New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Expecon Robustness

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

A skill your agent uses when an Experimental Economics (ExpEcon) result may be a power artifact, multiple-comparisons artifact, or sensitive to the inference unit, exclusions, or design choices.

MITAuto-check passedResearch & Science

Install Expecon Robustness

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

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills expecon-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/Experimental-Economics-Skills/skills/expecon-robustness .claude/skills/expecon-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
expecon-robustness
GitHub stars
1.2k
Token cost
~2k tokens
SKILL.md length
914 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when an Experimental Economics (ExpEcon) result may be a power artifact, multiple-comparisons artifact, or sensitive to the inference unit, exclusions, or design choices.

  • Works in 5 steps: Power / sample-size justification (do… → The unit-of-observation problem → Multiple treatments & multiple hypotheses → …
  • An Experimental Economics (ExpEcon) result may be a power artifact
  • SKILL.md covers When to trigger, The ExpEcon inference stack, Execution bridge (StatsPAI /… and Checklist, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Expecon Robustness is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when an Experimental Economics (ExpEcon) result may be a power artifact, multiple-comparisons artifact, or sensitive to the inference unit, exclusions, or design choices. Hardens the statistical case; it does not design the experiment or draft prose.

Its SKILL.md is about 2k 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. 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

  • An Experimental Economics (ExpEcon) result may be a power artifact
  • Multiple-comparisons artifact
  • Sensitive to the inference unit

Example prompts

  • “/expecon-robustness”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Power / sample-size justification (do this before data, defend it after)
  2. The unit-of-observation problem
  3. Multiple treatments & multiple hypotheses
  4. Non-parametric vs. parametric
  5. Robustness to design and analysis choices

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

Expecon Robustness loads about 2k tokens when it runs. Until then it costs about 68 tokens; SKILL.md has 914 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~68
When it runs · the whole SKILL.md, loaded when a task matches
~2k

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). 914 words, ~1,997 tokens.

Download SKILL.mdSave it as .claude/skills/expecon-robustness/SKILL.md (or your agent's skills folder).
name
expecon-robustness
description
Use when an Experimental Economics (ExpEcon) result may be a power artifact, multiple-comparisons artifact, or sensitive to the inference unit, exclusions, or design choices. Hardens the statistical case; it does not design the experiment or draft prose.

Robustness & Inference (expecon-robustness)

When to trigger

  • A referee asks "is the study adequately powered?" and there is no sample-size justification
  • You ran several treatments / outcomes and report many p-values without correction
  • Inference treats individual decisions as independent when subjects interact in groups
  • Results move when you change the exclusion rule, the outcome measure, or pool/unpool sessions

The ExpEcon inference stack

Experimental control buys clean identification; it does not buy clean inference. Five things separate a robust ExpEcon paper from a fragile one.

1. Power / sample-size justification (do this before data, defend it after)
  • Pre-specify the minimum detectable effect (MDE) that is economically meaningful and the power to detect it, at the correct unit (session or matching group, not individual decision). A study powered on individual n but analyzed at the group level is overstated.
  • Use pilot or prior-literature variances; for interactive games, simulate at the group level. Report the realized power for the primary comparison, not just a post-hoc "we found p<0.05."
  • A clean, well-powered null is publishable here — especially as a Registered Report. Do not p-hack a null into significance; defend it with power.
2. The unit-of-observation problem
  • Within a matching group, decisions are correlated; the independent unit is the group/session, often giving far fewer effective observations than the raw decision count suggests.
  • Use group-level summaries, cluster-robust SEs at the session/matching-group level, mixed models with group random effects, or non-parametric tests on group means (Mann–Whitney / permutation). With few clusters, prefer randomization-inference / permutation tests over asymptotic clustering.
3. Multiple treatments & multiple hypotheses
  • If you test several outcomes or several pairwise treatment contrasts, correct for multiplicity (Holm, Romano–Wolf, List–Shaikh–Xu for experiments, or pre-registered families). Distinguish the primary pre-registered comparison (no correction needed if it is the single confirmatory test) from secondary/exploratory ones (correct, and label exploratory).
  • Report the pre-registered analysis first, exactly as specified; report deviations and additional analyses separately and labeled.
4. Non-parametric vs. parametric
  • Experimental outcomes are often bounded, censored (contributions in [0,20]), or non-normal. Lead with non-parametric tests on the primary comparison; use regression for covariate adjustment and heterogeneity, not to rescue a fragile mean difference.
5. Robustness to design and analysis choices
  • Show the effect survives: alternative exclusion rules (comprehension failers in/out), first-half vs. second-half rounds (learning), partner vs. stranger if both run, and a permutation test of the treatment label.
  • Report attrition and, in field/online studies, differential attrition (Lee bounds if it threatens balance).

