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/Auto-Empirical-Research-Skills --skill 00.2-full-empirical-analysis-skill_statagit clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-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/auto-empirical-research-skills/00.2-full-empirical-analysis-skill_stata)<a href="https://agentmods.dev/skills/brycewang-stanford/auto-empirical-research-skills/00.2-full-empirical-analysis-skill_stata"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/auto-empirical-research-skills/00.2-full-empirical-analysis-skill_stata/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/auto-empirical-research-skills/00.2-full-empirical-analysis-skill_stata"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/auto-empirical-research-skills/00.2-full-empirical-analysis-skill_stata.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00940 | $0.36056 |
| Opus 5 | $0.00470 | $0.18028 |
| Sonnet 5 | $0.00188 | $0.07211 |
| Haiku 4.5 | $0.00094 | $0.03606 |
Grade A, and why
Full-empirical-analysis-skill-Stata 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 9d 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
What ships with it
9 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.
- README.md 14 KB
- references/01-data-cleaning.md 13 KB
- references/02-data-transformation.md 11 KB
- references/03-descriptive-stats.md 10 KB
- references/04-statistical-tests.md 10 KB
- references/05-modeling.md 20 KB
- references/06-robustness.md 13 KB
- references/07-further-analysis.md 13 KB
- references/08-tables-plots.md 16 KB
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.
- 9d ago Changed · +46 lines 5bc03fcf278d
- 13d ago First seen · 2,369 lines · 940 tokens per session scan A 59fe8d231cdc
Full-empirical-analysis-skill-Stata is a skill published in the GitHub repository brycewang-stanford/Auto-Empirical-Research-Skills (3,759 stars, last pushed 5d ago), with no licence file. It adds 940 tokens to every session and 36,056 once invoked, about $0.0047 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
validate-bib
Validate bibliography entries against citations in all lecture files. Structural checks (missing/unused entries, malformed fields) by default; --semantic adds citation-drift detection, DOI verification, and style-consistency checks.
ara-compile
SOP: Turn the feeding plan into the compiler's $ARGUMENTS and run the external ARA compiler once inline to produce ../ara/.
review-r
Read-only R code review protocol for .R scripts. Checks code quality, reproducibility, domain correctness, tidyverse idioms, and professional standards; produces a report without editing. Use when user says "review this R script", "check the R code", "audit the analysis code", "code review on the R", or when an R file…
causal-modeling
Campaign for building causal models — identify variables, map mechanisms, collect evidence, analyze interventions, validate models. Produces causal graphs in the wiki vault.
unit-segmentation
Split a paper's text into sentence- or clause-level units (with character offsets) for downstream classification, at a caller-specified granularity and scope (full text, abstract-only, or intro-only). Use this as the mandatory first step whenever any sentence/clause-level classification method (Argumentative Zoning…
question-framing
Fill a slot-based question-framing schema (PICO, PECO, or SPIDER) from a paper's stated research question. Use this whenever the user wants a paper's research question structured into one of these standard clinical/qualitative-research question frames; this frames what question is being asked, it does not read or…