Causal
ai-analyst-lab/ai-analyst
Causal inference toolkit for when experiments are not possible: estimate treatment effects from observational data with assumption checks and mandatory caveats.
A skill your agent uses when designing studies and measurement for a Journal of Applied Psychology (JAP) manuscript so they meet the journal's high bar on construct validity, causal inference…
Install Joap Study Design
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill joap-study-design -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills joap-study-design --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-Applied-Psychology-Skills/skills/joap-study-design .claude/skills/joap-study-design && 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 "joap-study-design" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Applied-Psychology-Skills/skills/joap-study-design into .claude/skills/joap-study-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "joap-study-design", 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-Applied-Psychology-Skills/skills/joap-study-designType 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 joap-study-design -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills joap-study-design --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-Applied-Psychology-Skills/skills/joap-study-design .agents/skills/joap-study-design && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "joap-study-design" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Applied-Psychology-Skills/skills/joap-study-design into .agents/skills/joap-study-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "joap-study-design", 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 joap-study-design -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills joap-study-design --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-Applied-Psychology-Skills/skills/joap-study-design .cursor/skills/joap-study-design && 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 "joap-study-design" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Applied-Psychology-Skills/skills/joap-study-design into .cursor/skills/joap-study-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "joap-study-design", 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-Applied-Psychology-Skills/skills/joap-study-design--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 joap-study-design -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills joap-study-design --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-Applied-Psychology-Skills/skills/joap-study-design .gemini/skills/joap-study-design && 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 "joap-study-design" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Applied-Psychology-Skills/skills/joap-study-design into .gemini/skills/joap-study-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "joap-study-design", 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 joap-study-designInstalls 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 joap-study-design -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-Applied-Psychology-Skills/skills/joap-study-design .github/skills/joap-study-design && 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 "joap-study-design" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Applied-Psychology-Skills/skills/joap-study-design into .github/skills/joap-study-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "joap-study-design", 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 joap-study-design -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 joap-study-design --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-Applied-Psychology-Skills/skills/joap-study-design .opencode/skills/joap-study-design && 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 "joap-study-design" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Applied-Psychology-Skills/skills/joap-study-design into .opencode/skills/joap-study-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "joap-study-design", 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
joap-study-design- GitHub stars
- 1.2k
- Token cost
- ~1.9k tokens
- SKILL.md length
- 655 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when designing studies and measurement for a Journal of Applied Psychology (JAP) manuscript so they meet the journal's high bar on construct validity, causal inference…
- Works in 5 steps: Construct validity first. Use validated… → Earn the causal claim. Cross-sectional… → Design against CMV. Build in procedural… → …
- Causal inference
- SKILL.md covers When to trigger, Design standards, Common-method variance — the… and Sample-size justification —…, plus 6 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Joap Study Design is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing studies and measurement for a Journal of Applied Psychology (JAP) manuscript so they meet the journal's high bar on construct validity, causal inference, common-method variance, nested/multilevel data, and sample-size justification. Strengthens the design and measurement plan; it does not write code.
Its SKILL.md is about 1.9k 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 Experimental design and Econometrics and empirical research. 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
- Causal inference
- Common-method variance
- Nested/multilevel data
- Sample-size justification
Example prompts
- “/joap-study-design”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Construct validity first. Use validated measures; report reliability and, where the construct is
- Earn the causal claim. Cross-sectional single-source correlation rarely suffices. Strengthen
- Design against CMV. Build in procedural remedies (temporal/source/measurement separation,
- Model the nesting. If employees are nested in teams/units/firms, justify N at each level, report
- Justify sample size at the right level. Power for the effect that carries the claim (e.g., the
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
Joap Study Design loads about 1.9k tokens when it runs. Until then it costs about 85 tokens; SKILL.md has 655 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). 655 words, ~1,856 tokens.
Download SKILL.mdSave it as
.claude/skills/joap-study-design/SKILL.md (or your agent's skills folder).- name
- joap-study-design
- description
- Use when designing studies and measurement for a Journal of Applied Psychology (JAP) manuscript so they meet the journal's high bar on construct validity, causal inference, common-method variance, nested/multilevel data, and sample-size justification. Strengthens the design and measurement plan; it does not write code.