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. Experimental Economics is lab/field experiments; randomization inference, romano_wolf for many treatments/outcomes, and power are decisive — observational tools secondary.

  • 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.

Checklist

  • Sample size justified via MDE + power at the group/session unit; realized power reported
  • Inference uses the correct independent unit (cluster-robust / group-means / RI), not raw decision n
  • Few-cluster inference handled (permutation / randomization inference) when sessions are few
  • Multiplicity corrected across outcomes/contrasts; primary vs. exploratory clearly separated
  • Primary analysis matches the pre-analysis plan exactly; deviations flagged
  • Non-parametric test leads for the bounded/non-normal primary outcome
  • Robustness to exclusions, learning (round halves), and order shown; attrition reported
Show full SKILL.md (356 more words)Show less

Anti-patterns

  • "We found a significant effect" with no power analysis and a tiny number of independent groups
  • Per-decision n inflating significance when subjects are in repeated, interacting groups
  • A cherry-picked significant contrast among many, uncorrected and unlabeled as exploratory
  • Switching the primary outcome or exclusion rule after seeing results, without disclosure
  • Parametric t-tests on heavily censored contribution data as the only evidence
  • Declaring a null "no effect" when the design never had power to detect a meaningful effect

Worked vignette (illustrative)

A public-goods paper runs 4 treatments × 3 outcomes and reports 7 significant tests. The fix: declare the single pre-registered primary contrast (cooperation under punishment vs. no-punishment) tested at the matching-group level via a permutation test on group means (say 12 groups/arm), then apply Romano–Wolf across the remaining family and label the rest exploratory. The headline survives correction (illustrative p=0.004); two secondary "effects" do not and are reported honestly as exploratory.

Referee pushback mapped to the fix

  • "How many independent observations do you actually have?" → Count groups/sessions, not decisions; report inference at that unit and the realized power for it.
  • "You ran a fishing expedition." → Declare the single pre-registered primary test; correct the rest (Romano–Wolf/Holm) and label them exploratory.
  • "Few clusters — your SEs are unreliable." → Use randomization inference / permutation tests on group means rather than asymptotic clustering.
  • "The null is uninformative." → Report the MDE; show the design had power to detect a meaningful effect, so the null is evidence of absence, not absence of evidence.
  • "Results depend on dropping subjects." → Show the effect with and without comprehension failers and state the pre-specified rule.

A minimal robustness panel to pre-build

  1. Primary test at the group/session unit (non-parametric lead).
  2. Same test with comprehension failers included vs. excluded.
  3. First-half vs. second-half rounds (learning).
  4. Permutation/randomization-inference p-value on the treatment label.
  5. Multiplicity-corrected family of secondary contrasts, labeled exploratory.
  6. Attrition table (and differential attrition / Lee bounds in field-online designs).

If all six point the same way, the result is robust in the sense ExpEcon referees mean; if (3) or (4) flips it, the headline is fragile and you learned that before a referee did.

Output format

text
【Journal】Experimental Economics (ESA method flagship)
【Skill】expecon-robustness
【Verdict】robust / fragile / underpowered
【Power】MDE + power at group/session unit; realized power
【Inference unit】group-means / cluster-robust / randomization inference
【Multiplicity】correction used; primary vs. exploratory split
【Design robustness】exclusions / learning halves / order / attrition
【Next skill】expecon-tables-figures

© 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 Experimental-Economics-Skills/skills/expecon-robustness of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Expecon Robustness 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.

Expecon Robustness compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Expecon Robustness this skillbrycewang-stanford/Awesome-Journal-Skills1.2k—~2kAutomated safety check: PassMIT
Hypothesis Generationspacering-net/codeg3.9k14 repos~3.6kAutomated safety check: NotesMIT
GitHub Deep Researchbytedance/deer-flow84k4 repos~1.3kAutomated safety check: PassMIT
Nature Paper CardYuan1z0825/nature-skills47k2 repos~2.1kAutomated safety check: PassApache-2.0
Content Research Writerweapp-tailwindcss/weapp-tailwindcss1.9k25 repos~3.5kAutomated safety check: PassMIT
Last30daysmvanhorn/last30days-skill64k—~7.9kAutomated safety check: NotesMIT

Similar skills

  • Hypothesis Generation

    spacering-net/codeg

    Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.

