Agent skill

Jfi Identification Strategy

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

A skill your agent uses when auditing the core analytical engine of a Journal of Financial Intermediation (JFI) paper — for empirics, the causal design that separates credit supply from demand in…

MITAuto-check passedResearch & Science

Install Jfi Identification Strategy

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

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jfi-identification-strategy --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-Financial-Intermediation-Skills/skills/jfi-identification-strategy .claude/skills/jfi-identification-strategy && 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
jfi-identification-strategy
GitHub stars
1.2k
Token cost
~1.6k tokens
SKILL.md length
699 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when auditing the core analytical engine of a Journal of Financial Intermediation (JFI) paper — for empirics, the causal design that separates credit supply from demand in…

  • Works in 5 steps: detect_design → recommend → fit with… → Staggered DiD: callaway_santanna /… → IV: effective_f_test + an… → …
  • Auditing the core analytical engine of a Journal of Financial Intermediation (JFI) paper — for empirics
  • SKILL.md covers When to trigger, Empirical track (applied…, Theory track (intermediation… and The within-firm benchmark, and…, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Jfi Identification Strategy is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when auditing the core analytical engine of a Journal of Financial Intermediation (JFI) paper — for empirics, the causal design that separates credit supply from demand in banking data; for theory, the assumptions, equilibrium discipline, and proof exposition. It pressure-tests the design; it does not run the analysis.

Its SKILL.md is about 1.6k 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

  • Auditing the core analytical engine of a Journal of Financial Intermediation (JFI) paper — for empirics
  • The causal design that separates credit supply from demand in banking data
  • The assumptions
  • Equilibrium discipline

Example prompts

  • “/jfi-identification-strategy”

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

Jfi Identification Strategy loads about 1.6k tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 699 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~88
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

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). 699 words, ~1,576 tokens.

Download SKILL.mdSave it as .claude/skills/jfi-identification-strategy/SKILL.md (or your agent's skills folder).
name
jfi-identification-strategy
description
Use when auditing the core analytical engine of a Journal of Financial Intermediation (JFI) paper — for empirics, the causal design that separates credit supply from demand in banking data; for theory, the assumptions, equilibrium discipline, and proof exposition. It pressure-tests the design; it does not run the analysis.

Identification Strategy (jfi-identification-strategy)

When to trigger

  • Setting up or defending the empirical design of a banking/intermediation paper
  • Setting up or defending the assumptions and propositions of a theory paper

Empirical track (applied banking / credit)

JFI referees are unforgiving on identification in bank data. Build a credible causal design and defend it:

  • Source of variation: a regulatory change, supervisory shock, branching deregulation, a discontinuity in capital/eligibility rules, or a plausibly exogenous credit-supply shifter.
  • Modern estimators: staggered DID with heterogeneity-robust estimators (Callaway–Sant'Anna, Sun–Abraham, de Chaisemartin–D'Haultfœuille), IV with weak-IV-robust inference, or RDD with the rdrobust toolkit.
  • Bank-data-specific threats: bank selection into treatment, borrower–firm sorting, balance-sheet timing and mechanical reverse causality, and the lending-channel separation of credit supply from demand (firm×time fixed effects in matched lender–borrower panels).
  • Inference: cluster at the level of treatment assignment (often bank or market); wild-cluster bootstrap when clusters are few.

Theory track (intermediation models)

When the contribution is a model, identification means analytical discipline:

  • State assumptions transparently and motivate each economically (what friction it encodes).
  • Make results precise as propositions/lemmas; keep proof exposition readable — sketch the mechanism in the text, full proofs in an appendix.
  • Argue generality: which results survive relaxed assumptions, and where the boundary lies.
  • A numerical example (see jfi-data-analysis) can illustrate the mechanism without claiming empirical estimation.

