A skill your agent uses when choosing and designing the evidence for a Journal of Public Policy & Marketing (JPP&M) manuscript — experiments with policy-realistic stimuli, quasi-experimental policy…

MITAuto-check passedData & Analytics

Install Jppm Methods

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

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jppm-methods --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-Public-Policy-and-Marketing-Skills/skills/jppm-methods .claude/skills/jppm-methods && 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
jppm-methods
GitHub stars
1.2k
Token cost
~1.6k tokens
SKILL.md length
683 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when choosing and designing the evidence for a Journal of Public Policy & Marketing (JPP&M) manuscript — experiments with policy-realistic stimuli, quasi-experimental policy…

  • Quasi-experimental policy evaluation (DiD
  • SKILL.md covers When to trigger, Methodological pluralism,…, Prospective designs:… and Retrospective designs:…, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Synthetic control)

What it does

Jppm Methods is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when choosing and designing the evidence for a Journal of Public Policy & Marketing (JPP&M) manuscript — experiments with policy-realistic stimuli, quasi-experimental policy evaluation (DiD, RDD, synthetic control), surveys, field data, or meta-analysis. Designs the studies; it does not estimate them (jppm-data-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 Data & Analytics, covering Data analysis. 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

  • Quasi-experimental policy evaluation (DiD
  • Synthetic control)

Example prompts

  • “/jppm-methods”

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

Jppm Methods loads about 1.6k tokens when it runs. Until then it costs about 85 tokens; SKILL.md has 683 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~85
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). 683 words, ~1,575 tokens.

Download SKILL.mdSave it as .claude/skills/jppm-methods/SKILL.md (or your agent's skills folder).
name
jppm-methods
description
Use when choosing and designing the evidence for a Journal of Public Policy & Marketing (JPP&M) manuscript — experiments with policy-realistic stimuli, quasi-experimental policy evaluation (DiD, RDD, synthetic control), surveys, field data, or meta-analysis. Designs the studies; it does not estimate them (jppm-data-analysis).

Methods & Evaluation Design (jppm-methods)

When to trigger

  • Deciding whether the question needs an experiment, a policy evaluation, a survey, or a synthesis
  • A policy rolled out and you must find a credible counterfactual before claiming impact
  • Your label/disclosure experiment uses stimuli no agency could ever mandate
  • The sample excludes the very population the policy is meant to protect
  • You are combining lab and field evidence and need the pieces to carry distinct weight

Methodological pluralism, disciplined by the policy question

JPP&M accepts a wider methods palette than most marketing journals — randomized experiments, quasi-experimental evaluation, surveys, field and archival data, meta-analysis, qualitative work — but the choice must follow from the policy inference required. Two questions dominate: Would the proposed instrument work? (prospective — usually experiments with realistic stimuli) and Did the enacted instrument work? (retrospective — evaluation designs with explicit counterfactuals). A submission that answers the retrospective question with before/after trends, or the prospective one with fantasy stimuli, fails regardless of statistical polish. Missing counterfactual reasoning in an evaluation design is one of this journal's known desk-reject patterns.

Prospective designs: policy-realistic experiments

  • Mandatable stimuli. Test warning/label/disclosure formats an agency could actually require — real estate on a package, formats from the live rulemaking (e.g., FDA front-of-package proposals), not a full-screen banner no rule would compel.
  • Treatment contrasts = decision options. Conditions should map to the regulator's choice set (status quo vs. proposed rule vs. stricter alternative), so each pairwise contrast answers a decision.
  • Consequential outcomes. Choice with real stakes, purchases, incentivized behavior — self-reported intentions alone are weak currency for a claim that a rule will change behavior.
  • Policy-relevant samples. Recruit the protected population (smokers for tobacco warnings, low-income households for financial disclosures, parents for children's marketing). A student panel cannot carry a vulnerability claim; when using Prolific/CloudResearch, screen and quota accordingly.
  • Marketplace noise. Add realistic competing information (cluttered shelf, competing claims); effects that survive noise are the ones that survive markets.

