Skills DirectorySkills Directory
SkillsLearnSecurityCategoriesDocsCommunityBlog
Sign InSubmit Skill
Skills Directory

Security-tested agent skills for Claude, coding agents, and AI workflows.

Directory

  • Browse Skills
  • All Skills A–Z
  • Claude Skills
  • Claude Code Skills
  • Agent Skills
  • Categories
  • Authors
  • Submit a Skill

Learn

  • Learn Hub
  • Install Claude Skills
  • Write SKILL.md
  • Skills vs MCP
  • Directories Compared

Security

  • Security
  • Methodology
  • Secure Claude Skills
  • Security Badges

Company

  • About
  • Community
  • Blog
  • API Docs
  • Advertise

2026 Skills Directory. All rights reserved.

ProTermsPrivacyRefunds
Back to skills

Jcr Methods

ASecurity

Use when choosing or stress-testing the research design for a Journal of Consumer Research (JCR) manuscript — multi-study behavioral experiments, interpretive Consumer Culture Theory (CCT) fieldwork, mixed designs, or a Registered Report — so the evidence matches the conceptual claim. Designs the studies; it does not analyze them (jcr-data-analysis).

1,052 stars
0 votes
0 copies
0 views
Added 6/5/2026
ai-agentsrustgotesting

Security Analysis

A100/100

Scanned 6/5/2026

Install to Claude Code

$npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill jcr-methods --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of Jcr Methods?

Add the live security badge to your README — it updates automatically with every re-scan.

Security grade badge for Jcr Methods
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/brycewang-stanford-jcr-methods/badge)](https://www.skillsdirectory.com/skills/brycewang-stanford-jcr-methods)

More formats (shields.io, HTML) on the badges page.

Download with Pro
Files
SKILL.md
---
name: jcr-methods
description: Use when choosing or stress-testing the research design for a Journal of Consumer Research (JCR) manuscript — multi-study behavioral experiments, interpretive Consumer Culture Theory (CCT) fieldwork, mixed designs, or a Registered Report — so the evidence matches the conceptual claim. Designs the studies; it does not analyze them (jcr-data-analysis).
---

# Methods & Design (jcr-methods)

## When to trigger

- You have a mechanism but are unsure how to test it
- Deciding between a behavioral-experiments paper and a CCT fieldwork paper
- A reviewer asks whether your design can actually support the process claim
- You are weighing a Registered Report for a confirmatory question

## JCR is methodologically pluralistic by mandate

JCR states **no single preferred method**; the bar is a clear conceptual contribution supported by *appropriate* empirical evidence. In practice two flagship traditions coexist under one masthead, and you should commit to one design logic (or a principled mix):

- **Theory-driven behavioral experimentation** (the dominant tradition): multiple lab and online experiments that isolate a **psychological process** and its boundary conditions.
- **Interpretive / Consumer Culture Theory (CCT)**: ethnography, depth interviews, phenomenology, or netnography that theorizes the sociocultural meanings of consumption.

The journal also publishes quantitative/modeling and methodological work. Choose the design the **conceptual claim** demands, not the one you find convenient.

## Designing the multi-study experimental package

- **Process evidence:** plan studies that establish the effect, then **mediation** (measured or, more convincingly, **moderation-of-process** / manipulated mediator), then **boundary conditions** that the theory predicts.
- **Internal validity:** random assignment; manipulation checks and attention checks; pretested stimuli; counterbalancing; rule out demand and confounds by design.
- **Robustness across studies:** vary populations, stimuli, and operationalizations so the effect is not stimulus-bound; a convergent multi-study package is the JCR norm.
- **Power & samples:** a priori power analysis; specify and justify sample sizes and exclusion rules in advance. Overflow stimuli, full instruments, and additional replication studies belong in the **web appendix** (max 40 MB, excluded from the 60-page cap).

