Agent skill · Code Review & Quality

cogpsych-review-process

Use when you need to understand how Cognitive Psychology (Elsevier) evaluates a manuscript — editorial triage for theoretical impact and fit, expert review weighing model rigor, recovery, design, and reproducibility, and the long revision cycles typical of a model-driven journal. Use when stress-testing a paper before submission or interpreting a decision letter. Sets expectations and shapes the paper to survive review; it does not contact editors.

brycew6m878★ · +32/wk · 1 repos on radarProfile →
claude-codeMIT
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cogpsych-review-process --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 6 KB
Bundled scripts: none
Path: Cognitive-Psychology-Skills/skills/cogpsych-review-process/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 909 · +31 this week
Language: Stata
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Review Process (cogpsych-review-process) Cognitive Psychology combines selectivity for **theoretical impact** with deep **methodological and modeling scrutiny**. Reviewers and editors weigh not only whether the finding is interesting, but whether the **model is well-specified, identifiable, and properly compared**, whether the **experiments discriminate the accounts**, and whether the work is **reproducible**. Knowing this lets you pre-empt the common rejection reasons. Confirm the current process on the official page (检索于 2026-06;以官网为准). ## When to trigger - Before submitting, to stress-test the manuscript - Interpreting a decision letter and setting expectations - Deciding how to fit a long, model-driven program to a demanding review ## How review works (typical Elsevier journal pattern) 1. **Editorial triage.** A handling editor assesses theoretical impact, scope, and fit; thin, single-effect, or atheoretical submissions may be **rejected without external review** at this long-form, model-driven venue. 2. **Expert peer review.** Typically multiple referees with cognitive-modeling and experimental expertise. Expect detailed scrutiny of model specification, identifiability/recov

What's inside
Steps it walks through
  1. When to trigger
  2. How review works (typical Elsevier journal pattern)
  3. Shape the paper to pass
  4. Desk-reject and decline-without-review patterns
  5. Worked micro-example (illustrative triage)
  6. How reviewers weigh the evidence (calibration anchors)
  7. Anti-patterns
  8. Output format
  9. Supplementary resources
More from Awesome-Journal-Skills
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About this skill
What does the cogpsych-review-process skill do?

Use when you need to understand how Cognitive Psychology (Elsevier) evaluates a manuscript — editorial triage for theoretical impact and fit, expert review weighing model rigor, recovery, design, and reproducibility, and the long revision cycles typical of a model-driven journal. Use when stress-testing a paper before submission or interpreting a decision letter. Sets expectations and shapes the paper to survive review; it does not contact editors.

How do I install it?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cogpsych-review-process --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going