A skill your agent uses when bank/central-bank data construction, measurement, or sample design is the bottleneck for a Journal of Money, Credit and Banking (JMCB) manuscript.

MITAuto-check passedResearch & Science

Install Jmcb Empirical Design

skills CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jmcb-empirical-design -a claude-code

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jmcb-empirical-design --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/Journal-of-Money-Credit-and-Banking-Skills/skills/jmcb-empirical-design .claude/skills/jmcb-empirical-design && 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
jmcb-empirical-design
GitHub stars
1.2k
Token cost
~2.2k tokens
SKILL.md length
1,033 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when bank/central-bank data construction, measurement, or sample design is the bottleneck for a Journal of Money, Credit and Banking (JMCB) manuscript.

  • Bank/central-bank data construction
  • SKILL.md covers When to trigger, The JMCB measurement bar, Construction craft by data type and Sample and specification hygiene, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Sample design is the bottleneck for a Journal of Money

What it does

Jmcb Empirical Design is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when bank/central-bank data construction, measurement, or sample design is the bottleneck for a Journal of Money, Credit and Banking (JMCB) manuscript. Hardens how the dataset is built and measured so the identification can do its job; it does not re-argue the causal strategy or write prose.

Its SKILL.md is about 2.2k 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. 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

  • Bank/central-bank data construction
  • Sample design is the bottleneck for a Journal of Money
  • Credit and Banking (JMCB) manuscript

Example prompts

  • “/jmcb-empirical-design”

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

Jmcb Empirical Design loads about 2.2k tokens when it runs. Until then it costs about 80 tokens; SKILL.md has 1,033 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~80
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k

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). 1,033 words, ~2,204 tokens.

Download SKILL.mdSave it as .claude/skills/jmcb-empirical-design/SKILL.md (or your agent's skills folder).
name
jmcb-empirical-design
description
Use when bank/central-bank data construction, measurement, or sample design is the bottleneck for a Journal of Money, Credit and Banking (JMCB) manuscript. Hardens how the dataset is built and measured so the identification can do its job; it does not re-argue the causal strategy or write prose.

Empirical Design (jmcb-empirical-design)

When to trigger

  • The dataset is assembled from Call Reports, Y-9C, supervisory, credit-register, or central-bank sources and the construction is under-documented
  • The key variable (a "monetary shock," a "bank capital ratio," a "credit-supply" measure) is a constructed object whose definition matters for the result
  • Sample period, window, or frequency choices are not motivated and could be driving the finding
  • Restricted-access bank/central-bank data are involved and the access path is unstated
  • A referee questioned whether the measurement, not the mechanism, produces the result

The JMCB measurement bar

JMCB carries a deep replication heritage — the journal's own 1980s–2000s Data Archive episodes (Dewald–Thursby–Anderson; the 2006 "Got Replicability?" audit) made it acutely aware that monetary/banking results often hinge on how series are spliced, deflated, and aligned. So referees scrutinize construction and timing: how a series is seasonally adjusted, how regulatory definitions changed mid-sample, how a bank merger reshapes a panel, and whether the announcement window for a monetary surprise is defensible. The standard is that a reader could rebuild the central variables from the description.

Construction craft by data type

Bank micro-data (Call Reports / Y-9C / credit registers)
  • Identifier hygiene: track RSSD/entity IDs through mergers and acquisitions; state how you treat acquirers vs. targets so a merger does not masquerade as growth.
  • Balance-sheet ratios: define capital, liquidity, and lending consistently across the sample; note Basel/regulatory regime changes that redefine the numerator or denominator mid-panel.
  • Winsorize/trim extreme ratios and state the rule; report how many bank-quarters are dropped and why.
Monetary / macro series
  • Real-time vs. revised data: for policy questions use the vintage the policymaker saw (ALFRED / real-time databases); say which and why.
  • Frequency and alignment: state how high-frequency surprises are aggregated to the estimation frequency and how announcement timestamps map to observations.
  • Splicing and deflation: document base years, deflators, and any series breaks (e.g., reserve-regime or reference-rate transitions).
Central-bank / supervisory / restricted data
  • Access path: name the RDC / central-bank data room / register and the disclosure constraints; this scopes what the replication package can contain.
  • Confidentiality: state aggregation/masking rules and how they affect inference.

Sample and specification hygiene

  • Motivate the sample window from the institution/policy, not from where significance appears.
  • Pre-specify the frequency (the local-projection horizon, the panel frequency) and show the headline survives nearby choices.
  • Document missing-data and entry/exit handling so survivorship does not drive results.

The construction decisions referees probe most

A JMCB referee will mentally re-run your data build and ask where it could have gone wrong. The recurring pressure points:

  • Seasonal adjustment and deflation. State the SA method and base year; an unstated SA choice can manufacture or erase a cyclical pattern.
  • Reference-rate and regime transitions. LIBOR→SOFR, reserve-regime changes, and the move to ample reserves all break series; splice them explicitly and note the break date.
  • Treatment timing. For a policy/regulation study, the exact effective date and any anticipation window matter; misdated treatment biases event studies.
  • Aggregation level. Holding-company (Y-9C) vs. bank (Call Report) reporting answers different questions; pick the level that matches the mechanism and say why.
  • Survivorship. Failed and acquired banks leaving the panel during a crisis is not random; show the result is not an artifact of who exits.

