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/StatsPAI --skill statspai_full_data_analysis_skillgit clone --depth 1 https://github.com/brycewang-stanford/StatsPAIWrote 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/statspai/statspai_full_data_analysis_skill)<a href="https://agentmods.dev/skills/brycewang-stanford/statspai/statspai_full_data_analysis_skill"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/statspai/statspai_full_data_analysis_skill.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00604 | $0.40231 |
| Opus 5 | $0.00302 | $0.20116 |
| Sonnet 5 | $0.00121 | $0.08046 |
| Haiku 4.5 | $0.00060 | $0.04023 |
Grade D, and why
StatsPAI_skill scanned grade D with 2 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 3d 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
| Title still shows ▢▢▢ tofu after `setup_plot()` | Host has none of the listed fonts. Install one — **macOS**: pre-installed (no action). **Linux**: `sudo apt install fonts-noto-cjk` (Debian/Ubuntu) or `sudo dnf install Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
| Title still shows ▢▢▢ tofu after `setup_plot()` | Host has none of the listed fonts. Install one — **macOS**: pre-installed (no action). **Linux**: `sudo apt install fonts-noto-cjk` (Debian/Ubuntu) or `sudo dnf install How it starts
The opening of the file, as written. The whole thing — 2,264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
StatsPAI: Agent-Native Causal Inference & AER-Style Empirical Workflow
StatsPAI is a validation-tiered Python package for causal inference and applied econometrics: one import statspai as sp, 1,100+ registered functions behind a self-describing API, and mature estimator result objects that commonly export to LaTeX / Word / Excel / BibTeX.
This skill drives StatsPAI through the canonical pipeline of an applied AER empirical paper. Each step emits a paper-ready artifact (Table 1, event-study figure, Table 2 main results, robustness panel, replication stamp).
- Source: https://github.com/brycewang-stanford/StatsPAI
- Install:
pip install "statspai[fixest,plotting]"(API surface re-validated against statspai 1.19.0 — everysp.*reference, signature, and result-object attribute claim in this skill is checked byvalidate_api_claims.pyin this folder). The barepip install statspaiis not enough for the default pipeline — see the dependency matrix below. - Paper: Wang & Rozelle (2026), Journal of Open Source Software 11(125), 10604, https://doi.org/10.21105/joss.10604; JSS materials in
Paper-JSS/README.mdanddocs/jss_source_audit_dossier.md
Install the right extras or the documented calls will raise
ImportError. Several core functions live behind optional dependency groups (verified frompyproject.toml):
You use… Needs extra Install Symptom if missing sp.feols/sp.fepois/sp.feglm(high-dim FE — the default for anyy ~ x | feregression)fixest(pyfixest)pip install "statspai[fixest]"ImportError: pyfixest is required …Any figure ( sp.coefplot,sp.binscatter, event-study/RD/SCM plots,.plot())plotting(matplotlib/seaborn)pip install "statspai[plotting]"ImportErroron first plotsp.dragonnet/sp.tarnet/sp.cfrnet/sp.cevae(neural causal)neural(torch)pip install "statspai[neural]"ImportError: PyTorch is required …sp.causal_text.*(text-as-treatment)text(sentence-transformers)pip install "statspai[text]"ImportErroron embedA one-shot install covering the whole skill:
pip install "statspai[fixest,plotting,neural,text]".sp.regtable/sp.collect/ Word+Excel+LaTeX export,sp.regress, IV, RD, DID (callaway_santanna), matching, DML, meta-learners, causal forest, BCF, TMLE, and the epi stack work on the base install.
What ships with it
3 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.
- 3d ago Changed c9e63dd652f6
- 9d ago First seen · 2,264 lines · 604 tokens per session scan D 993ddbe34dbe
StatsPAI_skill is a skill published in the GitHub repository brycewang-stanford/StatsPAI (314 stars, last pushed 2d ago), licensed MIT. It adds 604 tokens to every session and 40,231 once invoked, about $0.0030 per session on Opus 5. A static security scan graded it D with 2 findings (asks for root, recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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