A skill your agent uses when the design that isolates a behavioral mechanism is the bottleneck for a Journal of Economic Behavior & Organization (JEBO) manuscript — lab/field experiment…

MITAuto-check passedResearch & Science

Install Jebo Identification

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

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jebo-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-Economic-Behavior-and-Organization-Skills/skills/jebo-identification .claude/skills/jebo-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
jebo-identification
GitHub stars
1.2k
Token cost
~1.8k tokens
SKILL.md length
749 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when the design that isolates a behavioral mechanism is the bottleneck for a Journal of Economic Behavior & Organization (JEBO) manuscript — lab/field experiment…

  • Observational causal design
  • SKILL.md covers When to trigger, The JEBO identification bar, Execution bridge (StatsPAI /… and Checklist, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Tasks that involve Load testing

What it does

Jebo Identification is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when the design that isolates a behavioral mechanism is the bottleneck for a Journal of Economic Behavior & Organization (JEBO) manuscript — lab/field experiment, observational causal design, or simulation. Stress-tests experimental and observational identification to JEBO's behavioral-credibility bar; it does not write prose or build the deposit.

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

  • Observational causal design
  • Tasks that involve Load testing

Example prompts

  • “/jebo-identification”

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

Jebo Identification loads about 1.8k tokens when it runs. Until then it costs about 93 tokens; SKILL.md has 749 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~93
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). 749 words, ~1,840 tokens.

Download SKILL.mdSave it as .claude/skills/jebo-identification/SKILL.md (or your agent's skills folder).
name
jebo-identification
description
Use when the design that isolates a behavioral mechanism is the bottleneck for a Journal of Economic Behavior & Organization (JEBO) manuscript — lab/field experiment, observational causal design, or simulation. Stress-tests experimental and observational identification to JEBO's behavioral-credibility bar; it does not write prose or build the deposit.

Identification Strategy (jebo-identification)

When to trigger

  • A lab/field experiment's treatment, incentives, or comprehension are not pinned down
  • An effect could be an experimenter-demand effect rather than the claimed behavioral mechanism
  • An observational behavioral claim rests on OLS + controls, or TWFE on staggered timing
  • A study uses deception, or runs many treatments, and the inference/ethics implications are unaddressed
  • You are unsure the design isolates the behavioral mechanism, not a confound

The JEBO identification bar

JEBO judges identification through a behavioral-mechanism lens: the design must isolate the psychological or institutional channel the paper claims, not merely produce a significant difference. Because JEBO treats experimental design as a first-class identification branch alongside observational causal designs, "identification" means different things by branch — pick the branch and make the channel transparent. Inference must match the design (clustering at the level of randomization or assignment; few-cluster corrections).

Branch A: Lab / online experiment (the JEBO core)
  • Incentive compatibility: payoffs must make truthful/effortful behavior the dominant strategy for the elicited object (e.g., BDM, strategy method, incentivized beliefs). State the mechanism and the stakes.
  • No-deception norm: the experimental-economics convention is no deception; if you deviate, justify it and expect scrutiny — many referees treat deception as disqualifying for an incentivized study.
  • Comprehension & attention checks: report comprehension quizzes, control questions, and how failures were handled (drop / re-instruct), so the effect is not confusion.
  • Experimenter demand: rule out demand effects — neutral framing, between-subject where within-subject would cue the hypothesis, obfuscated objectives, or an explicit demand-treatment (e.g., Mummolo–Peterson / de Quidt-style bounds).
  • Randomization & balance: show balance on observables; report the randomization procedure and unit.
  • Multiple treatments: if several treatment arms, plan the comparisons and correct for multiplicity (see jebo-robustness).
  • Pre-registration: pre-register the design and primary analysis (AEA RCT Registry / AsPredicted / OSF) and report deviations. (JEBO does not currently mandate it — 待核实 — but referees increasingly expect it.)
Branch B: Field experiment
  • ITT vs. LATE/TOT stated; randomization unit and stratification described; spillovers and SUTVA addressed.
  • Attrition examined and bounded (Lee bounds if differential); compliance documented.
  • Ethical clearance / consent noted; external-validity scope stated (the field setting's generality).
Branch C: Observational behavioral empirics
  • DID / event study with staggered adoption: move beyond TWFE (Callaway–Sant'Anna, Sun–Abraham, de Chaisemartin–D'Haultfœuille); show clean event-study leads; Goodman-Bacon decomposition.
  • IV: strong first stage (effective F); weak-IV-robust sets (Anderson–Rubin) when needed; defend exclusion in institutions/theory + falsification.
  • RDD: density/manipulation test (McCrary / Cattaneo–Jansson–Ma); local-linear, data-driven bandwidth, bias-corrected robust CIs.
  • The behavioral interpretation must be argued, not assumed — the design identifies an estimate; the mechanism connecting it to a behavioral channel needs its own evidence (see jebo-theory-model).
Branch D: Agent-based / simulation
  • Document the data-generating process and behavioral rules; set and report seeds; show the result is not an artifact of grid/tuning choices (see jebo-robustness).
Show full SKILL.md (304 more words)Show less

