New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Aejpol Identification

by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills

A skill your agent uses when the credibility of the causal evaluation of a policy is the bottleneck for an AEJ: Economic Policy manuscript — DID/event study, IV, RDD/bunching, or RCT of a program.

MITAuto-check passedTesting & QA

Install Aejpol Identification

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

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills aejpol-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/AEJ-Economic-Policy-Skills/skills/aejpol-identification .claude/skills/aejpol-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
aejpol-identification
GitHub stars
1.2k
Token cost
~1.7k tokens
SKILL.md length
723 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when the credibility of the causal evaluation of a policy is the bottleneck for an AEJ: Economic Policy manuscript — DID/event study, IV, RDD/bunching, or RCT of a program.

  • Works in 5 steps: detect_design → recommend → fit with… → Staggered DiD: callaway_santanna /… → IV: effective_f_test + an… → …
  • The credibility of the causal evaluation of a policy is the bottleneck for an AEJ: Economic Policy manuscript — DID/event study
  • SKILL.md covers When to trigger, The AEJ: Policy identification…, Design paths and Execution bridge (StatsPAI /…, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Aejpol Identification is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when the credibility of the causal evaluation of a policy is the bottleneck for an AEJ: Economic Policy manuscript — DID/event study, IV, RDD/bunching, or RCT of a program. Stress-tests the quasi-experimental policy-evaluation design to the AEJ: Policy bar before exhibits are finalized; it does not build the welfare mapping or write exhibits.

Its SKILL.md is about 1.7k 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 Testing & QA, covering Load testing. 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

  • The credibility of the causal evaluation of a policy is the bottleneck for an AEJ: Economic Policy manuscript — DID/event study
  • RCT of a program

Example prompts

  • “/aejpol-identification”

Workflow steps

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

  1. detect_design → recommend → fit with as_handle=true → audit_result to list
  2. Staggered DiD: callaway_santanna / sun_abraham + bacon_decomposition +
  3. IV: effective_f_test + an anderson_rubin_ci (valid under weak instruments),
  4. RDD: rdrobust (bias-corrected) + rddensity / mccrary_test for manipulation.
  5. OVB: oster_delta / sensemakr — how strong a confounder would have to be.

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

Aejpol Identification loads about 1.7k tokens when it runs. Until then it costs about 93 tokens; SKILL.md has 723 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~93
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

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). 723 words, ~1,665 tokens.

Download SKILL.mdSave it as .claude/skills/aejpol-identification/SKILL.md (or your agent's skills folder).
name
aejpol-identification
description
Use when the credibility of the causal evaluation of a policy is the bottleneck for an AEJ: Economic Policy manuscript — DID/event study, IV, RDD/bunching, or RCT of a program. Stress-tests the quasi-experimental policy-evaluation design to the AEJ: Policy bar before exhibits are finalized; it does not build the welfare mapping or write exhibits.

Identification — Credible Policy Evaluation (aejpol-identification)

When to trigger

  • The causal effect of a policy rests on OLS + controls, or TWFE on staggered policy adoption
  • A reform / threshold / experiment exists but the design's assumptions are not pinned down
  • A referee questions whether the estimated effect is really caused by the policy
  • You are unsure the design clears AEJ: Policy's credible-causal-evidence bar

The AEJ: Policy identification bar

AEJ: Policy is an empirical policy journal: the effect attributed to the policy must be credibly causal, the estimand must be the policy-relevant one, and the design must survive the obvious confound that the policy was not random. The policy variation is the research design — name it explicitly (a reform date, an eligibility cutoff, a formula kink, a randomized rollout) and defend the assumption that makes it causal. Report standard errors (no significance asterisks; see aejpol-tables-figures) and make the design reproducible for the AEA Data Editor.

Design paths

Path A: DID / event study (reforms, staggered policy adoption)
  • With staggered adoption move beyond TWFE (Callaway–Sant'Anna, Sun–Abraham, Borusyak–Jaravel–Spiess, de Chaisemartin–D'Haultfœuille); report a Goodman-Bacon decomposition to show the bias TWFE would induce.
  • Show a clean event study with pre-period leads flat around zero; do not assert parallel trends, demonstrate it (and probe with Rambachan–Roth honest-DID where pre-trends are imperfect).
  • Define the policy-relevant estimand (ATT on treated jurisdictions; weight by population/exposure if the policy lesson requires it).
  • Cluster at the policy-assignment level (often state/jurisdiction); address few-cluster issues (wild-cluster bootstrap).
Path B: IV / instrumented policy exposure
  • Strong first stage; with weak instruments use Anderson–Rubin / weak-IV-robust sets and report the effective F.
  • Defend the exclusion restriction in institutions and theory, not just statistically; argue the instrument affects outcomes only through the policy channel.
  • State the LATE complier population and whether it is the policy-relevant margin.
Path C: RDD / bunching (eligibility thresholds, tax/benefit schedules)
  • RDD: McCrary / Cattaneo–Jansson–Ma density test; data-driven bandwidth; covariate smoothness at the cutoff; bias-corrected robust CIs (rdrobust).
  • Bunching at kinks/notches in tax or benefit schedules: defend the counterfactual density and the structural elasticity it implies.
  • Be explicit that the estimate is local to the threshold and argue its policy relevance.
Path D: RCT / field experiment of a program
  • Pre-registration with a pre-analysis plan; report deviations. Detailed instructions / protocol included.
  • Randomization balance; attrition (Lee bounds if differential); multiple-hypothesis adjustment; explicit estimand and a take-up / intent-to-treat vs. treatment-on-treated distinction.
  • Tie the experimental effect to the cost of the program so a welfare reading is possible.
Show full SKILL.md (319 more words)Show less

