aejmic-tables-figures — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited aejmic-tables-figures (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
AEJ: Micro results are mostly propositions, characterizations, and numerical examples, not regression tables. The exhibit job is to make a formal result legible and concrete.
| What you want to convey | Best exhibit |
|---|---|
| A characterization holds | A numbered Proposition + an intuition sentence; rarely a table |
| How an optimum moves with a primitive | A numerical-example plot (object vs. primitive) |
| The structure of an equilibrium | A schematic (timeline, partition, region plot) |
| A magnitude from estimation/experiment | A three-line table with SEs / coverage sets |
| Model fit (structural) | Data vs. model overlay, not a fit statistic alone |
Pick one exhibit per idea. If a sentence does the job, do not add a figure; an AEJ: Micro theory paper can be exhibit-light without penalty.
Generate exhibits from the fitted result, not by retyping numbers (the usual source of body-vs-appendix drift). Full map: execution-with-mcp.
etable (multi-model columns) or did_summary_to_latex straight from theresult_id — one variable definition, one set of numbers, body and appendix in sync.
plot_from_result / enhanced_event_study_plot / event_study_table —axis units and the SE/clustering note baked in.
states the magnitude in interpretable units.
See a full fitted-result → exhibit chain in the JF execution walkthrough.
An information-design paper proves the sender-optimal signal is a two-message partition. The presentation: state Proposition 2 cleanly ("The sender-optimal signal partitions the state into {low, high} with cutoff x"), precede it with the intuition (pooling above x maximizes the receiver's action while preserving credibility), then a schematic figure of the state line with the cutoff and a small numerical example showing how x* moves with the sender's bias. No regression table, no asterisks — the exhibits make the mechanism visible.
【Formal results】numbered propositions/theorems, each self-contained? [Y/N]
【Economic-content-first prose】present for each? [Y/N]
【Numerical example】illustrates which proposition; reproducible? [Y/N]
【Schematic figures】one idea each; vector; clean labels? [Y/N]
【Applied tables】SEs/coverage, no asterisks? [Y/N or N/A]
【Next step】aejmic-writing-style~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.