A skill your agent uses when the empirical identification strategy is the bottleneck for a 《财贸经济》 manuscript — fiscal / tax / financial / trade policy shocks and firm- / city- / bank-level…

MITAuto-check passedResearch & Science

Install Cte Identification

skills CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cte-identification -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cte-identification --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/Finance-and-Trade-Economics-Skills/skills/cte-identification .claude/skills/cte-identification && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
cte-identification
GitHub stars
1.2k
Token cost
~866 tokens
SKILL.md length
219 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when the empirical identification strategy is the bottleneck for a 《财贸经济》 manuscript — fiscal / tax / financial / trade policy shocks and firm- / city- / bank-level…

  • Works in 6 steps: 财经政策冲击 + DID(含 staggered / continuous… → 断点回归 /… → 工具变量——强工具 + 排他性论证(处理政策内生与反向因果的核心武器) → …
  • Tasks that involve Load testing
  • SKILL.md covers 触发时机, 设计优先级, 财经数据的内生性专项 and 分支路径, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Cte Identification is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when the empirical identification strategy is the bottleneck for a 《财贸经济》 manuscript — fiscal / tax / financial / trade policy shocks and firm- / city- / bank-level quasi-experimental designs (DID, IV, RDD, DML). Stress-tests the design and the policy endogeneity before drafting tables. 本技能服务于《财贸经济》(Finance & Trade Economics, CTE)。

Its SKILL.md is about 870 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Load testing and Experimental design. It works with Model Context Protocol. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. | 覆盖主流期刊的… The licence is MIT.

When your agent uses it

  • Tasks that involve Load testing
  • Tasks that involve Experimental design

Example prompts

  • “/cte-identification”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. 财经政策冲击 + DID(含 staggered / continuous treatment)——如营改增、减税降费、财政试点、金融监管改革、自贸区 / 关税调整的分批推行
  2. 断点回归 / 断点——清晰的政策门槛(如税收优惠资格线、财政转移支付分档线、监管资本门槛、贫困县 / 城市规模分档)
  3. 工具变量——强工具 + 排他性论证(处理政策内生与反向因果的核心武器)
  4. 双重机器学习(DML)——高维协变量 / 非线性混淆下的稳健处理效应估计
  5. 合成控制法——城市 / 省级政策评估(如单一试点城市)
  6. OLS + 严密的内生性讨论(仅在有强外生性论证或结构 / 理论实证时可接受)

What it can do on your machine

Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Cte Identification loads about 866 tokens when it runs. Until then it costs about 89 tokens; SKILL.md has 219 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~89
When it runs · the whole SKILL.md, loaded when a task matches
~866

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 219 words, ~866 tokens.

Download SKILL.mdSave it as .claude/skills/cte-identification/SKILL.md (or your agent's skills folder).
name
cte-identification
description
Use when the empirical identification strategy is the bottleneck for a 《财贸经济》 manuscript — fiscal / tax / financial / trade policy shocks and firm- / city- / bank-level quasi-experimental designs (DID, IV, RDD, DML). Stress-tests the design and the policy endogeneity before drafting tables. 本技能服务于《财贸经济》(Finance & Trade Economics, CTE)。

因果识别策略(cte-identification)

触发时机

  • 实证主体仅有描述统计 + OLS + 控制变量
  • 核心解释变量是政策 / 制度冲击(税制改革、财政试点、金融监管、贸易政策),但没处理政策内生与预期
  • DID 用了 TWFE 但没回应近年异质性处理批评(Goodman-Bacon, de Chaisemartin, Sun-Abraham, Callaway-Sant'Anna)
  • IV 第一阶段 F 弱 / 工具变量排他性疑虑
  • 用了机器学习控制高维协变量但没做正交化 / 交叉拟合(DML)

设计优先级

《财贸经济》编委对财经实证的偏好排序(强 → 弱):

