Agent skill

Ectj Identification Strategy

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

A skill your agent uses when stress-testing identification, assumptions, asymptotics, regularity conditions, and proofs in a The Econometrics Journal (EctJ) submission, including proof placement…

MITAuto-check passedResearch & Science

Install Ectj Identification Strategy

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

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills ectj-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/The-Econometrics-Journal-Skills/skills/ectj-identification-strategy .claude/skills/ectj-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
ectj-identification-strategy
GitHub stars
1.2k
Token cost
~1.5k tokens
SKILL.md length
685 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when stress-testing identification, assumptions, asymptotics, regularity conditions, and proofs in a The Econometrics Journal (EctJ) submission, including proof placement…

  • Stress-testing identification
  • SKILL.md covers Audit, Referee attack surface, Assumption ledger and Worked trace: a debiased panel…, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Regularity conditions

What it does

Ectj Identification Strategy is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when stress-testing identification, assumptions, asymptotics, regularity conditions, and proofs in a The Econometrics Journal (EctJ) submission, including proof placement under RES printed-appendix rules and pairing every asymptotic claim with finite-sample evidence referees can audit.

Its SKILL.md is about 1.5k 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 Econometrics and empirical research and 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

  • Stress-testing identification
  • Regularity conditions
  • Proofs in a The Econometrics Journal (EctJ) submission
  • Including proof placement under RES printed-appendix rules and pairing every asymptotic claim with finite-sample evidence referees can audit

Example prompts

  • “/ectj-identification-strategy”

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

Ectj Identification Strategy loads about 1.5k tokens when it runs. Until then it costs about 80 tokens; SKILL.md has 685 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
~1.5k

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). 685 words, ~1,512 tokens.

Download SKILL.mdSave it as .claude/skills/ectj-identification-strategy/SKILL.md (or your agent's skills folder).
name
ectj-identification-strategy
description
Use when stress-testing identification, assumptions, asymptotics, regularity conditions, and proofs in a The Econometrics Journal (EctJ) submission, including proof placement under RES printed-appendix rules and pairing every asymptotic claim with finite-sample evidence referees can audit.

EctJ Identification Strategy

Use this for theory and methods integrity. EctJ readers will tolerate compactness, but not hidden assumptions or vague asymptotic claims.

Audit

  • State the population object, identifying restrictions, estimator or test statistic, and target parameter before derivations.
  • Label each regularity condition by role: existence, identification, consistency, asymptotic normality, bootstrap validity, finite-sample approximation, or computation.
  • Show why the leading case is not a toy example; connect assumptions to the empirical application.
  • Keep proofs in the main text or printed appendix when current RES guidance requires that; do not park mathematical proofs only in the online appendix.
  • Separate theorem statements from implementation advice and simulation claims.
  • Flag any assumption that is convenient but empirically fragile.

Referee attack surface

EctJ referees usually attack the bridge between compact theory and practical use. Pre-answer these points:

  • Object drift: the target parameter in the theorem is not the object estimated in the application.
  • Assumption opacity: a regularity condition is stated but never tied to a data feature or estimator step.
  • Leading-case weakness: the theorem solves a toy case whose constraints make the empirical example irrelevant.
  • Proof placement risk: critical derivations are hidden in unreviewed online material or an untraceable appendix.
  • Simulation mismatch: the Monte Carlo design does not probe the assumption most likely to fail.

For each attack, write the exact theorem, assumption, table, or paragraph that will answer it.

Assumption ledger

Create a compact ledger before rewriting the theory section:

text
Condition | Role | Where used | Empirical/simulation check | If weakened

Use the ledger to remove decorative assumptions and expose missing ones. If a condition is used only for proof convenience, say whether it can be relaxed, whether it is standard in the closest EctJ-adjacent literature, and whether the simulation explores failure near that boundary. If a condition is essential but empirically unverifiable, the paper needs an interpretation paragraph that tells applied readers what kind of data-generating process would make it plausible.

Do not let notation hide the identification argument. A reader should be able to trace, in order, the target object, restrictions, estimator or statistic, asymptotic claim, and finite-sample diagnostic.

Show full SKILL.md (348 more words)Show less

Worked trace: a debiased panel treatment-effect estimator

A hypothetical EctJ vignette (illustrative throughout): the paper proposes an orthogonalized estimator for an average treatment effect in a panel where nuisance functions are fit by machine learning. The traceable chain referees expect:

  • Target object: the ATE under unconfoundedness conditional on high-dimensional firm controls.
  • Restrictions: overlap bounded away from zero; nuisance estimators converging faster than n^{-1/4}; cross-fitting with K=5 folds.
  • Estimator: the Neyman-orthogonal score averaged over folds.
  • Asymptotic claim: root-n normality with a variance estimator valid under cross-fitting.
  • Finite-sample diagnostic: coverage simulated at n in {250, 1000}; the rate condition is stressed by deliberately slowing one nuisance learner and showing where coverage degrades.

