A skill your agent uses when the empirical identification of a macro shock or dynamic causal effect is the bottleneck for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript — SVAR…

MITAuto-check passedResearch & Science

Install Aejmac Identification

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

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills aejmac-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-Macroeconomics-Skills/skills/aejmac-identification .claude/skills/aejmac-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
aejmac-identification
GitHub stars
1.2k
Token cost
~1.8k tokens
SKILL.md length
794 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 of a macro shock or dynamic causal effect is the bottleneck for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript — SVAR…

  • Works in 5 steps: detect_design → recommend → fit with… → Staggered DiD: callaway_santanna /… → IV: effective_f_test + an… → …
  • The empirical identification of a macro shock
  • SKILL.md covers When to trigger, The AEJ: Macro identification…, Branch paths and Execution bridge (StatsPAI /…, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Aejmac Identification is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when the empirical identification of a macro shock or dynamic causal effect is the bottleneck for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript — SVAR, local projections, narrative, high-frequency/proxy-VAR, or micro-data macro designs. Stress-tests the identification to the AEJ: Macro broad-interest quantitative bar; for model-parameter identification see aejmac-theory-model.

Its SKILL.md is about 1.8k 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. 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 empirical identification of a macro shock
  • Dynamic causal effect is the bottleneck for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript — SVAR
  • Local projections
  • High-frequency/proxy-VAR

Example prompts

  • “/aejmac-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

Aejmac Identification loads about 1.8k tokens when it runs. Until then it costs about 107 tokens; SKILL.md has 794 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~107
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

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). 794 words, ~1,825 tokens.

Download SKILL.mdSave it as .claude/skills/aejmac-identification/SKILL.md (or your agent's skills folder).
name
aejmac-identification
description
Use when the empirical identification of a macro shock or dynamic causal effect is the bottleneck for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript — SVAR, local projections, narrative, high-frequency/proxy-VAR, or micro-data macro designs. Stress-tests the identification to the AEJ: Macro broad-interest quantitative bar; for model-parameter identification see aejmac-theory-model.

Empirical Identification (aejmac-identification)

When to trigger

  • The macro effect rests on a recursive (Cholesky) SVAR with no defense of the ordering
  • A monetary/fiscal "shock" is plausibly anticipated or endogenous to the cycle
  • Local projections are run but lag length, controls, and inference are ad hoc
  • A narrative or high-frequency instrument is used but its exogeneity/relevance is unargued
  • You are unsure the design clears AEJ: Macro's identified-empirical bar

The AEJ: Macro identification bar

AEJ: Macro publishes identified-empirical macro, so the mapping from data to the dynamic causal object (an impulse response, a multiplier, a pass-through) must be explicit and defended. The aggregate, time-series setting makes identification harder than in micro: few effective observations, anticipation, simultaneity, and structural breaks. State the shock you claim to identify, the assumption that delivers it, and the horizon and object you report. Report standard errors / confidence bands (the AEA house style; significance asterisks are conventional in AEA tables but the band/SE must carry the inference, not the stars).

Branch paths

Branch A: Structural VAR (SVAR)
  • Recursive (Cholesky): defend the ordering as an economic timing assumption, not a default; show robustness to plausible reorderings.
  • Sign restrictions: state the full set; acknowledge set-identification (report the identified set / median-target with a credible band, not a point as if point-identified); address the "multiple models" critique.
  • Long-run / Blanchard–Quah: justify the long-run neutrality assumption.
  • Proxy-VAR / external instruments (SVAR-IV): show instrument relevance (reliability/F) and defend exogeneity; report weak-instrument-robust bands where relevance is marginal.
Branch B: Local projections (LP)
  • Report the horizon-by-horizon IRF with bands; state lag length and control set and show robustness to them.
  • Use Newey–West / HAC or LP-specific inference; for panel LP cluster appropriately.
  • Consider LP-IV when the shock needs instrumenting; report the first-stage strength.
  • Address the LP-vs-VAR bias/variance trade-off explicitly if both are plausible.
Branch C: Narrative & high-frequency identification
  • Narrative shocks (Romer–Romer style monetary/fiscal/tax): document the construction, the source record, and why the series is exogenous to the cycle; show it is unpredictable from macro history.
  • High-frequency monetary surprises (event-window around announcements): defend the window, address the "Fed information effect" (orthogonalize against forecasts or use the information-robust instruments), report relevance.
Branch D: Micro-data macro / cross-sectional identification
  • Cross-sectional or regional designs aggregated to a macro statement (e.g., regional multipliers): state the general-equilibrium vs. partial-equilibrium gap and how you map the cross-sectional elasticity to the aggregate.
  • Use modern heterogeneity-robust estimators where staggered timing applies; cluster at the assignment level.