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the identification claim, don't only argue it. Full map: execution-with-mcp. AEJ: Policy evaluates programs and reforms; the design must carry a policy-relevant magnitude, not just statistical significance.

  1. detect_design → recommend → fit with as_handle=true → audit_result to list the checks the design still owes.
  2. Staggered DiD: callaway_santanna / sun_abraham + bacon_decomposition + honest_did_from_result (the pre-trend test is low-power, Roth 2022).
  3. IV: effective_f_test + an anderson_rubin_ci (valid under weak instruments), not a 2SLS t-stat alone.
  4. RDD: rdrobust (bias-corrected) + rddensity / mccrary_test for manipulation.
  5. OVB: oster_delta / sensemakr — how strong a confounder would have to be.

Report the economic magnitude; route the full battery to the appendix; keep every number reproducible. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough. If StatsPAI/Stata are not connected, adapt the vendored resources/code/ skeleton and flag any unverified number.

Checklist

  • The policy variation is named and the identifying assumption stated in one sentence
  • Design-appropriate diagnostics shown (pre-trends / density / first-stage F / balance)
  • Modern heterogeneity-robust estimator used where TWFE would bias
  • Estimand is the policy-relevant one (right population, right weighting)
  • Inference clustered at the assignment level; few-cluster handled
  • SEs reported (no asterisks); the causal claim never exceeds what the design supports

Anti-patterns

  • TWFE on staggered policy rollout with no heterogeneity-bias discussion
  • Asserting parallel trends instead of showing flat, precisely-estimated leads
  • An exclusion restriction defended only by a significant first stage
  • An RDD estimate generalized far from the cutoff without argument
  • An RCT with no pre-registration, no attrition analysis, or no link to program cost
  • Reporting significance with asterisks instead of standard errors

Referee pushback mapped to the fix

  • "Staggered TWFE here is biased." → Re-estimate with Callaway–Sant'Anna / Sun–Abraham; show flat leads + Bacon decomposition.
  • "Pre-trends look slightly off." → Honest-DID (Rambachan–Roth) bounds; show the conclusion survives plausible violations.
  • "This is just the effect at the threshold." → State the local estimand; argue why the threshold population is policy-relevant or extrapolate cautiously.

Output format

【Design】DID / IV / RDD-bunching / RCT
【Policy variation】the reform/cutoff/rollout that identifies the effect
【Identifying assumption】one sentence + how it is defended
【Diagnostics shown】[pre-trends / density / first-stage F / balance + attrition]
【Estimand】policy-relevant population + weighting; inference/clustering
【What it does NOT identify】[...]
【Next step】aejpol-theory-model (welfare mapping) or aejpol-robustness

© 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 AEJ-Economic-Policy-Skills/skills/aejpol-identification of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Aejpol Identification next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Aejpol Identification compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Aejpol Identification this skillbrycewang-stanford/Awesome-Journal-Skills1.2k—~1.7kAutomated safety check: PassMIT
Get Available ResourcesK-Dense-AI/scientific-agent-skills48k1 repos~2.9kAutomated safety check: PassMIT
What If OracleLeonChaoX/qinyan-academic-skills9431 repos~2.2kAutomated safety check: PassMIT
Cpa Identificationfranklee16/academic-research-skills2231 repos~614Automated safety check: PassNone
Ecta Robustnessfranklee16/academic-research-skills2231 repos~1.4kAutomated safety check: PassNone
Jcr Topic Selectionfranklee16/academic-research-skills2231 repos~1kAutomated safety check: PassNone

Similar skills

  • Get Available Resources

    K-Dense-AI/scientific-agent-skills

    Detects host inventory and effective CPU, memory, disk, scheduler, container, and accelerator limits when a user asks for resource-aware planning or before a clearly resource-sensitive local workload.