  1. 财经政策冲击 + DID(含 staggered / continuous treatment)——如营改增、减税降费、财政试点、金融监管改革、自贸区 / 关税调整的分批推行
  2. 断点回归 / 断点——清晰的政策门槛(如税收优惠资格线、财政转移支付分档线、监管资本门槛、贫困县 / 城市规模分档)
  3. 工具变量——强工具 + 排他性论证(处理政策内生与反向因果的核心武器)
  4. 双重机器学习(DML)——高维协变量 / 非线性混淆下的稳健处理效应估计
  5. 合成控制法——城市 / 省级政策评估(如单一试点城市)
  6. OLS + 严密的内生性讨论(仅在有强外生性论证或结构 / 理论实证时可接受)

财经数据的内生性专项

财经实证最常见的内生性来源,审稿人必查:

  • 政策内生:政策是否非随机地投向"本就更好 / 更差"的地区 / 企业(如试点先选基础好的城市)?
  • 反向因果:是先有金融风险才有监管,还是监管带来风险变化?
  • 预期效应:市场主体是否在政策正式落地前已提前调整(抢出口、突击减税前投资)?
  • 测量与口径:财税口径 / 会计科目 / 贸易统计的可比性与操纵空间

针对性策略至少给出一条:固定效应 + 准实验冲击 / IV / 断点 / DML;纯 Heckman 或纯 PSM 仅作辅助,不能单独立住识别。

分支路径

分支 A:DID
  • 是否 staggered?→ 必须用 Goodman-Bacon 分解 + Callaway-Sant'Anna 或 Sun-Abraham
  • 平行趋势检验:事件研究图必须画,处理时点前系数应不显著
  • 安慰剂:随机分配处理组 / 处理时点 500–1000 次
  • 连续处理(如税率变动幅度)需回应剂量-反应的异质性偏误
分支 B:IV
  • 第一阶段 F 必须 ≥ 10(弱工具 → 用 Anderson-Rubin 或 weak-IV-robust CI)
  • 排他性论证至少 3 段:理论 / 制度 / 安慰剂;说明工具只通过处理变量影响结果
  • 财经常用工具(地理 / 历史 / 政策外生 / Bartik 份额移动)的内生性也要论证
  • 是否报告 reduced form?
分支 C:RDD / 断点
  • 是否做了 McCrary / 密度检验(防止企业 / 地方在门槛附近操纵,如为享税收优惠调节规模)?
  • 带宽:最优带宽(Calonico-Cattaneo-Titiunik)+ 至少 3 个带宽稳健性
  • 协变量平滑性检验;模糊断点需报告一阶段跳跃
分支 D:DML / 双重机器学习
  • 用于高维协变量 / 非线性混淆:交叉拟合(cross-fitting)+ Neyman 正交
  • 报告不同 ML 学习器(lasso / random forest / boosting)下估计的稳健性
  • 说明可识别性仍依赖 CIA / 无未观测混淆假设,不是"上了机器学习就外生"
分支 E:结构估计 / 理论实证
  • 市场主体决策模型的微观基础是否清晰?
  • 识别假设是否明确列出?
  • 是否提供反事实(如税率 / 关税政策模拟)?

Execution bridge (StatsPAI / Stata MCP)

把设计跑出来并审计,而不是只做描述。完整映射见 execution-with-mcp。《财贸经济》是财经实证刊,政策评估与企业 / 城市 / 银行面板为主;突出识别与政策内生性处理。

  • detect_design → recommend → 用 as_handle=true 拟合 → audit_result 列出尚欠的检查。
  • **观察性因果:**交错 DID(callaway_santanna / sun_abraham + bacon_decomposition + honest_did_from_result);IV(effective_f_test + anderson_rubin_ci);RDD(rdrobust + mccrary_test);高维混淆用 DML(交叉拟合 + 多学习器稳健性)。
  • **实验 / 多结果:**随机化推断 + romano_wolf 做多结果族错误率控制。
  • 敏感性:oster_delta / sensemakr 评估未观测混淆。