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the design, don't only describe it. Full map: execution-with-mcp. JEBO spans behavioral/experimental and applied micro; randomization inference for experiments, DiD/IV for observational claims.

  • 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.

Checklist

  • Branch chosen; the design-to-mechanism mapping stated in one sentence
  • Experiment: incentive-compatible elicitation; no deception (or justified); comprehension checks reported
  • Experimenter-demand effects ruled out or bounded
  • Randomization unit, balance, and (for field) attrition/spillovers handled
  • Observational: design-appropriate diagnostics; modern estimator where TWFE/2SLS would bias
  • Inference clustered at the randomization/assignment level; few-cluster issue addressed
  • The behavioral claim never exceeds what the design isolates

Anti-patterns

  • Reporting a treatment difference as a behavioral mechanism without ruling out demand effects
  • Using deception in an incentivized study without justification (referees may reject outright)
  • Within-subject designs that cue the hypothesis, presented as if between-subject-clean
  • TWFE on staggered treatment with no heterogeneity-bias discussion
  • Calling a significant coefficient "evidence of [bias]" when a non-behavioral confound survives
  • Significance asterisks standing in for clustered SEs or a pre-registered primary outcome

Worked vignette (illustrative)

A lab study claims that public visibility raises cooperation via image concerns. A referee asks whether subjects merely inferred the experimenter wanted more cooperation in the visible arm. The JEBO fix: add a demand-effect treatment (explicitly tell one cell "we expect more cooperation"), show the visibility effect (illustrative: +0.6 contributions, s.e. 0.2) is an order of magnitude larger than the pure demand response, and pre-register cooperation as the primary outcome — turning "could be demand" into a bounded, mechanism-level claim.

Output format

text
【Branch】lab / field / observational / simulation
【Design-to-mechanism mapping】one sentence
【Behavioral channel isolated】<image concern / loss aversion / learning / norm / ...>
【Identification evidence】[incentives+comprehension+demand-bound / balance+attrition / pre-trends+density+first-stage / DGP+seeds]
【Estimator + inference】estimator; clustering level; weak-IV/honest-DID if any
【What it does NOT identify】[...]
【Next step】jebo-theory-model

© 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-Economic-Behavior-and-Organization-Skills/skills/jebo-identification of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

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Jebo 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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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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Questions about Jebo Identification

What does Jebo Identification do?

A skill your agent uses when the design that isolates a behavioral mechanism is the bottleneck for a Journal of Economic Behavior & Organization (JEBO) manuscript — lab/field experiment…. Jebo Identification is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when the design that isolates a behavioral mechanism is the bottleneck for a Journal of Economic Behavior & Organization (JEBO) manuscript — lab/field experiment, observational causal design, or simulation.

When should I use Jebo Identification?

Jebo Identification fits situations like: observational causal design; tasks that involve Load testing.

How do I install Jebo Identification in Claude Code?

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

How do I install Jebo Identification in Codex?

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

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

What does Jebo Identification need to run?

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

Does Jebo 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 Jebo 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 Jebo Identification use?

Jebo 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 Jebo Identification use?

About 1.8k tokens (SKILL.md is roughly 7.4k 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 Jebo Identification?

Skills that share tags, products or a category with Jebo 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 Jebo 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.