    3.9k GitHub starsUsed in 14 repos~3.6k tokens
    Research & ScienceAuto-check: notes

  • GitHub Deep Research

    bytedance/deer-flow

    Researches a GitHub repository over four rounds using the GitHub API and web search, then writes a structured markdown report with timeline, metrics and Mermaid diagrams.

    84k GitHub starsUsed in 4 repos~1.3k tokens
    Research & ScienceAuto-check passed

  • Nature Paper Card

    Yuan1z0825/nature-skills

    Builds a structured deep-reading card for one scientific paper, covering methods, how experiments support claims, limitations and research ideas, with a script to prepare the source.

    47k GitHub starsUsed in 2 repos~2.1k tokens
    Research & ScienceAuto-check passed

  • Content Research Writer

    weapp-tailwindcss/weapp-tailwindcss

    Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section.

    1.9k GitHub starsUsed in 25 repos~3.5k tokens
    Research & ScienceAuto-check passed

  • Last30days

    mvanhorn/last30days-skill

    Research what people actually say about any topic in the last 30 days.

    64k GitHub stars~7.9k tokensUpdated today
    Research & ScienceAuto-check: notes

  • Peer Review

    spacering-net/codeg

    Structured manuscript/grant review with checklist-based evaluation.

    3.9k GitHub starsUsed in 17 repos~5.9k tokens
    Research & ScienceAuto-check: notes

More from brycewang-stanford/Awesome-Journal-Skills

All 2,387 skills in this repo

  • Aaag Data Analysis

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when running and reporting the analysis for an Annals of the American Association of Geographers manuscript — spatial statistics and modeling, remote-sensing accuracy, or…

    1.2k GitHub stars~1.3k tokensUpdated 13 days ago
    Auto-check passed

  • Aaag Literature Positioning

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when positioning an Annals of the American Association of Geographers manuscript in the literature — engaging geographic scholarship across the relevant area and the…

    1.2k GitHub stars~1.3k tokensUpdated 13 days ago
    Auto-check passed

  • Aaag Rebuttal

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when responding to an Annals of the American Association of Geographers decision letter (major/minor revision) — building a point-by-point response to the subject editor and…

    1.2k GitHub stars~1.4k tokensUpdated 13 days ago
    Auto-check passed

  • Aaag Research Design

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when defending the research design of an Annals of the American Association of Geographers manuscript — spatial/quantitative analysis and GIScience, remote-sensing and…

    1.2k GitHub stars~1.4k tokensUpdated 13 days ago
    Auto-check passed

  • Aaag Review Process

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when you need to understand how the Annals of the American Association of Geographers evaluates a manuscript — double-anonymous review routed through a subject editor by…

    1.2k GitHub stars~1.3k tokensUpdated 13 days ago
    Auto-check passed

  • Aaag Submission

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when running the final pre-submission preflight for the Annals of the American Association of Geographers via ScholarOne Manuscripts — area/article-type selection…

    1.2k GitHub stars~1.6k tokensUpdated 13 days ago
    Auto-check passed

Categories

Questions about Expecon Robustness

What does Expecon Robustness do?

A skill your agent uses when an Experimental Economics (ExpEcon) result may be a power artifact, multiple-comparisons artifact, or sensitive to the inference unit, exclusions, or design choices. Expecon Robustness is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when an Experimental Economics (ExpEcon) result may be a power artifact, multiple-comparisons artifact, or sensitive to the inference unit, exclusions, or design choices.

When should I use Expecon Robustness?

Expecon Robustness fits situations like: an Experimental Economics (ExpEcon) result may be a power artifact; multiple-comparisons artifact; sensitive to the inference unit.

How do I install Expecon Robustness in Claude Code?

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

How do I install Expecon Robustness in Codex?

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

Can I use Expecon 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 expecon-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/expecon-robustness, .gemini/skills/expecon-robustness, .github/skills/expecon-robustness and .opencode/skills/expecon-robustness in your project.

What does Expecon Robustness need to run?

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

Does Expecon 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 Expecon 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 Expecon Robustness use?

Expecon 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 Expecon Robustness use?

About 2k tokens (SKILL.md is roughly 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 Expecon Robustness?

Skills that share tags, products or a category with Expecon Robustness: Hypothesis Generation (spacering-net/codeg, 3.9k stars), GitHub Deep Research (bytedance/deer-flow, 84k stars), Nature Paper Card (Yuan1z0825/nature-skills, 47k stars) and Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Expecon 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.