lang-review-process
Use when anticipating how a Language (LSA) manuscript will be judged — the double-anonymous review, the general-audience and cross-framework bar, the desk-return filters (descriptive data dump, single-framework parochialism, undocumented data), and the decision categories. Sets expectations and stress-tests before submission; it does not write the paper.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill lang-review-process --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Review Process (lang-review-process) Knowing how *Language* actually evaluates a manuscript lets you pre-empt the objections before you submit. *Language* runs **double-anonymous** review under co-editors and an editorial team, drawing referees from **across subfields**, and it screens hard at intake: a paper that is a **descriptive data dump**, that lives **inside one framework**, or that rests on **undocumented data** may be returned before external review. This skill maps the process and stress-tests the paper against it. ## When to trigger - Before submission, to predict reviewer objections and the likely outcome - After a decision letter, to read the outcome category correctly (then route to `lang-rebuttal`) - Deciding whether the piece fits a full article or a shorter/online section - Calibrating expectations for a first-round outcome ## What the process looks like (verify on the author pages) - **Intake screen.** Editors check fit, section, anonymization, and whether the paper makes a theoretically grounded claim for a general audience. Data dumps and framework-internal exercises can be **returned without review**. - **Double-anonymous external review.** Referees from the
- When to trigger
- What the process looks like (verify on the author pages)
- What reviewers are asked to weigh (anticipate each)
- Desk-return filters (the intake traps)
- Calibration (Language review culture, hedged)
- Anti-patterns
- Output format
- Supplementary resources
What does the lang-review-process skill do?
Use when anticipating how a Language (LSA) manuscript will be judged — the double-anonymous review, the general-audience and cross-framework bar, the desk-return filters (descriptive data dump, single-framework parochialism, undocumented data), and the decision categories. Sets expectations and stress-tests before submission; it does not write the paper.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill lang-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.