From measurement to a credible replication path

Measurement and reproducibility are the same discipline at JMCB. As you finalize the data build, write the construction in enough detail that the eventual replication package — or, for restricted data, the documented access path — lets someone rebuild the central variables. Note which inputs are public (Call Reports, Y-9C, FRED, ALFRED real-time vintages) and which require restricted access (supervisory panels, credit registers, RDC), since this determines what jmcb-internet-appendix can include. A measurement section that doubles as a reproduction recipe pre-empts the journal's signature replication concern.

Show full SKILL.md (420 more words)Show less

Execution bridge (StatsPAI / Stata MCP)

Run the asset-pricing battery, don't just specify it. Full map: execution-with-mcp. JMCB is monetary/banking — macro time series + bank panels; local projections for the macro lane, DiD/IV for the bank lane.

  • Factor regressions / time-series alphas: feols with the right SEs (Newey–West / clustered) — read the alpha and t off the return.
  • Factor-zoo haircut: after disclosing how many signals were screened, apply romano_wolf / benjamini_hochberg and report the alpha that survives.
  • Fama–MacBeth + Shanken EIV are Stata-canonical — run via mcp__stata-mcp__stata_do with the vendored resources/code/ (asreg / xtfmb).
  • Exhibits: etable; hand formatting to the tables/figures skill.

Report the economic magnitude (bps/month alpha, Sharpe gain); full factor grid → appendix. JF execution walkthrough.

Checklist

  • Every constructed key variable has a definition a reader could rebuild
  • Bank IDs tracked through M&A; merger treatment stated
  • Regulatory/definitional regime changes within the sample flagged and handled
  • Real-time vs. revised data choice stated and justified for policy questions
  • Window/frequency motivated by institutions, not by significance; headline survives nearby choices
  • Restricted-data access path and disclosure constraints documented
  • Winsorizing/trimming and missing-data rules stated with counts

Anti-patterns

  • A constructed "shock" or ratio whose definition is buried, so the result cannot be reproduced
  • Bank mergers silently inflating growth because acquirer/target handling is unspecified
  • Using revised data for a real-time policy question (or vice versa) without saying so
  • A sample window or estimation horizon that quietly maximizes significance
  • Restricted-data results with no statement of what the replication package can and cannot include
  • Splicing series across a regime break (reserve regime, reference-rate transition) without noting it

Public-data first, restricted-data when the mechanism demands it

Not every JMCB question needs supervisory access. Call Reports and Y-9C (bank balance sheets and income), FRED and ALFRED (macro series and real-time vintages), and disclosed monetary-surprise datasets carry a large share of publishable transmission and banking work, and they make the replication path trivial. Reserve restricted data (credit registers, supervisory loan-level panels, RDC products) for mechanisms that genuinely require within-firm-across-bank or loan-level variation. Choosing the lightest data that identifies the mechanism is both a feasibility win and a reproducibility win.

Worked vignette (illustrative)

A paper measures the deposits channel using bank-level deposit betas. A referee notes the panel grows 30% over the sample and asks whether mergers drive it. The fix: build a merger-adjusted panel that aggregates acquirer+target pre-merger, recompute betas, and show the deposit-beta gradient is unchanged (e.g., high-branch-density banks pass through 40% of rate hikes vs. 70% for low-density, illustrative). Documenting the RSSD crosswalk and the winsorization rule turns a fragile measurement into a defensible one.

Output format

text
【Journal】Journal of Money, Credit and Banking
【Skill】jmcb-empirical-design
【Data sources】Call Report / Y-9C / register / central-bank / macro series
【Key constructed variable】definition a reader could rebuild
【Timing/measurement risk】real-time vs revised / window / regime change handled
【Sample hygiene】M&A, entry/exit, winsorizing, counts
【Access constraints】restricted-data path + disclosure limits (if any)
【Next skill】jmcb-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 Journal-of-Money-Credit-and-Banking-Skills/skills/jmcb-empirical-design of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

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Questions about Jmcb Empirical Design

What does Jmcb Empirical Design do?

A skill your agent uses when bank/central-bank data construction, measurement, or sample design is the bottleneck for a Journal of Money, Credit and Banking (JMCB) manuscript. Jmcb Empirical Design is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when bank/central-bank data construction, measurement, or sample design is the bottleneck for a Journal of Money, Credit and Banking (JMCB) manuscript.

When should I use Jmcb Empirical Design?

Jmcb Empirical Design fits situations like: bank/central-bank data construction; sample design is the bottleneck for a Journal of Money; credit and Banking (JMCB) manuscript.

How do I install Jmcb Empirical Design in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jmcb-empirical-design -a claude-code`. Or copy the skill folder (Journal-of-Money-Credit-and-Banking-Skills/skills/jmcb-empirical-design in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/jmcb-empirical-design in your project. Claude Code loads it when a task matches its description.

How do I install Jmcb Empirical Design in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jmcb-empirical-design -a codex`. Or copy the skill folder (Journal-of-Money-Credit-and-Banking-Skills/skills/jmcb-empirical-design in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/jmcb-empirical-design in your project. Codex loads it when a task matches its description.

Can I use Jmcb Empirical Design 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 jmcb-empirical-design -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/jmcb-empirical-design, .gemini/skills/jmcb-empirical-design, .github/skills/jmcb-empirical-design and .opencode/skills/jmcb-empirical-design in your project.

What does Jmcb Empirical Design need to run?

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

Does Jmcb Empirical Design 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 Jmcb Empirical Design 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 Jmcb Empirical Design use?

Jmcb Empirical Design 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 Jmcb Empirical Design use?

About 2.2k tokens (SKILL.md is roughly 8.8k 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 Jmcb Empirical Design?

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Who maintains Jmcb Empirical Design?

brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 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.