Retrospective designs: evaluation with a counterfactual

DesignUse whenJPP&M-specific cautions
Difference-in-differencespolicy adopted in some states/markets/categories, not othersstaggered adoption needs heterogeneity-robust estimators; argue parallel trends behaviorally, not just visually
Regression discontinuityeligibility threshold or size cutoff assigns exposurecheck manipulation at the cutoff (firms sort!); effects are local to the threshold
Synthetic controlone large unit treated (a city soda tax, a national ban)pre-period fit and placebo runs are the argument
Event studytiming of enforcement/announcement is sharpanticipation by firms and media coverage blur the event date
Interrupted time seriesno untreated comparison exists at allweakest option; state so and bound the claims

Firms' strategic responses are both a threat and a finding: reformulation, pre-emptive compliance, or channel-shifting can contaminate the comparison group — design to detect it (untreated outcomes, supply-side data) rather than assume it away.

Show full SKILL.md (226 more words)Show less

Surveys, qualitative work, and synthesis

  • Surveys earn their place for constructs no archive holds (perceived deception, privacy concern, financial anxiety) — use validated scales and probability or well-quota'd samples when claims are population-level.
  • Qualitative designs are welcome for vulnerable populations whose experience frames the policy problem; document access, consent, and IRB care to a higher standard, and avoid designs that further burden participants.
  • Meta-analysis suits mature streams (warning-label effects, disclosure formats); code moderators the regulator controls (format, placement, dose).

Checklist

  • The design answers the paper's policy question (prospective vs. retrospective) directly
  • Experimental stimuli are mandatable and conditions map to the regulator's choice set
  • The sample includes the population the policy targets; vulnerable groups are powered, not token
  • Evaluations name the counterfactual and the assignment mechanism explicitly
  • Firm strategic response is measured or ruled out, not assumed absent
  • Ethics/IRB treatment matches the sensitivity of the population studied
  • Pre-registration or a pre-analysis plan is in place for confirmatory studies

Anti-patterns

  • Before/after theater: a pre/post trend presented as policy impact with no comparison group
  • Fantasy stimuli: disclosure formats no agency could mandate, generalized to regulation
  • Convenience-sample vulnerability claims: conclusions about protected groups from panels that exclude them
  • Intentions-only evidence for behavior-change claims
  • One-method dogma: forcing an experiment onto a question that demands field variation, or vice versa
  • Sorted cutoffs: an RDD where firms demonstrably manipulate the threshold, unexamined

Output format

text
【Policy question type】prospective (would it work) / retrospective (did it work)
【Design】experiment / DiD / RDD / synthetic control / survey / meta-analysis / qualitative
【Counterfactual】comparison group + assignment logic (retrospective) or control condition logic (prospective)
【Stimuli & sample】mandatable formats; policy-target population included
【Firm response plan】how strategic reactions are detected or bounded
【Next skill】jppm-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-Public-Policy-and-Marketing-Skills/skills/jppm-methods of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Jppm Methods 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.

Jppm Methods compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Jppm Methods this skillbrycewang-stanford/Awesome-Journal-Skills1.2k—~1.6kAutomated safety check: PassMIT
Sprint Velocity Analysismohitagw15856/pm-claude-skills1.4k—~3.4kAutomated safety check: PassMIT
Exploratory Data Analysisspacering-net/codeg3.9k14 repos~3.6kAutomated safety check: PassMIT
Excel and CSV Data Analysisbytedance/deer-flow84k4 repos~2.2kAutomated safety check: PassMIT
Exploratory Data AnalysisOleafly/Oleafly2092 repos~3.4kAutomated safety check: NotesMIT
Pandas ProJeffallan/claude-skills12k1 repos~1.5kAutomated safety check: PassMIT

Similar skills

  • Sprint Velocity Analysis

    mohitagw15856/pm-claude-skills

    Analyze sprint velocity data and produce an engineering team health report covering delivery trends, capacity utilization, and improvement recommendations.

    1.4k GitHub stars~3.4k tokensUpdated yesterday
    Data & AnalyticsAuto-check passed
  • Exploratory Data Analysis

    spacering-net/codeg

    Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats.

    3.9k GitHub starsUsed in 14 repos~3.6k tokens
    Data & AnalyticsAuto-check passed
  • Excel and CSV Data Analysis

    bytedance/deer-flow

    Analyzes uploaded Excel and CSV files with SQL through DuckDB, producing schema inspections, statistical summaries and exports to CSV, JSON or Markdown.

