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/empirical-design/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/empirical-design)<a href="https://agentmods.dev/skills/lambenthan/empiricalwiki/empirical-design"><img src="https://agentmods.dev/badge/skills/lambenthan/empiricalwiki/empirical-design/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/empirical-design"><img src="https://agentmods.dev/badge/skills/lambenthan/empiricalwiki/empirical-design.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 | $0.00040 | $0.00532 |
| Opus 5 | $0.00020 | $0.00266 |
| Sonnet 5 | $0.00008 | $0.00106 |
| Haiku 4.5 | $0.00004 | $0.00053 |
Grade A, and why
empirical-design 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 12d 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
/empirical-design
从 wiki 知识库生成一份可落地的实证研究方案。它连接文献阅读和数据处理，不负责替用户发明 idea。
Workflow
Step 1: Read Project Context
读取：
wiki/graph/context_brief.mdwiki/papers/*.mdwiki/variables/*.mdwiki/datasets/*.mdwiki/models/*.mdwiki/mechanisms/*.mdwiki/identification/*.mdwiki/robustness/*.mdwiki/heterogeneity/*.md- 项目根目录和上级目录中的
README.md raw/notes/research-intent.md（如果存在）
Step 2: Produce the Design
输出结构：
# 实证研究设计
## 1. 研究问题
## 2. 文献定位
## 3. 理论机制
## 4. 研究假设
## 5. 变量设计
## 6. 数据来源与样本构造
## 7. 基准模型
## 8. 识别策略与内生性风险
## 9. 机制检验
## 10. 异质性检验
## 11. 稳健性检验
## 12. 预期表格结构
## 13. 数据缺口清单
## 14. 下一步操作
每个关键判断都要引用 wiki 页面，如 [[耐心资本]]、[[耐心资本对企业esg表现的影响研究]]。
Step 3: Archive
写入：
wiki/outputs/empirical-design-{slug}-{YYYY-MM-DD}.md
追加日志并建议是否继续运行 /stata-plan。
Constraints
- 明确区分“文献已有做法”和“本项目建议做法”。
- 对内生性风险要具体，不写空泛的“可能存在内生性问题”。
- 如果本地数据不足，要列出缺口，不要假装可做。
- 不生成最终论文段落；这里输出的是研究设计和执行方案。
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.
- 12d ago First seen · 69 lines · 40 tokens per session scan A b7042ebb493b
empirical-design is a skill published in the GitHub repository Lambenthan/empiricalwiki (84 stars, last pushed 3mo ago), licensed MIT. It adds 40 tokens to every session and 532 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…
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"…