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.
A skill your agent uses when a Journal of Risk and Uncertainty (JRU) result may be sensitive to specification, incentive frame, sample, or inference.
Install Jru Robustness
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jru-robustness -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jru-robustness --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-Risk-and-Uncertainty-Skills/skills/jru-robustness .claude/skills/jru-robustness && rm -rf skills-srcUse ~/.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/
Install the "jru-robustness" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Risk-and-Uncertainty-Skills/skills/jru-robustness into .claude/skills/jru-robustness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jru-robustness", 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 install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Risk-and-Uncertainty-Skills/skills/jru-robustnessType 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.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jru-robustness -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jru-robustness --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/Journal-of-Risk-and-Uncertainty-Skills/skills/jru-robustness .agents/skills/jru-robustness && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "jru-robustness" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Risk-and-Uncertainty-Skills/skills/jru-robustness into .agents/skills/jru-robustness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jru-robustness", 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.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jru-robustness -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jru-robustness --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/Journal-of-Risk-and-Uncertainty-Skills/skills/jru-robustness .cursor/skills/jru-robustness && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "jru-robustness" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Risk-and-Uncertainty-Skills/skills/jru-robustness into .cursor/skills/jru-robustness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jru-robustness", 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 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path Journal-of-Risk-and-Uncertainty-Skills/skills/jru-robustness--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jru-robustness -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jru-robustness --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/Journal-of-Risk-and-Uncertainty-Skills/skills/jru-robustness .gemini/skills/jru-robustness && rm -rf skills-srcUse ~/.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/
Install the "jru-robustness" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Risk-and-Uncertainty-Skills/skills/jru-robustness into .gemini/skills/jru-robustness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jru-robustness", 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.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jru-robustnessInstalls 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).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jru-robustness -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/Journal-of-Risk-and-Uncertainty-Skills/skills/jru-robustness .github/skills/jru-robustness && rm -rf skills-srcUse ~/.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/
Install the "jru-robustness" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Risk-and-Uncertainty-Skills/skills/jru-robustness into .github/skills/jru-robustness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jru-robustness", 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.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jru-robustness -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jru-robustness --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/Journal-of-Risk-and-Uncertainty-Skills/skills/jru-robustness .opencode/skills/jru-robustness && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "jru-robustness" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Risk-and-Uncertainty-Skills/skills/jru-robustness into .opencode/skills/jru-robustness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jru-robustness", 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
jru-robustness- GitHub stars
- 1.2k
- Token cost
- ~1.8k tokens
- SKILL.md length
- 849 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 Risk and Uncertainty (JRU) result may be sensitive to specification, incentive frame, sample, or inference.
- Works in 5 steps: Rank threats by how badly each would… → Run the check that kills the most… → Report robustness as "the sign and rough… → …
- A Journal of Risk and Uncertainty (JRU) result may be sensitive to specification
- SKILL.md covers When to trigger, Organize robustness by threat,…, Sequencing and Execution bridge (StatsPAI /…, plus 6 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Jru Robustness is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when a Journal of Risk and Uncertainty (JRU) result may be sensitive to specification, incentive frame, sample, or inference. Organizes robustness by the threat to the risk/uncertainty parameter; it does not invent evidence or citations.
Its SKILL.md is about 1.8k 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 Citation management. 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 Risk and Uncertainty (JRU) result may be sensitive to specification
- Incentive frame
Example prompts
- “/jru-robustness”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Rank threats by how badly each would damage the headline claim if true.
- Run the check that kills the most dangerous threat first; if the result dies there, stop and rethink before polishing anything.
- Report robustness as "the sign and rough magnitude of [parameter] is stable across [family]," not "Table A12 shows similar results."
- Distinguish checks that the design demands (incentive-frame tests for experiments) from generic ones (alternate clustering).
- Hand off to jru-tables-figures once the parameter is stable across the threats that matter.
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
Jru Robustness loads about 1.8k tokens when it runs. Until then it costs about 64 tokens; SKILL.md has 849 words of instructions outside code blocks.
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). 849 words, ~1,806 tokens.