    48k GitHub starsUsed in 1 repo~2.9k tokens
    Testing & QAAuto-check passed

  • What If Oracle

    LeonChaoX/qinyan-academic-skills

    Run structured What-If scenario analysis with multi-branch possibility exploration.

    943 GitHub starsUsed in 1 repo~2.2k tokens
    Testing & QAAuto-check passed

  • Cpa Identification

    franklee16/academic-research-skills

    A skill your agent uses when the research design is the bottleneck for a 《中国行政管理》 manuscript — choosing and stress-testing a quantitative, qualitative, or normative design and matching it to the…

    223 GitHub starsUsed in 1 repo~614 tokens
    Testing & QAAuto-check passed

  • Ecta Robustness

    franklee16/academic-research-skills

    A skill your agent uses when an Econometrica manuscript needs finite-sample evidence and edge-case scrutiny — Monte Carlo design, finite-sample performance, regularity-condition stress tests, and…

    223 GitHub starsUsed in 1 repo~1.4k tokens
    Testing & QAAuto-check passed

  • Jcr Topic Selection

    franklee16/academic-research-skills

    A skill your agent uses when shaping or stress-testing a research question for the Journal of Consumer Research (JCR) — confirming it is a genuine consumer-behavior question with a conceptual…

    223 GitHub starsUsed in 1 repo~1k tokens
    Testing & QAAuto-check passed

  • Misq Topic Selection

    franklee16/academic-research-skills

    A skill your agent uses when shaping or stress-testing a research question for MIS Quarterly — confirming it is a genuine information-systems question, naming which of the four IS traditions…

    223 GitHub starsUsed in 1 repo~1k tokens
    Testing & QAAuto-check passed

More from brycewang-stanford/Awesome-Journal-Skills

All 2,387 skills in this repo

  • Aaag Data Analysis

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when running and reporting the analysis for an Annals of the American Association of Geographers manuscript — spatial statistics and modeling, remote-sensing accuracy, or…

    1.2k GitHub stars~1.3k tokensUpdated 12 days ago
    Auto-check passed

  • Aaag Literature Positioning

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when positioning an Annals of the American Association of Geographers manuscript in the literature — engaging geographic scholarship across the relevant area and the…

    1.2k GitHub stars~1.3k tokensUpdated 12 days ago
    Auto-check passed

  • Aaag Rebuttal

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when responding to an Annals of the American Association of Geographers decision letter (major/minor revision) — building a point-by-point response to the subject editor and…

    1.2k GitHub stars~1.4k tokensUpdated 12 days ago
    Auto-check passed

  • Aaag Research Design

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when defending the research design of an Annals of the American Association of Geographers manuscript — spatial/quantitative analysis and GIScience, remote-sensing and…

    1.2k GitHub stars~1.4k tokensUpdated 12 days ago
    Auto-check passed

  • Aaag Review Process

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when you need to understand how the Annals of the American Association of Geographers evaluates a manuscript — double-anonymous review routed through a subject editor by…

    1.2k GitHub stars~1.3k tokensUpdated 12 days ago
    Auto-check passed

  • Aaag Submission

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when running the final pre-submission preflight for the Annals of the American Association of Geographers via ScholarOne Manuscripts — area/article-type selection…

    1.2k GitHub stars~1.6k tokensUpdated 12 days ago
    Auto-check passed

Categories

Questions about Aejpol Identification

What does Aejpol Identification do?

A skill your agent uses when the credibility of the causal evaluation of a policy is the bottleneck for an AEJ: Economic Policy manuscript — DID/event study, IV, RDD/bunching, or RCT of a program. Aejpol Identification is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when the credibility of the causal evaluation of a policy is the bottleneck for an AEJ: Economic Policy manuscript — DID/event study, IV, RDD/bunching, or RCT of a program.

When should I use Aejpol Identification?

Aejpol Identification fits situations like: the credibility of the causal evaluation of a policy is the bottleneck for an AEJ: Economic Policy manuscript — DID/event study; RCT of a program.

How do I install Aejpol Identification in Claude Code?

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

How do I install Aejpol Identification in Codex?

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

Can I use Aejpol 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 aejpol-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/aejpol-identification, .gemini/skills/aejpol-identification, .github/skills/aejpol-identification and .opencode/skills/aejpol-identification in your project.

What does Aejpol Identification need to run?

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

Does Aejpol 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 Aejpol 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 Aejpol Identification use?

Aejpol 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 Aejpol Identification use?

About 1.7k tokens (SKILL.md is roughly 6.7k 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 Aejpol Identification?

Skills that share tags, products or a category with Aejpol Identification: Get Available Resources (K-Dense-AI/scientific-agent-skills, 48k stars), What If Oracle (LeonChaoX/qinyan-academic-skills, 943 stars), Cpa Identification (franklee16/academic-research-skills, 223 stars) and Ecta Robustness (franklee16/academic-research-skills, 223 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Aejpol 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.