    84k GitHub starsUsed in 4 repos~2.2k tokens
    Data & AnalyticsAuto-check passed
  • Perform bounded, local exploratory analysis of explicitly supported scientific files.

    209 GitHub starsUsed in 2 repos~3.4k tokens
    Data & AnalyticsAuto-check: notes
  • Pandas Pro

    Jeffallan/claude-skills

    Handles pandas DataFrame work: cleaning, merging, groupby aggregation, pivots, time-series resampling and memory tuning, with checks on dtypes, shapes and nulls.

    12k GitHub starsUsed in 1 repo~1.5k tokens
    Data & AnalyticsAuto-check passed
  • Python Executor

    cortega26/chile-hub

    Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh).

    113 GitHub starsUsed in 2 repos~1.5k tokens
    Data & AnalyticsAuto-check passed

More from brycewang-stanford/Awesome-Journal-Skills

All 2,387 skills in this repo
  • Aaag Data Analysis

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when running and reporting the analysis for an Annals of the American Association of Geographers manuscript — spatial statistics and modeling, remote-sensing accuracy, or…

    1.2k GitHub stars~1.3k tokensUpdated 12 days ago
    Auto-check passed
  • Aaag Literature Positioning

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when positioning an Annals of the American Association of Geographers manuscript in the literature — engaging geographic scholarship across the relevant area and the…

    1.2k GitHub stars~1.3k tokensUpdated 12 days ago
    Auto-check passed
  • Aaag Rebuttal

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when responding to an Annals of the American Association of Geographers decision letter (major/minor revision) — building a point-by-point response to the subject editor and…

    1.2k GitHub stars~1.4k tokensUpdated 12 days ago
    Auto-check passed
  • Aaag Research Design

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when defending the research design of an Annals of the American Association of Geographers manuscript — spatial/quantitative analysis and GIScience, remote-sensing and…

    1.2k GitHub stars~1.4k tokensUpdated 12 days ago
    Auto-check passed
  • Aaag Review Process

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when you need to understand how the Annals of the American Association of Geographers evaluates a manuscript — double-anonymous review routed through a subject editor by…

    1.2k GitHub stars~1.3k tokensUpdated 12 days ago
    Auto-check passed
  • Aaag Submission

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when running the final pre-submission preflight for the Annals of the American Association of Geographers via ScholarOne Manuscripts — area/article-type selection…

    1.2k GitHub stars~1.6k tokensUpdated 12 days ago
    Auto-check passed

Questions about Jppm Methods

What does Jppm Methods do?

A skill your agent uses when choosing and designing the evidence for a Journal of Public Policy & Marketing (JPP&M) manuscript — experiments with policy-realistic stimuli, quasi-experimental policy…. Jppm Methods is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when choosing and designing the evidence for a Journal of Public Policy & Marketing (JPP&M) manuscript — experiments with policy-realistic stimuli, quasi-experimental policy evaluation (DiD, RDD, synthetic control), surveys, field data, or meta-analysis.

When should I use Jppm Methods?

Jppm Methods fits situations like: quasi-experimental policy evaluation (DiD; synthetic control).

How do I install Jppm Methods in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jppm-methods -a claude-code`. Or copy the skill folder (Journal-of-Public-Policy-and-Marketing-Skills/skills/jppm-methods in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/jppm-methods in your project. Claude Code loads it when a task matches its description.

How do I install Jppm Methods in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jppm-methods -a codex`. Or copy the skill folder (Journal-of-Public-Policy-and-Marketing-Skills/skills/jppm-methods in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/jppm-methods in your project. Codex loads it when a task matches its description.

Can I use Jppm Methods 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 jppm-methods -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/jppm-methods, .gemini/skills/jppm-methods, .github/skills/jppm-methods and .opencode/skills/jppm-methods in your project.

What does Jppm Methods need to run?

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

Does Jppm Methods 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 Jppm Methods 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 Jppm Methods use?

Jppm Methods 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 Jppm Methods 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 Jppm Methods?

Skills that share tags, products or a category with Jppm Methods: Sprint Velocity Analysis (mohitagw15856/pm-claude-skills, 1.4k stars), Exploratory Data Analysis (spacering-net/codeg, 3.9k stars), Excel and CSV Data Analysis (bytedance/deer-flow, 84k stars) and Exploratory Data Analysis (Oleafly/Oleafly, 209 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Jppm Methods?

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.