正文报告经济量级(相当于均值百分比 / 折算金额),完整 battery 进附录;每个数字都能复现。端到端真跑示例见 JF 执行 walkthrough。若 StatsPAI / Stata 未连接,改用 resources/code/ 并标注未验证数字。

必查清单

  • 平行趋势 / 平滑性 / 弱工具 / 交叉拟合稳健性 检验都做了
  • 政策内生 / 反向因果 / 预期效应有明确处理(不是一句"我们假设外生")
  • 安慰剂检验做了(处理时点随机 / 处理组随机)
  • 标准误聚类层次合理(企业 / 城市 / 行业 / 政策推行层级)
  • 是否回应了"政策提前预期"问题(政策落地前的抢跑行为)

反模式

  • 纯描述性统计 + OLS 就下因果结论
  • 政策非随机投放却当外生冲击处理
  • TWFE + staggered 但不讨论异质性处理偏误
  • IV 用"历史 / 地理变量"但不论证它不通过其他渠道影响当代财经结果
  • RDD 用了门槛但不做密度检验(主体可能操纵分组)
  • 上了机器学习就宣称"控制了内生性"

输出格式

【识别策略】DID / IV / RDD / DML / 合成控制 / 结构估计
【政策内生处理】方式:[...]
【已完成检验】[平行趋势, 安慰剂, 弱工具, 交叉拟合, 密度检验, ...]
【缺失检验】[...]
【聚类层次】企业 / 城市 / 行业 / ...
【下一步】cte-mechanism

© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in Finance-and-Trade-Economics-Skills/skills/cte-identification of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

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Clinical Protocol Draftingaws-samples/amazon-bedrock-agents-healthcare-lifesciences274—~1.4kAutomated safety check: PassMIT-0
Bgpt Paper Searchagent-skills-hub/agent-skills-hub1113 repos~715Automated safety check: NotesMIT
Research Coordinatorbrycewang-stanford/Auto-Empirical-Research-Skills4.5k—~3.2kAutomated safety check: PassCustom licence

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Questions about Cte Identification

What does Cte Identification do?

A skill your agent uses when the empirical identification strategy is the bottleneck for a 《财贸经济》 manuscript — fiscal / tax / financial / trade policy shocks and firm- / city- / bank-level…. Cte Identification is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when the empirical identification strategy is the bottleneck for a 《财贸经济》 manuscript — fiscal / tax / financial / trade policy shocks and firm- / city- / bank-level quasi-experimental designs (DID, IV, RDD, DML).

When should I use Cte Identification?

Cte Identification fits situations like: tasks that involve Load testing; tasks that involve Experimental design.

How do I install Cte Identification in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cte-identification -a claude-code`. Or copy the skill folder (Finance-and-Trade-Economics-Skills/skills/cte-identification in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/cte-identification in your project. Claude Code loads it when a task matches its description.

How do I install Cte Identification in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cte-identification -a codex`. Or copy the skill folder (Finance-and-Trade-Economics-Skills/skills/cte-identification in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/cte-identification in your project. Codex loads it when a task matches its description.

Can I use Cte Identification in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cte-identification -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cte-identification, .gemini/skills/cte-identification, .github/skills/cte-identification and .opencode/skills/cte-identification in your project.

What does Cte Identification need to run?

SKILL.md names no scripts, command-line tools or credentials: Cte Identification is instructions for the agent only.

Does Cte Identification access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Cte Identification safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Cte Identification use?

Cte Identification is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Cte Identification use?

About 866 tokens (SKILL.md is roughly 3.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Cte Identification?

Skills that share tags, products or a category with Cte Identification: Research Refine (zjYao36/Auto-Research-Refine, 128 stars), Academic Grill (Exekiel179/psyclaw, 103 stars), Clinical Protocol Drafting (aws-samples/amazon-bedrock-agents-healthcare-lifesciences, 274 stars) and Bgpt Paper Search (agent-skills-hub/agent-skills-hub, 111 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cte Identification?

brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,228 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.

Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.