/aeja-tables-figures
Use when building or revising the exhibits of an American Economic Journal: Applied Economics (AEJ: Applied) manuscript so the main causal result is legible in one table or figure and respects AEA house presentation norms. Formats exhibits; it does not establish the result
$ npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill aeja-tables-figures --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/aeja-tables-figures
Context preview
The summary Claude sees to decide when to auto-load this skill.
Use when building or revising the exhibits of an American Economic Journal: Applied Economics (AEJ: Applied) manuscript so the main causal result is legible in one table or figure and respects AEA house presentation norms. Formats exhibits; it does not establish the result
SKILL.md
aeja-tables-figures.SKILL.mdname: aeja-tables-figures
description: Use when building or revising the exhibits of an American Economic Journal: Applied Economics (AEJ: Applied) manuscript so the main causal result is legible in one table or figure and respects AEA house presentation norms. Formats exhibits; it does not establish the result (aeja-identification / aeja-robustness) or write the surrounding prose.
Tables & Figures (aeja-tables-figures)
When to trigger
- The main result is settled and must be made readable at a glance
- Tables are dense, over-decimaled, or bury the headline coefficient
- An event-study / RD / first-stage plot needs to carry the identification visually
- You are preparing exhibits for submission and want them AEA-house-style compliant
The AEJ: Applied exhibit bar
At AEJ: Applied the **main causal estimate should be findable in seconds** and every exhibit should earn its place. AEA house style permits **significance stars but expects standard errors** in parentheses, clear notes that make each exhibit self-contained, and clean figures over chartjunk. Lead with the design's signature visual — the event-study plot, the RD scatter, or the balance table — because at this journal the *picture of the identification* is half the persuasion.
| Exhibit | What it must show | Common failure | |---------|-------------------|----------------| | Main results table | headline coefficient, SE in parentheses, N, controls indicated, dependent-var mean | too many columns; no SEs; over-precision | | Balance table (RCT) | baseline means by arm, differences, joint test | missing joint test; no attrition row | | Event-study figure | leads + lags, CIs, reference period, flat pre-trends visible | no CIs; ambiguous reference period | | RD figure | binned scatter + fitted lines, bandwidth, density | overfit polynomial; no density panel | | First-stage / IV table | first-stage F, exclusion logic in notes | weak first stage hidden | | Robustness exhibit | point-estimate stability across checks | a starred wall with no map |
Exhibit craft
1. **One table for the headline.** Table 1 (or 2) should let a referee read the main causal estimate, its SE, and N without flipping pages. 2. **Standard errors always, stars optional.** AEA permits stars, but SEs in parentheses are the load-bearing object; report the dependent-variable mean so magnitudes are interpretable. 3. **Self-contained notes.** Each exhibit's note states sample, units, clustering level, controls, and what an asterisk (if used) means — a referee should not need the text to read the table. 4. **Figures carry identification.** Event-study leads, RD continuity, and balance are more convincing as figures than as prose; make them publication-clean (vector output, readable fonts, CIs shown). 5. **Right precision.** Two to three significant figures; do not report coefficients to five decimals.
Execution bridge (StatsPAI / Stata MCP)
Generate exhibits from the fitted result, not by retyping numbers (the usual source of body-vs-appendix drift). Full map: [`execution-with-mcp`](../../../shared-resources/empirical-methods/execution-with-mcp.md).
- **Tables:** `etable` (multi-model columns) or `did_summary_to_latex` straight from the
`result_id` — one variable definition, one set of numbers, body and appendix in sync.
- **Figures:** `plot_from_result` / `enhanced_event_study_plot` / `event_study_table` —
axis units and the SE/clustering note baked in.
- **Every note** names the estimator + clustering (from the result's diagnostics) and
states the magnitude in interpretable units.
See a full fitted-result → exhibit chain in the [JF execution walkthrough](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).
