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Agent skill

Jhe Robustness

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

A skill your agent uses when a Journal of Health Economics (JHE) result must be shown stable to specification, sample, inference, and mechanism threats before submission or in response to referees.

MITAuto-check passedResearch & Science

Install Jhe Robustness

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

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

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

At a glance

A skill your agent uses when a Journal of Health Economics (JHE) result must be shown stable to specification, sample, inference, and mechanism threats before submission or in response to referees.

  • Works in 6 steps: Lead with the threat the editor/referee… → Report stability, not stars. Show the… → Right-size the spending/skew problem.… → …
  • A Journal of Health Economics (JHE) result must be shown stable to specification
  • SKILL.md covers When to trigger, Robustness the JHE way:…, Threat-to-check ledger and Sequencing, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Jhe Robustness is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when a Journal of Health Economics (JHE) result must be shown stable to specification, sample, inference, and mechanism threats before submission or in response to referees. Organizes robustness by identifying threat; it does not establish the design (jhe-identification) or build exhibits.

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

  • A Journal of Health Economics (JHE) result must be shown stable to specification
  • Mechanism threats before submission
  • In response to referees

Example prompts

  • “/jhe-robustness”

Workflow steps

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

  1. Lead with the threat the editor/referee will raise first — usually selection or concurrent policy at JHE.
  2. Report stability, not stars. Show the point estimate across checks in one figure or compact table; if it moves, say so and explain.
  3. Right-size the spending/skew problem. Health expenditure is heavily right-skewed and zero-inflated; defend the estimator (two-part, GLM…
  4. Treat inference as a first-class robustness item, not a footnote — few-state clustering is a classic JHE referee catch.
  5. Pre-register the primary outcome where multiple health outcomes invite cherry-picking.
  6. Show, do not assert, stability. A single figure plotting the point estimate and CI across every check is worth more than a paragraph…

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

Jhe Robustness loads about 2k tokens when it runs. Until then it costs about 77 tokens; SKILL.md has 927 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~77
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). 927 words, ~2,015 tokens.

Download SKILL.mdSave it as .claude/skills/jhe-robustness/SKILL.md (or your agent's skills folder).
name
jhe-robustness
description
Use when a Journal of Health Economics (JHE) result must be shown stable to specification, sample, inference, and mechanism threats before submission or in response to referees. Organizes robustness by identifying threat; it does not establish the design (jhe-identification) or build exhibits.

Robustness Strategy (jhe-robustness)

When to trigger

  • The headline estimate may be sensitive to specification, sample window, or functional form
  • Inference is suspect: few clusters (states), serial correlation, or multiple outcomes/subgroups
  • A referee will ask whether the result is a coverage/take-up artifact rather than the claimed effect
  • The mechanism story is asserted but not separated from competing explanations

Robustness the JHE way: organize by threat, not by appendix

A wall of starred alternative specifications persuades no one. JHE referees want each robustness check mapped to a specific threat to the health-economics claim, with the point estimate's stability — not its significance — as the object. Build the robustness section as a threat-response ledger: name the threat a health economist would raise, run the check that addresses it, and report whether the magnitude moves. The threats that recur at JHE are selection, concurrent policy, measurement of health/utilization, and inference with few policy clusters.

Threat-to-check ledger

Threat to the claimCheck that addresses it
Residual selection into insurance/treatmentbounds (Lee/Manski/Oster); selection-on-observables-to-unobservables (Oster δ)
Concurrent reform contaminates the policy variationplacebo on ineligible group/period; leave-one-reform-out; pre-period falsification
Staggered-timing biasheterogeneity-robust estimator (CS / SA / dCDH); honest-DID sensitivity
Health/utilization mismeasurement (claims coding, self-report)alternative outcome definitions; administrative vs. survey cross-check; coding-change robustness
Functional form / sample windowlog vs. level, trimming outliers (skewed health spending!), alternative bandwidths, donut RD
Few-cluster inference (states)wild-cluster bootstrap; randomization inference; correct clustering level
Multiple outcomes/subgroupsMHT adjustment (Romano–Wolf / sharpened q-values); pre-specify the primary outcome
Mechanism is one of severalhorse-race the channels; show the competing story predicts a pattern you do not see

Sequencing

  1. Lead with the threat the editor/referee will raise first — usually selection or concurrent policy at JHE.
  2. Report stability, not stars. Show the point estimate across checks in one figure or compact table; if it moves, say so and explain.
  3. Right-size the spending/skew problem. Health expenditure is heavily right-skewed and zero-inflated; defend the estimator (two-part, GLM, IHS) rather than defaulting to OLS on a log.
  4. Treat inference as a first-class robustness item, not a footnote — few-state clustering is a classic JHE referee catch.
  5. Pre-register the primary outcome where multiple health outcomes invite cherry-picking.
  6. Show, do not assert, stability. A single figure plotting the point estimate and CI across every check is worth more than a paragraph claiming robustness; a referee can read it in seconds.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. JHE is health economics — insurance/program reforms and selection; foreground DiD/IV/RDD and selection corrections.