## Designing interpretive / CCT work

- Justify **site, informant selection, and immersion**; show the data are rich enough to support conceptual claims.
- Plan for **trustworthiness**: triangulation, prolonged engagement, member checks, and an audit trail rather than p-values.
- Theorize as you go: the design should enable moving from thick description to second-order constructs.

## Transparency is a design decision, not an afterthought

JCR's transparency regime shapes the design from the start: a **Data Collection Statement** is required for **all** submissions (Step 6), data/materials posting is **required at invited revision** unless exempt, and replication code must be provided. Build clean materials, preregistration where appropriate, and a repository plan (OSF / Harvard Dataverse / Qualitative Data Repository / ResearchBox) into the design. For confirmatory questions, consider a **Registered Report** (full review before final data collection; must be JCR-worthy regardless of outcome).

## Checklist

- [ ] Design logic (experiments / CCT / mixed) matches the conceptual claim
- [ ] Experiments: effect → process → boundary mapped to specific studies
- [ ] Manipulation/attention checks, random assignment, pretested stimuli
- [ ] A priori power, sample sizes, and exclusion rules pre-specified
- [ ] CCT: site/informant justification and a trustworthiness plan
- [ ] Materials, code, and a repository plan prepared for transparency requirements

## Anti-patterns

- A single study asked to carry a process claim.
- "Mediation" inferred from a measured mediator without manipulating the process.
- Stimulus-bound effects (one scenario, one product) generalized broadly.
- CCT design with too little immersion to support conceptual claims.
- Treating data/materials posting as a post-acceptance chore.

## Output format

```
【Design logic】experiments / CCT / mixed / Registered Report
【Study chain】effect → process → boundary (or CCT framework)
【Validity safeguards】randomization / checks / pretests / trustworthiness
【Power & samples】a priori N, exclusions
【Transparency plan】repository + code + Data Collection Statement
【Web appendix】overflow stimuli / extra studies (≤40 MB)
【Next step】jcr-data-analysis
```

Attribution

brycewang-stanfordbrycewang-stanford
View sourceMore from brycewang-stanford →
SSkills DirectorySkills Directory

Know which skills are safe — weekly.

Best new skills + every skill we flagged as malicious. From the team that scanned 103,619.

Join free

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments (0)

No comments yet. Be the first to comment!

SSkills DirectorySkills Directory

Know which skills are safe — weekly.

Best new skills + every skill we flagged as malicious. From the team that scanned 103,619.

Join free

Related Skills

Caveman

Ultra-compressed communication mode that cuts output tokens while keeping technical accuracy. Levels: lite, full, ultra and the wenyan variants. Use for /caveman, "caveman mode", "talk like caveman", "be brief" or "less tokens".

1066601 votes

Hyperplan

Adversarial multi-agent planning skill. Self-orchestrates 5 hostile category members (unspecified-low, unspecified-high, deep, ultrabrain, artistry) via team-mode for ruthless cross-critique debate, distills only the defensible insights, then MANDATORILY hands the distilled insight bundle to the `plan` agent for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', '/hyperplan', ...

693161 votes

Mcp Code Execution

Routes multi-tool workflows through MCP servers for large datasets and pipelines. Use when Bash tool overhead is limiting throughput on data-heavy tasks.

3351 votes

catchup

Recovers the conversation and failed tool calls of a previous Codex, Claude Code, Antigravity, Cline, Copilot CLI, Cursor, DeepSeek Harness, Kimi, OpenCode, Pi Agent, or ZCode session. Use when the user says "catch up", "what did the last session do", "get me up to speed", "I switched agents", asks to recover/summarize a previous session before continuing, or asks to diagnose or report a catchup failure. Do NOT use for the current conversation, git history, or any non-agent log.

651 votes

math-skill

A comprehensive mathematical reasoning skill for AI assistants — handles arithmetic to research-level problems with rigorous step-by-step reasoning, systematic verification, and transparent uncertainty handling

381 votes
View all in ai-agents →