Checklist
- [ ] Main causal estimate readable in one table: coefficient, SE in parentheses, N, dep-var mean, controls flagged
- [ ] Standard errors reported everywhere; clustering level stated in notes; stars (if used) defined
- [ ] Identification figure present (event-study with CIs / RD with density / balance with joint test)
- [ ] Notes make every exhibit self-contained (sample, units, clustering, controls)
- [ ] Figures are clean vector output, legible, no chartjunk, consistent scales
- [ ] Precision sensible (2–3 sig figs); no redundant exhibits
Anti-patterns
- A main results table with 12 columns where the headline coefficient is hard to find
- Reporting stars or t-stats but omitting standard errors / clustering level
- Over-precision (coefficients to 5 decimals) implying false accuracy
- An event-study plot with no confidence intervals or unclear reference period
- An RD figure with a high-order global polynomial that manufactures a jump
- Exhibit notes that force the reader back to the text to interpret the table
Worked vignette (illustrative)
A draft's Table 4 has 12 columns sweeping every control combination, and the headline coefficient is buried in column 9 with only t-statistics shown. The AEJ: Applied fix: promote the preferred specification to a two-panel Table 2 — Panel A the main estimate (coefficient 3.1, s.e. 0.9 in parentheses, N, dependent-var mean 0.44), Panel B the same with the full controls — and move the sweep to the online appendix. Add Figure 1, the event-study with confidence intervals and a clearly marked reference period, so the identification is visible before the reader reaches the table. The result is now findable in seconds.
Referee pushback mapped to the exhibit fix
- *"I cannot find your main estimate."* → One headline table with the coefficient, SE, N, and dep-var mean;
everything else demoted to the appendix.
- *"Where are the standard errors / what is the clustering?"* → SEs in parentheses everywhere; clustering
level and (if used) star meaning stated in the self-contained note.
- *"This RD jump looks like an artifact of the polynomial."* → Replace the global high-order fit with a
local-linear binned scatter plus a density panel.
Read more
name: aeja-tables-figures description: Use when building or revising the exhibits of an American Economic Journal: Applied Economics (AEJ: Applied) manuscript so the main causal result is legible in one table or figure and respects AEA house presentation norms. Formats exhibits; it does not establish the result (aeja-identification / aeja-robustness) or write the surrounding prose.
Tables & Figures (aeja-tables-figures)
When to trigger
- The main result is settled and must be made readable at a glance
- Tables are dense, over-decimaled, or bury the headline coefficient
- An event-study / RD / first-stage plot needs to carry the identification visually
- You are preparing exhibits for submission and want them AEA-house-style compliant
The AEJ: Applied exhibit bar
At AEJ: Applied the **main causal estimate should be findable in seconds** and every exhibit should earn its place. AEA house style permits **significance stars but expects standard errors** in parentheses, clear notes that make each exhibit self-contained, and clean figures over chartjunk. Lead with the design's signature visual — the event-study plot, the RD scatter, or the balance table — because at this journal the *picture of the identification* is half the persuasion.
| Exhibit | What it must show | Common failure | |---------|-------------------|----------------| | Main results table | headline coefficient, SE in parentheses, N, controls indicated, dependent-var mean | too many columns; no SEs; over-precision | | Balance table (RCT) | baseline means by arm, differences, joint test | missing joint test; no attrition row | | Event-study figure | leads + lags, CIs, reference period, flat pre-trends visible | no CIs; ambiguous reference period | | RD figure | binned scatter + fitted lines, bandwidth, density | overfit polynomial; no density panel | | First-stage / IV table | first-stage F, exclusion logic in notes | weak first stage hidden | | Robustness exhibit | point-estimate stability across checks | a starred wall with no map |
Exhibit craft
1. **One table for the headline.** Table 1 (or 2) should let a referee read the main causal estimate, its SE, and N without flipping pages. 2. **Standard errors always, stars optional.** AEA permits stars, but SEs in parentheses are the load-bearing object; report the dependent-variable mean so magnitudes are interpretable. 3. **Self-contained notes.** Each exhibit's note states sample, units, clustering level, controls, and what an asterisk (if used) means — a referee should not need the text to read the table. 4. **Figures carry identification.** Event-study leads, RD continuity, and balance are more convincing as figures than as prose; make them publication-clean (vector output, readable fonts, CIs shown). 5. **Right precision.** Two to three significant figures; do not report coefficients to five decimals.
