New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Psychrev Argument Development

by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills

A skill your agent uses when deriving predictions from a Psychological Review theory and confronting them with existing data and rival models — the journal's substitute for an empirical results…

MITAuto-check passed

Install Psychrev Argument Development

skills CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill psychrev-argument-development -a claude-code

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills psychrev-argument-development --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/Psychological-Review-Skills/skills/psychrev-argument-development .claude/skills/psychrev-argument-development && 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
psychrev-argument-development
GitHub stars
1.2k
Token cost
~1.3k tokens
SKILL.md length
587 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when deriving predictions from a Psychological Review theory and confronting them with existing data and rival models — the journal's substitute for an empirical results…

  • Works in 3 steps: Derive, do not assert. For each… → Separate signature from accommodation. A… → Make at least one risky, novel…
  • SKILL.md covers When to trigger, What replaces a results…, The derivation discipline and The confrontation discipline, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Psychrev Argument Development is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deriving predictions from a Psychological Review theory and confronting them with existing data and rival models — the journal's substitute for an empirical results section. Develops the argument; it does NOT build the model (psychrev-theory-construction) or set its scope and identifiability limits (psychrev-boundary-conditions).

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

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.

Example prompts

  • “/psychrev-argument-development”

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. Derive, do not assert. For each phenomenon in the explanandum, show how it follows
  2. Separate signature from accommodation. A strong prediction is a signature — a
  3. Make at least one risky, novel prediction. Falsifiability is the journal's currency

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

Psychrev Argument Development loads about 1.3k tokens when it runs. Until then it costs about 93 tokens; SKILL.md has 587 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
~1.3k

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). 587 words, ~1,260 tokens.

Download SKILL.mdSave it as .claude/skills/psychrev-argument-development/SKILL.md (or your agent's skills folder).
name
psychrev-argument-development
description
Use when deriving predictions from a Psychological Review theory and confronting them with existing data and rival models — the journal's substitute for an empirical results section. Develops the argument; it does NOT build the model (psychrev-theory-construction) or set its scope and identifiability limits (psychrev-boundary-conditions).

Argument Development: Deriving & Confronting Predictions (psychrev-argument-development)

When to trigger

  • The model is built but you have not shown what it predicts
  • You assert the theory "explains" phenomena without deriving them
  • You have not compared your predictions to rival models on diagnostic cases
  • A reviewer will ask "could this theory have been wrong?"

What replaces a results section here

Psychological Review has no experiment of its own as the contribution. The work that an empirical paper does with data, a Review paper does with derivation and confrontation: you derive predictions from the model's assumptions, then confront them with already- existing evidence and with what rival models predict. Logical and quantitative soundness is the rigor standard, exactly as statistical inference is at empirical journals.

The derivation discipline

  1. Derive, do not assert. For each phenomenon in the explanandum, show how it follows from the assumptions — analytically, or by simulation that traces assumptions → behavior. "The model can explain X" is worthless without the derivation that it does.
  2. Separate signature from accommodation. A strong prediction is a signature — a pattern the theory entails and rivals do not, ideally a parameter-free qualitative ordering or a novel pattern not used to build the model. Accommodating known data with fitted parameters is weaker; label it honestly as accommodation, not prediction.
  3. Make at least one risky, novel prediction. Falsifiability is the journal's currency: name a pattern that, if observed, would disconfirm the theory, and ideally one not yet tested so future work can adjudicate.

The confrontation discipline

  • Confront existing data. Use published datasets (yours or others') to show the model reproduces the diagnostic phenomena. Report fit honestly: degrees of freedom, number of free parameters, and whether parameters were estimated or set a priori.
  • Confront rival models head-to-head. On each diagnostic phenomenon, show what your model and the rival each predict, and why the data favor yours. A nested or formal model comparison (e.g., information criteria, parameter recovery) beats a verbal contrast.
  • Address alternative explanations. For every prediction your model gets right, ask whether a simpler rival gets it right too; if so, the case is not diagnostic — find one that is.
  • Probe robustness. Show the key results do not depend on a fragile parameter setting or an arbitrary functional form (sensitivity over a plausible range).
Show full SKILL.md (208 more words)Show less

Quantitative honesty (for formal models)

  • State the number of free parameters and what each was fit to.
  • Distinguish fit (reproducing data used to build the model) from prediction (data the model was not tuned on).
  • Prefer generalization tests (fit on one set, predict another) over in-sample fit.
  • Beware flexibility: a model that can fit any pattern predicts nothing — show what it cannot do.

