Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add brycewang-stanford/AER-Skills --skill aer-paper-bodygit clone --depth 1 https://github.com/brycewang-stanford/AER-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/brycewang-stanford/aer-skills/aer-paper-body)<a href="https://agentmods.dev/skills/brycewang-stanford/aer-skills/aer-paper-body"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/aer-skills/aer-paper-body/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/brycewang-stanford/aer-skills/aer-paper-body"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/aer-skills/aer-paper-body.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00078 | $0.02932 |
| Opus 5 | $0.00039 | $0.01466 |
| Sonnet 5 | $0.00016 | $0.00586 |
| Haiku 4.5 | $0.00008 | $0.00293 |
Grade A, and why
aer-paper-body scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 266 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AER Paper Body
Overview
The introduction decides whether the editor sends the paper out; the body sections decide what the referees write. Referees live in Data, Empirical Strategy, and Results, checking that the estimand is defined, the assumption stated, the magnitudes interpreted, and the prose matched to the tables. Draft the body before the introduction — the introduction summarizes a paper that already exists, and writing it first produces promises the body fails to keep.
When to Use
- The empirics are stable (after
aer-identificationandaer-robustness) and the manuscript needs full section drafts - A results section reads like a table walk-through ("Column 1 shows...") and needs narration surgery
- A referee called the paper "hard to follow," "under-interpreted," or "reads like a report"
- Coefficients are reported but never converted into economic magnitudes
- The conclusion restates the abstract and needs to do real work
Canonical Section Architecture
A full-length empirical AER paper, after the unlabeled introduction:
I. Background (or: Institutional Setting; Policy Context)
II. Data (sources, sample construction, measurement, summary stats)
III. Empirical Strategy (estimand, equation, identifying assumption, inference)
IV. Results (main estimates, dynamics, robustness pointers)
V. Mechanisms (or: Heterogeneity and Mechanisms; Interpretation)
VI. Conclusion
Variants: a conceptual framework goes between Background and Data (or replaces Background for theory-led papers); AER: Insights compresses to Data and Design → Results → Discussion; structural papers add Model and Estimation, where the rules below bind with more force, not less. One rule binds everywhere: every term, dataset, and design feature is defined before first use — referees read linearly on the first pass.
Section I — Background / Institutional Setting
Give exactly the institutional detail needed to (a) locate the identifying variation and (b) believe the identifying assumption — nothing else.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 266 lines · 78 tokens per session scan A f37345d59771
aer-paper-body is a skill published in the GitHub repository brycewang-stanford/AER-Skills (49 stars, last pushed 1mo ago), licensed MIT. It adds 78 tokens to every session and 2,932 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
review-paper
Comprehensive manuscript review with three modes: single-pass (default), --adversarial critic-fixer loop, and --peer [journal] simulated peer-review pipeline (editor + 2 dispositioned referees + editorial decision, calibrated to a target journal). R&R continuation via --peer --r2/--r3; hostile-editor stress test via…
audit-reproducibility
Enforce the replication-protocol.md rule by cross-checking numeric claims in a manuscript against the actual R / Stata / Python outputs. Report PASS/FAIL per claim against tolerance thresholds. Use before submission and before releasing a replication package.
capture-environment
Snapshot the computational environment for a replication package — detects the analysis stack (R / Stata / Python) and emits the right lockfiles (renv.lock + sessionInfo.txt, requirements.txt / environment.yml / uv.lock, Stata version + ado package list), records seeds and RNG kind, optionally writes a pinning…
grant-proposal
Scaffold a research grant proposal (NSF, NIH, ERC, or foundation) by composing existing primitives — pulls identification strategy from an /interview-me spec, delegates the data-management plan to /data-management-plan and the facilities statement to /capture-environment, and emits a funder-requirements checklist. Use…
preregister
Draft a structured preregistration document (OSF, AsPredicted, or AEA RCT Registry style) from a research spec or free-form study description. Output is a Markdown file with hypotheses, design, sampling plan, analysis plan, exclusions, and inference criteria — annotated with MUST / SHOULD / MAY clarity flags. Use when…
simulation-study
Scaffold and run a reproducible Monte Carlo simulation study in R — a declared assumption regime, a parameterized DGP, an estimator grid, a seeded replication loop, and a summary of bias, RMSE, empirical SE, coverage, size/power with Monte Carlo standard errors. Use when the user says "run a Monte Carlo simulation"…