The within-firm benchmark, and when it is not enough

The Khwaja–Mian within-firm estimator is this community's default answer to demand confounds: with multi-bank firms, firm×time fixed effects difference out borrower demand and isolate the credit-supply channel. A JFI referee then pushes past the default:

  • Multi-bank firms are larger and less bank-dependent — show what the design's external margin (single-relationship firms) does, or bound how far the within-firm estimate travels.
  • "Equal demand across a firm's lenders" is itself an assumption: a firm may cut demand for one bank's specialized product. Address with loan-purpose controls or product-level fixed effects.
  • Firm-level real outcomes cannot carry firm×time FE; aggregate the bank shock to the firm with pre-period exposure shares, and defend share exogeneity as in shift-share designs.

Design selection for common intermediation shocks

Variation exploitedDefault designVenue-specific threat to pre-empt
Staggered regulation/deregulation across states or countriesHeterogeneity-robust staggered DIDBanks lobby for timing — show treatment is not predicted by pre-trend bank health
Capital- or size-threshold ruleRDD with density testBanks bunch by managing the ratio; McCrary check is mandatory
Funding or deposit shock with differential exposureExposure (shift-share) designExposure shares correlate with local demand — balance on borrower observables
Run or crisis windowHigh-frequency event designMechanical balance-sheet timing; reverse causality from borrower distress
Show full SKILL.md (277 more words)Show less

Worked contrast: one estimate, two readings (illustrative)

A 1pp funding shock reduces bank-level lending by 2.8pp (bank panel, OLS). At JFI that is not yet a result: the same number is consistent with shocked banks happening to serve shocked borrowers. The within-firm version at 1.6pp (firm×time FE) is the publishable object — and the 1.2pp gap becomes evidence on borrower–bank sorting worth its own paragraph, not a nuisance to hide. JFI referees read the movement of the coefficient across fixed-effect columns as a diagnostic in itself; design the identification section so that movement is interpreted, not merely displayed.

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the identification claim, don't only argue it. Full map: execution-with-mcp. JFI is banking and financial intermediation — typically corporate / bank causal designs built around regulation and shocks.

  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.

Anti-patterns

  • OLS-plus-controls dressed up as identification on a bank panel
  • Conflating credit supply and demand without firm×time absorption
  • A theory whose key result silently depends on an unstated assumption
  • Clustering at the wrong level, or ignoring few-cluster inference

Output format

【Track】empirical / theory
【Design or assumptions】<the variation, or the key assumptions>
【Top threat / boundary】<the main objection + answer>
【Inference / generality】<clustering, or which results survive>
【Next skill】jfi-data-analysis

© 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-Financial-Intermediation-Skills/skills/jfi-identification-strategy of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

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Questions about Jfi Identification Strategy

What does Jfi Identification Strategy do?

A skill your agent uses when auditing the core analytical engine of a Journal of Financial Intermediation (JFI) paper — for empirics, the causal design that separates credit supply from demand in…. Jfi Identification Strategy is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when auditing the core analytical engine of a Journal of Financial Intermediation (JFI) paper — for empirics, the causal design that separates credit supply from demand in banking data; for theory, the assumptions, equilibrium discipline, and proof exposition.

When should I use Jfi Identification Strategy?

Jfi Identification Strategy fits situations like: auditing the core analytical engine of a Journal of Financial Intermediation (JFI) paper — for empirics; the causal design that separates credit supply from demand in banking data; the assumptions; equilibrium discipline.

How do I install Jfi Identification Strategy in Claude Code?

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

How do I install Jfi Identification Strategy in Codex?

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

Can I use Jfi Identification Strategy 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 jfi-identification-strategy -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/jfi-identification-strategy, .gemini/skills/jfi-identification-strategy, .github/skills/jfi-identification-strategy and .opencode/skills/jfi-identification-strategy in your project.

What does Jfi Identification Strategy need to run?

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

Does Jfi Identification Strategy 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 Jfi Identification Strategy 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 Jfi Identification Strategy use?

Jfi Identification Strategy 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 Jfi Identification Strategy use?

About 1.6k tokens (SKILL.md is roughly 6.3k 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 Jfi Identification Strategy?

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Who maintains Jfi Identification Strategy?

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.