Study Design & Measurement (joap-study-design)
JAP holds measurement and design to an exacting standard. The recurring killers are common-method variance (CMV), weak causal warrants (cross-sectional single-source data), unmodeled nesting, and construct validity gaps. This skill hardens the design before data collection, where most of these problems can actually be solved.
When to trigger
- Planning a study, a multi-study package, or a measurement strategy
- Writing a preregistration / pre-analysis plan
- A reviewer questioned CMV, causal inference, measurement, nesting, or power
- Justifying sample size at the relevant level of analysis
Design standards
- Construct validity first. Use validated measures; report reliability and, where the construct is new or contested, provide validity evidence (CFA, convergent/discriminant, measurement invariance across groups/time). A weak measure dooms an otherwise good design.
- Earn the causal claim. Cross-sectional single-source correlation rarely suffices. Strengthen with temporal separation (multi-wave), multiple sources (self + supervisor + objective), experimental or quasi-experimental legs, or a field experiment.
- Design against CMV. Build in procedural remedies (temporal/source/measurement separation, protected anonymity) and plan statistical checks; declare the strategy up front. Post hoc Harman's single-factor test alone is treated as insufficient at JAP.
- Model the nesting. If employees are nested in teams/units/firms, justify N at each level, report ICC(1)/ICC(2) and r_wg for aggregated constructs, and use multilevel models — do not ignore dependence or aggregate away the structure without justification.
- Justify sample size at the right level. Power for the effect that carries the claim (e.g., the cross-level interaction or indirect effect), not just the total N; for multilevel designs, the L2 sample size usually constrains power.
Common-method variance — the JAP design playbook
| Remedy | Type | Note |
|---|---|---|
| Temporal separation (multi-wave) | procedural | predictor and outcome at different waves |
| Source separation (self + other/objective) | procedural | the strongest single defense |
| Measurement/context separation | procedural | different scales/formats for predictor vs outcome |
| Protected anonymity, balanced items | procedural | reduces consistency and acquiescence bias |
| Marker variable / CFA marker technique | statistical | plan a theoretically unrelated marker in advance |
| ULMC (unmeasured latent method construct) | statistical | report alongside, not instead of, procedural remedies |
Sample-size justification — worked example (illustrative)
For the servant-leadership package, justify N at the level the hypotheses live, before collecting.
Multilevel field study (2-2-2 / 2-1-2 mediation):
Constraint: 74 teams (L2) drives power for the team-level indirect effect.
Power target: 80% for the indirect effect (Monte Carlo power for multilevel
mediation), assuming a path ≈ .25, b path ≈ .30, ICC(1) ≈ .15.
Result: target ≥ 70 teams, ~8 members each → ~560–620; we collect 612 in 74.
Lab experiment (causal leg):
Between-subjects, two conditions; power for the interaction (H3 boundary),
N ≈ 240 at 80%, alpha .05; fixed-N, no optional stopping.
Aggregation: report ICC(1), ICC(2), r_wg(j) to justify team-level aggregation
of psychological safety; preregister exclusion rules.Pre-data lockdown checklist
| Degree of freedom | Lock before data? | Where it lives |
|---|---|---|
| Hypotheses + direction + level | yes | preregistration |
| Measures (all scales, all items) | yes | preregistration (prevents scale cherry-picking) |
| CMV remedies (procedural + planned statistical) | yes | design + preregistration |
| Aggregation rules (ICC/r_wg thresholds) | yes | analysis plan |
| Exclusion rules (careless responding, attrition) | yes | preregistration |
| Covariates / model form | yes | analysis plan |
| Exploratory analyses | allowed, labeled | reported separately, post hoc |
Show full SKILL.md (242 more words)Show less
Design-stage reviewer pushback and the venue fix
- "Cross-sectional, same-source — common method bias" → add temporal/source separation or an experimental leg; declare procedural remedies, not just a Harman's test.
- "You ignored nesting" → model multilevel structure; report ICC(1)/ICC(2)/r_wg; justify aggregation.
- "Measure validity unclear" → report reliability, CFA fit, and invariance; cite scale provenance.
- "Underpowered for the cross-level effect" → repower at the constraining level; report the Monte Carlo
power analysis (handoff to
joap-data-analysis).