Download SKILL.mdSave it as
.claude/skills/jru-robustness/SKILL.md (or your agent's skills folder).- name
- jru-robustness
- description
- Use when a Journal of Risk and Uncertainty (JRU) result may be sensitive to specification, incentive frame, sample, or inference. Organizes robustness by the threat to the risk/uncertainty parameter; it does not invent evidence or citations.
Robustness Strategy (jru-robustness)
When to trigger
- The headline risk/ambiguity parameter shifts under a different functional form (CRRA vs. CARA vs. expo-power) and you are unsure which to report
- An experimental result might be an artifact of stakes, order, the random-incentive system, or a particular elicitation device
- A referee will ask whether the finding survives EU vs. non-EU specifications, or pooled vs. heterogeneous-type estimation
- Multiple hypotheses are tested across many lottery menus or treatments and no correction is in place
Organize robustness by threat, not by appendix
A JRU robustness section earns its place when every check is tied to a specific threat to the parameter's interpretation. List the threats first, then the check that answers each.
| Threat to the result | The check that addresses it |
|---|---|
| Functional-form dependence of the risk parameter | Re-estimate under CRRA, CARA, expo-power; report whether the qualitative claim is stable |
| Utility–weighting confound | Show the result holds under a model that separates u from w (e.g., RDU/CPT, not just EU) |
| Elicitation-device artifact | Replicate the pattern with a second device (price list vs. BDM vs. matching probabilities) |
| Random-incentive / isolation failure | Compare one-shot-paid vs. all-paid; test for portfolio/house-money effects |
| Stake / hypothetical-bias sensitivity | Vary real stakes; compare to hypothetical where relevant |
| Subject heterogeneity masked by pooling | Estimate a mixture / finite-type model or random coefficients, not just a representative agent |
| Multiple comparisons across menus/treatments | Adjust (e.g., Holm / Romano–Wolf) and report which results survive |
| Inference too optimistic | Cluster at the subject level; report with few-cluster corrections where needed |
For VSL / insurance empirics, add: alternative risk measures, sample-selection probes, and sensitivity to the publication-selection / meta-analytic benchmark.
Sequencing
- Rank threats by how badly each would damage the headline claim if true.
- Run the check that kills the most dangerous threat first; if the result dies there, stop and rethink before polishing anything.
- Report robustness as "the sign and rough magnitude of [parameter] is stable across [family]," not "Table A12 shows similar results."
- Distinguish checks that the design demands (incentive-frame tests for experiments) from generic ones (alternate clustering).
- Hand off to
jru-tables-figuresonce the parameter is stable across the threats that matter.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. JRU spans decision experiments and applied risk; randomization inference for experiments, DiD/IV for observational claims.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_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 exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Decisive checks in the body, exhaustive battery in the appendix. JF execution walkthrough.
Checklist
- Every robustness exhibit names the threat it addresses
- The risk parameter is shown stable across at least two functional forms
- A model that separates utility from probability weighting is among the specifications
- Experiments: incentive-frame, stake, and order effects probed; second elicitation device where feasible
- Heterogeneity addressed (mixture / random coefficients) rather than masked by a representative agent
- Multiple-comparison adjustment applied when many menus/treatments are tested
- Inference clusters at the subject (or assignment) level; few-cluster issue handled
Show full SKILL.md (339 more words)Show less
Anti-patterns
- A robustness appendix that is a pile of tables with no map from threat to check
- Reporting only the functional form that gives the cleanest number
- Treating an EU-only robustness suite as sufficient when the claim is about non-EU behavior
- Pooling across heterogeneous subjects and presenting the average as if it were a type
- Mining many lottery menus and reporting the significant ones without correction
Distinguish robustness from a specification search
JRU referees draw a sharp line between probing a result and searching for one. Stay on the right side of it:
- Pre-commit the headline specification and present alternatives as deviations from it, not as a menu you chose among.
- Report all the forms you ran, including the ones where the estimate weakened — selective reporting reads as a fishing expedition to a specialist.
- State the decision rule for when the result "survives": e.g., the sign holds and the magnitude stays within a stated band across forms and devices.
- For experiments, distinguish pre-registered confirmatory checks from exploratory ones, and label them as such.
Robustness the experiment specifically demands
Lab and field elicitation papers carry threats that generic econometric robustness misses:
- Comprehension and noise: show the result is not driven by subjects who failed comprehension checks; consider a trembling-hand / Fechner noise term rather than dropping "irrational" subjects.