If any link is missing, that link is what the report will quote back. A rate condition of the n^{-1/4} kind is exactly the assumption that must be tied to a data feature: say which learner plausibly meets it in the application and what the simulation shows when it fails.

Proof-economy rules for the compact format

  • Every theorem keeps its full proof in the printed paper or printed appendix; the online appendix carries only secondary lemmas, and only when current RES guidance permits it — confirm against the journal's current author guidelines before moving any derivation out of print.
  • A leading-case theorem may delegate generality to a remark, but the remark must say what breaks in the general case, not just that extensions are straightforward.
  • Each asymptotic statement should name the exhibit where its finite-sample counterpart appears; EctJ referees treat unpaired asymptotics as an unfinished result.

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the design, don't only describe it. Full map: execution-with-mcp. The Econometrics Journal is a methods venue — estimator validity + simulation; pair estimates with diagnostics.

  • detect_design → recommend → fit with as_handle=true → audit_result.
  • Observational causal claims: staggered DiD (callaway_santanna / sun_abraham + bacon_decomposition + honest_did_from_result); IV (effective_f_test + anderson_rubin_ci); RDD (rdrobust + mccrary_test).
  • Experiments: randomization-based inference + romano_wolf for many-outcome control.
  • Sensitivity: oster_delta / sensemakr for observational claims.

Report the magnitude in interpretable units; route the full battery to the appendix. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.

Output format

text
[Identification status] defensible / needs repair / not ready
[Target object] <parameter, estimator, test, or procedure>
[Critical assumptions] <condition -> role>
[Proof gaps] <missing lemma, rate, regularity, or edge case>
[Applied connection] <how the application validates the setup>

© 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 The-Econometrics-Journal-Skills/skills/ectj-identification-strategy of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Ectj Identification Strategy 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.

Ectj Identification Strategy compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Ectj Identification Strategy this skillbrycewang-stanford/Awesome-Journal-Skills1.2k—~1.5kAutomated safety check: PassMIT
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Jeg Identification Strategyfranklee16/academic-research-skills2231 repos~1kAutomated safety check: PassNone
Joe Identification Strategyfranklee16/academic-research-skills2231 repos~1.1kAutomated safety check: PassNone
Restud Identificationfranklee16/academic-research-skills2231 repos~1.6kAutomated safety check: PassNone
Rfs Identificationfranklee16/academic-research-skills2231 repos~1.4kAutomated safety check: PassNone

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Questions about Ectj Identification Strategy

What does Ectj Identification Strategy do?

A skill your agent uses when stress-testing identification, assumptions, asymptotics, regularity conditions, and proofs in a The Econometrics Journal (EctJ) submission, including proof placement…. Ectj Identification Strategy is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when stress-testing identification, assumptions, asymptotics, regularity conditions, and proofs in a The Econometrics Journal (EctJ) submission, including proof placement under RES printed-appendix rules and pairing every asymptotic claim with finite-sample evidence referees can audit.

When should I use Ectj Identification Strategy?

Ectj Identification Strategy fits situations like: stress-testing identification; regularity conditions; proofs in a The Econometrics Journal (EctJ) submission; including proof placement under RES printed-appendix rules and pairing every asymptotic claim with finite-sample evidence referees can audit.

How do I install Ectj Identification Strategy in Claude Code?

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

How do I install Ectj Identification Strategy in Codex?

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

Can I use Ectj 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 ectj-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/ectj-identification-strategy, .gemini/skills/ectj-identification-strategy, .github/skills/ectj-identification-strategy and .opencode/skills/ectj-identification-strategy in your project.

What does Ectj Identification Strategy need to run?

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

Does Ectj 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 Ectj 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 Ectj Identification Strategy use?

Ectj 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 Ectj Identification Strategy use?

About 1.5k tokens (SKILL.md is roughly 6k 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 Ectj Identification Strategy?

Skills that share tags, products or a category with Ectj Identification Strategy: Econometric Research Writing (franklee16/academic-research-skills, 223 stars), Jeg Identification Strategy (franklee16/academic-research-skills, 223 stars), Joe Identification Strategy (franklee16/academic-research-skills, 223 stars) and Restud Identification (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 Ectj Identification Strategy?

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.