Borrowing it
Nothing to install: this file belongs to Lambenthan/empiricalwiki. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Lambenthan/empiricalwiki/main/.claude/skills/stata-plan/SKILL.mdgit clone --depth 1 https://github.com/Lambenthan/empiricalwikiWrote 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/lambenthan/empiricalwiki/stata-plan)<a href="https://agentmods.dev/skills/lambenthan/empiricalwiki/stata-plan"><img src="https://agentmods.dev/badge/skills/lambenthan/empiricalwiki/stata-plan/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/lambenthan/empiricalwiki/stata-plan"><img src="https://agentmods.dev/badge/skills/lambenthan/empiricalwiki/stata-plan.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00037 | $0.00695 |
| Opus 5 | $0.00018 | $0.00347 |
| Sonnet 5 | $0.00007 | $0.00139 |
| Haiku 4.5 | $0.00004 | $0.00069 |
Grade A, and why
stata-plan 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 10d 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.
What it actually says
/stata-plan
把 wiki 中的变量、数据和模型信息转成 Stata 层面的执行顺序。默认生成计划;只有用户传
--write-do时才创建.do骨架。
Workflow
Step 1: Read Design and Data Context
读取:
- 指定的
wiki/outputs/empirical-design-*.md,或按 topic 匹配最近设计文档 wiki/variables/*.mdwiki/datasets/*.mdwiki/models/*.mdwiki/robustness/*.mdwiki/heterogeneity/*.mdwiki/identification/*.md(设计涉及 DID / PSM / IV / RDD / TWFE 时优先读对应预置策略卡)tools/stata-templates/*.do(预置模板:twfe / did_staggered / psm / iv_2sls / rdd;--write-do时以匹配的模板为骨架起点,按设计文档改写变量与参数)- 项目中的
.do、.dta、.xlsx、变量字典和 README
Step 2: Produce Stata Execution Plan
输出结构:
# Stata 执行计划
## 1. 输入数据
## 2. 主键与合并顺序
## 3. 样本筛选
## 4. 变量构造
## 5. 缩尾与缺失值处理
## 6. 描述性统计
## 7. 相关性分析
## 8. 基准回归
## 9. 机制检验
## 10. 异质性检验
## 11. 稳健性检验
## 12. 表格导出
## 13. 复核清单
每一步尽量写成 Stata 伪代码或代码块,但不要假造不存在的变量名。变量名不确定时用占位符并标记“需确认”。
Step 3: Optional Do File
只有用户传 --write-do 时,创建:
wiki/outputs/stata-plan-{slug}-{YYYY-MM-DD}.do
默认只写:
wiki/outputs/stata-plan-{slug}-{YYYY-MM-DD}.md
Step 4: Log
python3 tools/research_wiki.py log wiki "stata-plan | <topic> | output: outputs/<file>"
Constraints
- 不覆盖用户已有
.do文件。 - 不假设 Stata 包已经安装;需要
reghdfe、esttab等时在计划里列出安装命令。 - 代码骨架必须保留复核点,如样本量检查、merge 结果检查、变量分布检查。
- 对中文路径和空格路径使用引号。
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.
- 10d ago First seen · 76 lines · 37 tokens per session scan A b5d25f3869b8
stata-plan is a skill published in the GitHub repository Lambenthan/empiricalwiki (82 stars, last pushed 2mo ago), licensed MIT. It adds 37 tokens to every session and 695 once invoked, about $0.0002 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…
data-management-plan
Draft a funder-compliant Data Management Plan (NSF DMP, NIH DMS Policy 2023, ERC, Horizon Europe) by composing the confidential-data and environment-capture primitives. Sections cover data description, formats/metadata, storage/backup, access/sharing, preservation/archiving, and roles. Use when user says "data…
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…
power-analysis
Compute statistical power, required sample size, and minimum detectable effect (MDE) for a study design, then write a registry-ready power section. Handles two-arm RCTs (with clustering / ICC and unequal allocation), multiple-arm corrections, and a simulation-based power option for non-standard designs…