- Incentive realism: compare real vs. hypothetical, and high vs. low stakes, where the claim depends on it.
- Within-subject consistency: report test-retest or internal consistency for the elicited parameter.
Worked vignette (illustrative)
A paper reports loss aversion λ ≈ 2.1 from a choice-list experiment. The most dangerous threat is that λ is an artifact of the list format (multiple switching, framing). The first check replicates the estimate with a second device (matching probabilities); the second re-estimates under CPT vs. a reference-dependent EU baseline; the third splits by a mixture model to confirm λ is not driven by a confused minority. Only after λ survives all three — with the across-device range reported in full — does the paper present it as the headline in jru-tables-figures.
Output format
【Journal】Journal of Risk and Uncertainty
【Skill】jru-robustness
【Verdict】robust / patch / result fragile
【Top threat】<the check that would most damage the claim>
【Threat→check map】<list>
【Parameter stability】sign+magnitude across <families/devices>
【Heterogeneity】mixture / random coefficients / not addressed
【Source status】verified / 待核实 / not asserted
【Next skill】jru-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 Journal-of-Risk-and-Uncertainty-Skills/skills/jru-robustness of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Jru 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Jru Robustness this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.8k | Automated safety check: Pass | MIT | |
| Content Research Writerweapp-tailwindcss/weapp-tailwindcss | 1.9k | 25 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Systematic Review ScreenerImbad0202/academic-research-skills | 51k | — | ~8.4k | Automated safety check: Pass | Custom licence | |
| NetworkxzLanqing/codex-claude-academic-skills | 4.7k | 15 repos | ~3.2k | Automated safety check: Pass | BSD-3-Clause | |
| Literature Reviewneflibata-feng/MyArxiv-Agent | 126 | 20 repos | ~5.9k | Automated safety check: Notes | MIT | |
| Openalex Databaseneflibata-feng/MyArxiv-Agent | 126 | 12 repos | ~3k | Automated safety check: Pass | Custom licence |
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Networkx
zLanqing/codex-claude-academic-skills
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python.
Literature Review
neflibata-feng/MyArxiv-Agent
Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.).
Openalex Database
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Query and analyze scholarly literature using the OpenAlex database.
Citation Verification Guide
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Reference guidance for checking every citation in academic writing against canonical sources such as DOI, arXiv, CrossRef and Semantic Scholar, to catch fake or wrong references.
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Questions about Jru Robustness
What does Jru Robustness do?
A skill your agent uses when a Journal of Risk and Uncertainty (JRU) result may be sensitive to specification, incentive frame, sample, or inference. Jru Robustness is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when a Journal of Risk and Uncertainty (JRU) result may be sensitive to specification, incentive frame, sample, or inference.
When should I use Jru Robustness?
Jru Robustness fits situations like: A Journal of Risk and Uncertainty (JRU) result may be sensitive to specification; incentive frame.
How do I install Jru Robustness in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jru-robustness -a claude-code`. Or copy the skill folder (Journal-of-Risk-and-Uncertainty-Skills/skills/jru-robustness in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/jru-robustness in your project. Claude Code loads it when a task matches its description.
How do I install Jru Robustness in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jru-robustness -a codex`. Or copy the skill folder (Journal-of-Risk-and-Uncertainty-Skills/skills/jru-robustness in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/jru-robustness in your project. Codex loads it when a task matches its description.
Can I use Jru 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 jru-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/jru-robustness, .gemini/skills/jru-robustness, .github/skills/jru-robustness and .opencode/skills/jru-robustness in your project.
What does Jru Robustness need to run?
SKILL.md names no scripts, command-line tools or credentials: Jru Robustness is instructions for the agent only.
Does Jru 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 Jru 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 Jru Robustness use?
Jru 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 Jru Robustness use?
About 1.8k tokens (SKILL.md is roughly 7.2k 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 Jru Robustness?
Skills that share tags, products or a category with Jru Robustness: Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars), Systematic Review Screener (Imbad0202/academic-research-skills, 51k stars), Networkx (zLanqing/codex-claude-academic-skills, 4.7k stars) and Literature Review (neflibata-feng/MyArxiv-Agent, 126 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Jru 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.