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Agent skill

Jfm Identification

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

A skill your agent uses when the identification argument is the bottleneck for a Journal of Financial Markets (JFM) manuscript — causal effects on market quality, or what pins down a microstructure…

MITAuto-check passedResearch & Science

Install Jfm Identification

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

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jfm-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/Journal-of-Financial-Markets-Skills/skills/jfm-identification .claude/skills/jfm-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
jfm-identification
GitHub stars
1.2k
Token cost
~2.6k tokens
SKILL.md length
1,209 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when the identification argument is the bottleneck for a Journal of Financial Markets (JFM) manuscript — causal effects on market quality, or what pins down a microstructure…

  • Works in 5 steps: detect_design → recommend → fit with… → Staggered DiD: callaway_santanna /… → IV: effective_f_test + an… → …
  • The identification argument is the bottleneck for a Journal of Financial Markets (JFM) manuscript — causal effects on market quality
  • SKILL.md covers When to trigger, The JFM identification bar, Referee pushback mapped to the… and Separating mechanical from…, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Jfm Identification is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when the identification argument is the bottleneck for a Journal of Financial Markets (JFM) manuscript — causal effects on market quality, or what pins down a microstructure model. Stress-tests the design against JFM's microstructure-insider bar before exhibits are finalized.

Its SKILL.md is about 2.6k 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, 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 identification argument is the bottleneck for a Journal of Financial Markets (JFM) manuscript — causal effects on market quality
  • What pins down a microstructure model

Example prompts

  • “/jfm-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

Jfm Identification loads about 2.6k tokens when it runs. Until then it costs about 75 tokens; SKILL.md has 1,209 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~75
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k

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). 1,209 words, ~2,574 tokens.

Download SKILL.mdSave it as .claude/skills/jfm-identification/SKILL.md (or your agent's skills folder).
name
jfm-identification
description
Use when the identification argument is the bottleneck for a Journal of Financial Markets (JFM) manuscript — causal effects on market quality, or what pins down a microstructure model. Stress-tests the design against JFM's microstructure-insider bar before exhibits are finalized.

Identification Strategy (jfm-identification)

When to trigger

  • A causal claim about liquidity, spreads, depth, or price discovery rests on OLS + controls
  • An event study uses a window so wide that confounding news or contemporaneous market-wide shocks contaminate it
  • A market-structure change is exploited but the parallel-trends / no-anticipation logic is not argued
  • Reverse causality is live: liquidity and the regressor (volume, volatility, ownership) are jointly determined
  • A microstructure model is fit but it is unclear what moment in the data identifies the key parameter (PIN, lambda, adverse-selection share)

The JFM identification bar

JFM referees know that market-quality variables are endogenous to almost everything — volume, volatility, information arrival, and prices co-move mechanically. So the bar is high for any causal liquidity/price-impact claim, and the journal especially rewards designs built on exogenous changes in market structure. The credible JFM toolkit is design-based, not control-saturated.

Branch A: Market-structure natural experiments (the JFM sweet spot)
  • Canonical shocks: decimalization, the SEC Tick-Size Pilot (2016-18), Reg NMS, MiFID/MiFID II, short-sale bans, circuit-breaker/LULD triggers, venue entry/exit, fee/rebate (maker-taker) changes, tick-size regime changes.
  • Design: DiD across affected vs. unaffected stocks/venues; RDD at a price/market-cap threshold (e.g., tick-size eligibility); event study around a known implementation date.
  • With staggered timing, move beyond TWFE (Callaway-Sant'Anna, Sun-Abraham, de Chaisemartin-D'Haultfœuille); show clean pre-trends in spreads/depth; cluster at the assignment level (stock/venue), not the observation.
Branch B: Intraday / high-frequency event studies
  • Tight, pre-registered windows around a discrete event (an order-type launch, a latency upgrade, a halt). Justify the window from market mechanics, not from where the effect is biggest.
  • Control for market-wide microstructure shocks (index moves, scheduled macro releases) and for the diurnal (U-shaped) intraday pattern in spreads/volume.
Branch C: Instruments for liquidity / order flow
  • Honest about weak instruments: report first-stage F, use Anderson-Rubin / weak-IV-robust sets. Defend exclusion in market-mechanism terms (the instrument moves trading frictions only through the channel claimed).
Branch D: Structural microstructure models
  • Tie each parameter to an identifying data feature: adverse-selection component from the permanent price impact / spread decomposition; PIN from the trade-imbalance distribution; Kyle's lambda from the price-impact regression. State estimator (MLE/GMM), and show the parameter is not just a fit artifact.

Referee pushback mapped to the identification fix

  • "Liquidity and your regressor are jointly determined." → Replace the panel regression with an exogenous market-structure shock (DiD/RDD), or a defensible IV; show the result is not a mechanical co-movement with volume/volatility.
  • "Your staggered TWFE is biased with heterogeneous effects." → Re-estimate with Callaway-Sant'Anna or Sun-Abraham; plot flat pre-event leads in spreads/depth.
  • "The event window is cherry-picked." → Justify the window from market mechanics (settlement, implementation date), show robustness to nearby windows, and rule out contemporaneous macro releases.
  • "What identifies PIN/lambda?" → Point to the data moment (trade-imbalance distribution; permanent price impact) and report estimation diagnostics, not just the point estimate.
  • "Your control group is contaminated." → Show the control stocks/venues were not indirectly affected (e.g., order flow migrating from treated to control); report a clean, unaffected comparison or a spillover-robust design.