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the identification claim, don't only argue it. Full map: execution-with-mcp. AEJ: Macro mixes empirical and structural work — local projections (local_projections / irf) are in StatsPAI, but DSGE / calibration estimation is outside this causal-inference toolchain.

  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.

Show full SKILL.md (250 more words)Show less

Checklist

  • Branch chosen; the shock and the data-to-IRF mapping stated in one sentence
  • SVAR: ordering / sign set / long-run / proxy assumption defended, not defaulted
  • LP: lag length, controls, HAC inference stated; robustness to them shown
  • Narrative/HF: construction documented; exogeneity (unpredictability) demonstrated; relevance reported
  • Cross-sectional-to-aggregate: PE-vs-GE gap addressed
  • Inference: bands/SEs carry the conclusion; weak-instrument-robust where relevant
  • The macro claim never exceeds the horizon/object the design identifies

Anti-patterns

  • A Cholesky ordering presented as if it were innocuous, with no economic timing argument
  • Sign-restricted IRFs reported as point estimates, hiding set-identification
  • A "monetary shock" that is predictable from the prior quarter's data (anticipation not addressed)
  • High-frequency surprises used without confronting the Fed information effect
  • LP reported at a single cherry-picked horizon instead of the full response with bands
  • Mapping a regional/cross-sectional elasticity straight to an aggregate multiplier with no GE caveat

Worked vignette: identifying a monetary shock (illustrative)

A paper estimates the output response to monetary policy via a recursive SVAR ordered output → prices → policy rate. A referee says the ordering is indefensible at high frequency. The AEJ: Macro fix: replace (or corroborate) the recursive shock with a high-frequency surprise from a tight window around FOMC announcements, purged of the information effect by orthogonalizing against Greenbook/SPF forecasts, then feed it as an external instrument in a proxy-VAR or as the shock in local projections. Suppose the peak output response stabilizes at -0.6% (90% band [-1.0, -0.2]) and is robust across the SVAR-IV and LP implementations — that cross-method agreement is the identification argument.

Output format

【Branch】SVAR / LP / narrative-HF / cross-sectional-macro
【Shock + data-to-IRF mapping】one sentence
【Identifying assumption】ordering / sign set / exogeneity / GE mapping
【Inference】bands/SEs; weak-IV-robust if relevant; HAC/cluster choice
【Object + horizon reported】...
【What it does NOT identify】...
【Next step】aejmac-robustness (then aejmac-theory-model if a model is matched to this)

© 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-Macroeconomics-Skills/skills/aejmac-identification of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Aejmac 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.

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Questions about Aejmac Identification

What does Aejmac Identification do?

A skill your agent uses when the empirical identification of a macro shock or dynamic causal effect is the bottleneck for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript — SVAR…. Aejmac Identification is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when the empirical identification of a macro shock or dynamic causal effect is the bottleneck for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript — SVAR, local projections, narrative, high-frequency/proxy-VAR, or micro-data macro designs.

When should I use Aejmac Identification?

Aejmac Identification fits situations like: the empirical identification of a macro shock; dynamic causal effect is the bottleneck for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript — SVAR; local projections; high-frequency/proxy-VAR.

How do I install Aejmac Identification in Claude Code?

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

How do I install Aejmac Identification in Codex?

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

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

What does Aejmac Identification need to run?

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

Does Aejmac 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 Aejmac 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 Aejmac Identification use?

Aejmac 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 Aejmac Identification use?

About 1.8k tokens (SKILL.md is roughly 7.3k 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 Aejmac Identification?

Skills that share tags, products or a category with Aejmac Identification: What If Oracle (K-Dense-AI/scientific-agent-skills, 48k stars), Paper Review (EvoScientist/EvoSkills, 476 stars), Data Finder (brycewang-stanford/Auto-Empirical-Research-Skills, 4.5k stars) and Weakness Scanner (flonat/flonat-research, 146 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Aejmac 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.