Execution bridge (StatsPAI / Stata MCP)
Estimate and audit the design, don't only describe it. Full map:
execution-with-mcp. JAP is organizational psychology — multilevel survey/field data and experiments; cluster at the right level and apply mediation/moderation discipline.
detect_design→recommend→ fit withas_handle=true→audit_result.- Observational causal claims: staggered DiD (
callaway_santanna/sun_abraham+bacon_decomposition+honest_did_from_result); IV (effective_f_test+anderson_rubin_ci); RDD (rdrobust+mccrary_test). - Experiments: randomization-based inference,
romano_wolffor many-outcome family-wise control, andmediatefor mediation (not naive controlling-away). - Sensitivity:
oster_delta/sensemakrfor observational claims.
Report the effect size in interpretable units; route the full battery to the appendix/supplement. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.
Anti-patterns
- Cross-sectional single-source self-report as the sole evidentiary base
- CMV addressed only by a post hoc Harman's single-factor test
- Nested data analyzed as if independent, or aggregated without ICC/r_wg justification
- New or modified measures with no validity evidence
- Sample size justified by total N while the carrying effect lives at L2
Output format
【Construct validity】reliability + CFA/invariance evidence? [Y/N]
【Causal warrant】temporal / multi-source / experimental leg present? [Y/N]
【CMV】procedural remedies + planned statistical check declared? [Y/N]
【Nesting】levels, ICC/r_wg, multilevel model justified? [Y/N/NA]
【Sample size】powered for the carrying effect at the right level? [Y/N]
【Next】joap-data-analysisSupplementary resources
../../resources/external_tools.md— Mplus/lavaan/lme4, Monte Carlo power, CMV-marker and invariance tools../../resources/official-source-map.md— measurement, design, and reporting expectations
© 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-Applied-Psychology-Skills/skills/joap-study-design of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Joap Study Design 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 |
|---|---|---|---|---|---|---|
| Joap Study Design this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.9k | Automated safety check: Pass | MIT | |
| Causalai-analyst-lab/ai-analyst | 304 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Fin Experiment Designcsmar432/finai-research | 109 | — | ~4.2k | Automated safety check: Pass | MIT | |
| Judea PearlK-Dense-AI/mimeo | 282 | — | ~1.7k | Automated safety check: Pass | MIT | |
| Academic Paper Verifybrycewang-stanford/Auto-Empirical-Research-Skills | 4.5k | — | ~2.9k | Automated safety check: Pass | Custom licence | |
| Designing Experimentsforyourhealth111-pixel/Vibe-Skills | 3.6k | — | ~600 | Automated safety check: Pass | Apache-2.0 |
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Questions about Joap Study Design
What does Joap Study Design do?
A skill your agent uses when designing studies and measurement for a Journal of Applied Psychology (JAP) manuscript so they meet the journal's high bar on construct validity, causal inference…. Joap Study Design is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing studies and measurement for a Journal of Applied Psychology (JAP) manuscript so they meet the journal's high bar on construct validity, causal inference, common-method variance, nested/multilevel data, and sample-size justification.
When should I use Joap Study Design?
Joap Study Design fits situations like: causal inference; common-method variance; nested/multilevel data; sample-size justification.
How do I install Joap Study Design in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill joap-study-design -a claude-code`. Or copy the skill folder (Journal-of-Applied-Psychology-Skills/skills/joap-study-design in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/joap-study-design in your project. Claude Code loads it when a task matches its description.
How do I install Joap Study Design in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill joap-study-design -a codex`. Or copy the skill folder (Journal-of-Applied-Psychology-Skills/skills/joap-study-design in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/joap-study-design in your project. Codex loads it when a task matches its description.
Can I use Joap Study Design 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 joap-study-design -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/joap-study-design, .gemini/skills/joap-study-design, .github/skills/joap-study-design and .opencode/skills/joap-study-design in your project.
What does Joap Study Design need to run?
SKILL.md names no scripts, command-line tools or credentials: Joap Study Design is instructions for the agent only.
Does Joap Study Design 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 Joap Study Design 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 Joap Study Design use?
Joap Study Design 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 Joap Study Design use?
About 1.9k tokens (SKILL.md is roughly 7.4k 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 Joap Study Design?
Skills that share tags, products or a category with Joap Study Design: Causal (ai-analyst-lab/ai-analyst, 304 stars), Fin Experiment Design (csmar432/finai-research, 109 stars), Judea Pearl (K-Dense-AI/mimeo, 282 stars) and Academic Paper Verify (brycewang-stanford/Auto-Empirical-Research-Skills, 4.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Joap Study Design?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,228 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.