Separating mechanical from behavioral effects

A recurring identification subtlety in market-structure work is that a rule change has both a mechanical effect (a wider tick arithmetically widens the minimum quotable spread) and a behavioral effect (liquidity suppliers and informed traders re-optimize). A credible JFM design isolates the behavioral channel, because the mechanical one is not a finding. Show the effect on stocks where the tick does not bind, decompose the spread change into the binding-tick component and the residual, or condition on pre-period spread relative to the new tick. Conflating the two is a frequent reviewer catch.

Worked vignette: the tick-size pilot (illustrative)

The SEC Tick-Size Pilot widened the quoting/trading increment for a randomized set of small-cap stocks. This is close to an ideal JFM design: random treatment assignment, a discrete date, and a treated/control split. The clean identification statement is one sentence — the effect of a wider tick on depth is identified by the random assignment of stocks to the pilot's test groups. The credible version shows flat pre-pilot trends in depth, estimates with assignment-level clustering, and separates the mechanical (tick-binding) effect from the behavioral (liquidity-supply) response. A weak version regresses depth on a post-dummy with controls and calls it causal.

Show full SKILL.md (522 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. JFM is market microstructure and asset pricing — liquidity, price discovery, and cross-sectional return tests where the factor-zoo multiple-testing haircut is salient.

  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

  • Branch chosen; the data-to-effect (or data-to-parameter) mapping stated in one sentence
  • Endogeneity of the liquidity/flow variable explicitly addressed, not assumed away
  • Market-structure design: clean pre-trends, modern staggered estimator, assignment-level clustering
  • Intraday events: window justified from mechanics; diurnal pattern and market-wide shocks controlled
  • IV: first-stage strength reported; exclusion defended in microstructure terms
  • Structural: each parameter tied to an identifying moment; estimator and inference stated
  • The causal claim never exceeds what the design supports

A catalog of clean market-structure shocks

Knowing the field's natural experiments speeds design. Commonly exploited exogenous changes, each with its own caveats: decimalization (2001) — tick size from sixteenths to pennies; the SEC Tick-Size Pilot (2016-18) — randomized, the cleanest assignment; Reg NMS (2007) — order protection and access fees; MiFID (2007) / MiFID II (2018) — European venue competition and transparency; short-sale bans (2008, and country-specific) — abrupt constraint changes; maker-taker / fee pilots — rebate structure; circuit breakers / LULD — discrete trading halts; index reconstitutions — forced, scheduled order flow; venue launches/closures and dark-pool entry. For each, the identification hinges on (a) whether assignment is plausibly exogenous to the stock's liquidity trajectory and (b) whether a clean control group exists. Argue both explicitly; a shock is not self-justifying.

Anti-patterns

  • "Liquidity → outcome" from a panel regression with controls, called identification
  • TWFE on a staggered tick-size / decimalization rollout with no heterogeneity-bias discussion
  • An event window chosen to maximize significance rather than from market mechanics
  • Ignoring the intraday U-shape so that time-of-day masquerades as the treatment effect
  • Reporting a PIN/lambda estimate without saying what moment in the data moves it

Inference choices that travel with the design

Identification is not finished until inference matches the data structure. Microstructure panels are correlated in two dimensions — the same stock is autocorrelated over time and all stocks co-move on a given day — so single-clustered or plain OLS standard errors overstate precision. Default to two-way clustering by stock and by day; use Newey-West when the time-series autocorrelation is the dominant concern; use a wild-cluster bootstrap when the number of treated venues or events is small (few-cluster bias). For event studies, account for cross-sectional correlation in abnormal liquidity across the event window. State the choice and its rationale where the design is described, not as an afterthought — a referee reads the clustering as part of the identification claim.

Output format

text
【Journal】Journal of Financial Markets (JFM)
【Skill】jfm-identification
【Branch】market-structure NE / intraday event / IV / structural
【Data-to-effect mapping】one sentence
【Identifying variation】<shock / window / instrument / moment>
【Endogeneity handled】how liquidity/flow endogeneity is broken
【Inference】clustering level + (if needed) weak-IV-robust set
【What it does NOT identify】<…>
【Source status】verified URL / 待核实 / not asserted
【Next skill】jfm-empirical-design

© 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-Markets-Skills/skills/jfm-identification of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

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Jfm Identification compared with similar skills
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Data Finderbrycewang-stanford/Auto-Empirical-Research-Skills4.6k—~1.7kAutomated safety check: PassCustom licence
Weakness Scannerflonat/flonat-research146—~1.5kAutomated safety check: PassMIT
Ecta Identificationfranklee16/academic-research-skills2231 repos~1.9kAutomated safety check: PassNone

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Categories

Questions about Jfm Identification

What does Jfm Identification do?

A skill your agent uses when the identification argument is the bottleneck for a Journal of Financial Markets (JFM) manuscript — causal effects on market quality, or what pins down a microstructure…. Jfm Identification is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when the identification argument is the bottleneck for a Journal of Financial Markets (JFM) manuscript — causal effects on market quality, or what pins down a microstructure model.

When should I use Jfm Identification?

Jfm Identification fits situations like: the identification argument is the bottleneck for a Journal of Financial Markets (JFM) manuscript — causal effects on market quality; what pins down a microstructure model.

How do I install Jfm Identification in Claude Code?

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

How do I install Jfm Identification in Codex?

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

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

What does Jfm Identification need to run?

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

Does Jfm 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 Jfm 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 Jfm Identification use?

Jfm 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 Jfm Identification use?

About 2.6k tokens (SKILL.md is roughly 10k 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 Jfm Identification?

Skills that share tags, products or a category with Jfm Identification: What If Oracle (K-Dense-AI/scientific-agent-skills, 48k stars), Paper Review (EvoScientist/EvoSkills, 478 stars), Data Finder (brycewang-stanford/Auto-Empirical-Research-Skills, 4.6k 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 Jfm Identification?

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.