Checklist

  • Each explanandum phenomenon is derived, not merely asserted, from the assumptions
  • At least one risky, novel, falsifiable prediction is stated
  • Signatures (rival-distinguishing) are separated from accommodations (fitted)
  • Existing data are used to confront the model; free-parameter count is disclosed
  • Head-to-head comparison with rival models on diagnostic phenomena is shown
  • Alternative simpler explanations are ruled out on each diagnostic case
  • Robustness to parameter/functional-form choices is demonstrated

Anti-patterns

  • "The model can explain X" with no derivation that it does
  • Fitting known data and calling accommodation a prediction
  • A model so flexible it could fit any result (and therefore predicts nothing)
  • Verbal hand-waving where a rival has a formal, quantitative account
  • Hiding the number of free parameters or which data were used to fit them
  • Picking only phenomena where all theories agree (non-diagnostic)
  • Introducing a brand-new experiment as the deciding evidence (data only constrain here)

Output format

【Derivations】[phenomenon → how it follows from assumptions] for each
【Signatures vs. accommodations】[risky/novel predictions] | [fitted accommodations]
【Confrontation】existing data used; free-parameter count; fit vs. generalization
【Head-to-head】[diagnostic phenomenon → your prediction vs. rival's vs. data]
【Robustness】key results stable over parameter/form range: yes / fix
【Next step】psychrev-boundary-conditions (scope, identifiability, what it does NOT explain)

© 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 Psychological-Review-Skills/skills/psychrev-argument-development of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Psychrev Argument Development 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.

Psychrev Argument Development compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Psychrev Argument Development this skillbrycewang-stanford/Awesome-Journal-Skills1.2k—~1.3kAutomated safety check: PassMIT
Marketing Psychologynexu-io/open-design100k—~317Automated safety check: PassApache-2.0
Autopilot Predictruvnet/ruflo74k—~337Automated safety check: PassMIT
Longbridge Derivativessickn33/agentic-awesome-skills47k1 repos~1.1kAutomated safety check: PassMIT
Prediction Market Oracle Researchaffaan-m/ECC276k1 repos~577Automated safety check: PassMIT
Prediction Market Risk Reviewaffaan-m/ECC276k1 repos~471Automated safety check: PassMIT

Similar skills

  • Marketing Psychology

    nexu-io/open-design

    Apply psychological principles and behavioral science to copy and design.

    100k GitHub stars~317 tokensUpdated today
    Marketing & SEOAuto-check passed

  • Autopilot Predict

    ruvnet/ruflo

    Use learned patterns and current state to predict the optimal next action

    74k GitHub stars~337 tokensUpdated today
    Agent WorkflowsAuto-check passed

  • Longbridge Derivatives

    sickn33/agentic-awesome-skills

    Curated upstream guidance for Longbridge Derivatives; use when the workflow matches the user goal.

    47k GitHub starsUsed in 1 repo~1.1k tokens
    Business, Finance & HRAuto-check passed

  • Prediction Market Oracle Research

    affaan-m/ECC

    Research prediction markets as data sources or oracle signals for products, agents, dashboards, and corporate decision intelligence.

    276k GitHub starsUsed in 1 repo~577 tokens
    Knowledge ManagementAuto-check passed

  • Prediction Market Risk Review

    affaan-m/ECC

    Review prediction-market, basket, oracle, and trading-agent workflows for compliance, safety, data-quality, privacy, and execution risk.

    276k GitHub starsUsed in 1 repo~471 tokens
    Data & AnalyticsAuto-check passed

  • Formula Derivation

    brycewang-stanford/Auto-Empirical-Research-Skills

    Structure and derive research formulas when the user wants to 推导公式, derive a theory line, build equations from a problem statement, clarify assumptions, separate formal derivation from remarks, or…

    4.6k GitHub stars~1.7k tokensUpdated 4 days ago
    Auto-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 13 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 13 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 13 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 13 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 13 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 13 days ago
    Auto-check passed

Questions about Psychrev Argument Development

What does Psychrev Argument Development do?

A skill your agent uses when deriving predictions from a Psychological Review theory and confronting them with existing data and rival models — the journal's substitute for an empirical results…. Psychrev Argument Development is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deriving predictions from a Psychological Review theory and confronting them with existing data and rival models — the journal's substitute for an empirical results section.

How do I install Psychrev Argument Development in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill psychrev-argument-development -a claude-code`. Or copy the skill folder (Psychological-Review-Skills/skills/psychrev-argument-development in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/psychrev-argument-development in your project. Claude Code loads it when a task matches its description.

How do I install Psychrev Argument Development in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill psychrev-argument-development -a codex`. Or copy the skill folder (Psychological-Review-Skills/skills/psychrev-argument-development in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/psychrev-argument-development in your project. Codex loads it when a task matches its description.

Can I use Psychrev Argument Development 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 psychrev-argument-development -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/psychrev-argument-development, .gemini/skills/psychrev-argument-development, .github/skills/psychrev-argument-development and .opencode/skills/psychrev-argument-development in your project.

What does Psychrev Argument Development need to run?

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

Does Psychrev Argument Development 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 Psychrev Argument Development 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 Psychrev Argument Development use?

Psychrev Argument Development 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 Psychrev Argument Development use?

About 1.3k tokens (SKILL.md is roughly 5k 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 Psychrev Argument Development?

Skills that share tags, products or a category with Psychrev Argument Development: Marketing Psychology (nexu-io/open-design, 100k stars), Autopilot Predict (ruvnet/ruflo, 74k stars), Longbridge Derivatives (sickn33/agentic-awesome-skills, 47k stars) and Prediction Market Oracle Research (affaan-m/ECC, 276k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Psychrev Argument Development?

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.