  • 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

  • Every robustness check is mapped to a named threat to the health-econ claim
  • Point-estimate stability is the reported object (not just significance)
  • Selection and concurrent-policy threats are addressed head-on
  • Skewed/zero-inflated health spending handled with a defended estimator
  • Inference matches the design: correct clustering level + few-cluster correction
  • Multiple outcomes/subgroups get MHT adjustment; primary outcome pre-specified
  • The mechanism is distinguished from at least one competing explanation
Show full SKILL.md (390 more words)Show less

Anti-patterns

  • A leave-out or alternative-sample check run but never reconciled when the estimate moves
  • An appendix of starred specifications with no map from check to threat
  • OLS on log spending with no handling of zeros or skew
  • Clustering below the policy level, then claiming significance
  • Running every subgroup and reporting the significant ones with no MHT adjustment
  • "Results are robust" with no figure showing the point estimate holding
  • Dodging the selection threat with more controls instead of a bound or design fix

The skewed-spending decision, made explicitly

Health spending and utilization are the journal's signature dependent variables, and they are right-skewed, zero-inflated, and heavy-tailed — the estimator choice is itself a robustness question a referee will press. Make it a deliberate, defended choice rather than a default:

  • Many zeros + continuous positive part → two-part model (probit/logit for any use × GLM for the positive amount); report both parts.
  • Skew without excess zeros → GLM with a log link (often gamma), which avoids retransformation bias that plagues OLS-on-log.
  • Want to keep zeros and interpret in levels → IHS or Poisson/PPML, stating the elasticity interpretation honestly.
  • Whatever you pick, show the result is not an artifact of the functional form by reporting at least one alternative, and never present OLS-on-log as if retransformation were free.

Worked vignette (illustrative)

A provider-payment paper shows intensity rises after a fee change; a referee suspects it is patient selection, not a true behavioral response. The JHE fix: hold the threat ledger explicit — (a) an Oster δ shows selection on unobservables would need to be 2× the observables to overturn the result; (b) a placebo on a fee-unaffected service shows no jump; (c) the spending outcome is re-run with a two-part model given 30% zeros; (d) inference is wild-cluster bootstrapped over 41 providers. The point estimate holds across all four (say 6.2%, stable within ±0.8pp, illustrative). The mechanism — behavioral response, not selection — now survives.

Output format

text
【Journal】Journal of Health Economics
【Skill】jhe-robustness
【Primary threat】selection / concurrent-policy / staggered-bias / measurement / inference
【Threat→check ledger】[threat: check → estimate movement]
【Spending estimator】OLS / two-part / GLM / IHS — defended? [Y/N]
【Inference】clustering level + few-cluster correction
【MHT】adjusted across outcomes/subgroups? [Y/N]
【Verdict】estimate stable / moves (explained) / fragile
【Next skill】jhe-tables-figures

Handoff boundary

This skill stress-tests an already-identified estimate; it does not fix a broken design (that is jhe-identification) or present the results (that is jhe-tables-figures). If a robustness check reveals the estimate is not actually identified — it swings with the selection bound or fails the placebo — route back to jhe-identification rather than papering over it with more specifications. When the point estimate holds across the threat ledger, hand off to jhe-tables-figures to make the stability legible.

© 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 Journal-of-Health-Economics-Skills/skills/jhe-robustness of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

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Categories

Questions about Jhe Robustness

What does Jhe Robustness do?

A skill your agent uses when a Journal of Health Economics (JHE) result must be shown stable to specification, sample, inference, and mechanism threats before submission or in response to referees. Jhe Robustness is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when a Journal of Health Economics (JHE) result must be shown stable to specification, sample, inference, and mechanism threats before submission or in response to referees.

When should I use Jhe Robustness?

Jhe Robustness fits situations like: A Journal of Health Economics (JHE) result must be shown stable to specification; mechanism threats before submission; in response to referees.

How do I install Jhe Robustness in Claude Code?

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

How do I install Jhe Robustness in Codex?

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

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

What does Jhe Robustness need to run?

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

Does Jhe 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 Jhe 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 Jhe Robustness use?

Jhe 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 Jhe Robustness use?

About 2k tokens (SKILL.md is roughly 8.1k 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 Jhe Robustness?

Skills that share tags, products or a category with Jhe 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 Jhe 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.