize it

  1. Pick the threats that could actually overturn the claim — tie each test to a specific objection.
  2. Lead with the most dangerous test, not the easiest one.
  3. Report a coefficient-stability table or specification curve so the reader sees the distribution of estimates.
  4. State the verdict honestly: "the estimate ranges X–Y across N specifications; it loses significance only when Z."
  5. Push the long tail to the Supplementary material, keep the load-bearing tests in-text.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. EER is a general economics field journal; the DiD/IV/RDD chain serves its applied lane.

  • Many outcomes / specifications: romano_wolf (step-down FWER) or benjamini_hochberg.
  • OVB sensitivity: oster_delta / sensemakr.
  • Inference: wild_cluster_bootstrap (few clusters), twoway_cluster / conley.
  • Re-fit off one handle: audit_result(result_id) lists missing checks + the exact suggest_function for each.
  • Exhibits: etable / did_summary_to_latex from the handle — no retyped numbers.

Decisive checks in the body, exhaustive battery in the appendix. JF execution walkthrough.

Show full SKILL.md (210 more words)Show less

Checklist

  • Each robustness test is tied to a named objection (not decorative)
  • Sample robustness: leave-one-out and alternative windows shown
  • Inference robustness: clustering justified; few-cluster / dependence handled
  • Estimator robustness: modern vs naive estimator agree (or the gap is explained)
  • Multiple-testing correction where several outcomes are tested
  • Structural: parameter/calibration sensitivity reported
  • A coefficient-stability table or spec curve summarizes the distribution
  • Fragilities stated honestly, not hidden

Anti-patterns

  • A robustness appendix that only adds controls and never threatens the result
  • Reporting 20 specs that all "confirm" the result while omitting the one that breaks it
  • Clustering at a convenient level to shrink standard errors
  • Specification mining presented as robustness (no rationale per test)
  • Burying a fragility the referee will find anyway — better to disclose and bound it
  • Significance stars substituting for a coefficient-stability view

Worked vignette (illustrative)

An IO paper finds a merger raised prices 4%. A weak appendix re-runs with more controls. An EER battery: leave-one-market-out (range 3.1–4.6%, illustrative), alternative price index, synthetic-control placebo on untreated markets, wild-cluster bootstrap (28 markets), and a Romano–Wolf correction across the three outcomes. Verdict stated plainly: "the price effect is 3.1–4.6% and significant in all but the trimmed-outlier sample, where it is 2.0% (s.e. 1.1)." The reader trusts the number because its fragility was mapped.

Output format

【Core claim under test】one sentence
【Threats probed】[spec / sample / measurement / estimator / inference / MHT / structural]
【Most dangerous test + result】[...]
【Estimate range across specs】X–Y (where it breaks: Z)
【Honest fragilities】[...]
【Next step】eer-tables-figures (present the battery) or eer-referee-strategy

© 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 European-Economic-Review-Skills/skills/eer-robustness of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

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Data Finderbrycewang-stanford/Auto-Empirical-Research-Skills4.6k—~1.7kAutomated safety check: PassCustom licence
Weakness Scannerflonat/flonat-research146—~1.5kAutomated safety check: PassMIT
Ecta Identificationfranklee16/academic-research-skills2231 repos~1.9kAutomated safety check: PassNone

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Questions about Eer Robustness

What does Eer Robustness do?

A skill your agent uses when a European Economic Review (EER) result must be shown to survive specification, sample, measurement, and inference changes — the robustness battery referees demand. Eer Robustness is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when a European Economic Review (EER) result must be shown to survive specification, sample, measurement, and inference changes — the robustness battery referees demand.

When should I use Eer Robustness?

Eer Robustness fits situations like: A European Economic Review (EER) result must be shown to survive specification; inference changes — the robustness battery referees demand.

How do I install Eer Robustness in Claude Code?

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

How do I install Eer Robustness in Codex?

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

Can I use Eer Robustness 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 eer-robustness -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/eer-robustness, .gemini/skills/eer-robustness, .github/skills/eer-robustness and .opencode/skills/eer-robustness in your project.

What does Eer Robustness need to run?

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

Does Eer Robustness 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 Eer Robustness 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 Eer Robustness use?

Eer Robustness 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 Eer Robustness use?

About 1.4k tokens (SKILL.md is roughly 5.8k 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 Eer Robustness?

Skills that share tags, products or a category with Eer Robustness: What If Oracle (K-Dense-AI/scientific-agent-skills, 48k stars), Paper Review (EvoScientist/EvoSkills, 478 stars), Data Finder (brycewang-stanford/Auto-Empirical-Research-Skills, 4.6k stars) and Weakness Scanner (flonat/flonat-research, 146 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Eer Robustness?

brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 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.