Execution bridge (StatsPAI / Stata MCP)
Generate exhibits from the fitted result, not by retyping numbers (the usual source of body-vs-appendix drift). Full map: [`execution-with-mcp`](../../../shared-resources/empirical-methods/execution-with-mcp.md).
- **Tables:** `etable` (multi-model columns) or `did_summary_to_latex` straight from the
`result_id` — one variable definition, one set of numbers, body and appendix in sync.
- **Figures:** `plot_from_result` / `enhanced_event_study_plot` / `event_study_table` —
axis units and the SE/clustering note baked in.
- **Every note** names the estimator + clustering (from the result's diagnostics) and
states the magnitude in interpretable units.
See a full fitted-result → exhibit chain in the [JF execution walkthrough](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).
Checklist
- [ ] Main causal estimate readable in one table: coefficient, SE in parentheses, N, dep-var mean, controls flagged
- [ ] Standard errors reported everywhere; clustering level stated in notes; stars (if used) defined
- [ ] Identification figure present (event-study with CIs / RD with density / balance with joint test)
- [ ] Notes make every exhibit self-contained (sample, units, clustering, controls)
- [ ] Figures are clean vector output, legible, no chartjunk, consistent scales
- [ ] Precision sensible (2–3 sig figs); no redundant exhibits
Anti-patterns
- A main results table with 12 columns where the headline coefficient is hard to find
- Reporting stars or t-stats but omitting standard errors / clustering level
- Over-precision (coefficients to 5 decimals) implying false accuracy
- An event-study plot with no confidence intervals or unclear reference period
- An RD figure with a high-order global polynomial that manufactures a jump
- Exhibit notes that force the reader back to the text to interpret the table
Worked vignette (illustrative)
A draft's Table 4 has 12 columns sweeping every control combination, and the headline coefficient is buried in column 9 with only t-statistics shown. The AEJ: Applied fix: promote the preferred specification to a two-panel Table 2 — Panel A the main estimate (coefficient 3.1, s.e. 0.9 in parentheses, N, dependent-var mean 0.44), Panel B the same with the full controls — and move the sweep to the online appendix. Add Figure 1, the event-study with confidence intervals and a clearly marked reference period, so the identification is visible before the reader reaches the table. The result is now findable in seconds.
Referee pushback mapped to the exhibit fix
- *"I cannot find your main estimate."* → One headline table with the coefficient, SE, N, and dep-var mean;
everything else demoted to the appendix.
- *"Where are the standard errors / what is the clustering?"* → SEs in parentheses everywhere; clustering
level and (if used) star meaning stated in the self-contained note.
- *"This RD jump looks like an artifact of the polynomial."* → Replace the global high-order fit with a
local-linear binned scatter plus a density panel.
Stanford REAP × CoPaper.AI · 由斯坦福实证方法论团队精选与维护 访问 copaper.ai 微信:CoPaper.AI 按 11 个主流学科板块覆盖 经管与商科 社会科学 人文学科 数学与物理科学 生命科学 医学与健康 工程与技术 计算机科学与 AI 体育科学 点击任一学科名可跳转到对应说明;每类下的代表子领域在正文总览中完整列出。下方封面墙按 venue 导航,完整分类见覆盖一览。 🧭 布局指南 · 📚 Skill Pack 一览 · ⚡ 如何使用 · 🧪 自动实证
Other skills on awesome-journal-skills.
- /aaai-artifact-evaluation
Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.
Open skill - /aaai-author-response
Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round
Open skill - /aaai-camera-ready
Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and
Open skill - /aaai-experiments
Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and
Open skill - /aaai-related-work
Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a
Open skill - /aaai-reproducibility
Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that
Open skill

