A skill your agent uses when designing the experiments for a Cognitive Psychology (Elsevier) manuscript so they tightly control confounds, discriminate competing models, and have adequate power…

MITAuto-check passedResearch & Science

Install Cogpsych Study Design

skills CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cogpsych-study-design -a claude-code

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cogpsych-study-design --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/Cognitive-Psychology-Skills/skills/cogpsych-study-design .claude/skills/cogpsych-study-design && 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
cogpsych-study-design
GitHub stars
1.2k
Token cost
~1.8k tokens
SKILL.md length
629 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when designing the experiments for a Cognitive Psychology (Elsevier) manuscript so they tightly control confounds, discriminate competing models, and have adequate power…

  • Works in 5 steps: Design for discrimination. Build the… → Control researcher and stimulus degrees… → Power the critical contrast. Justify N… → …
  • Designing the experiments for a Cognitive Psychology (Elsevier) manuscript so they tightly control confounds
  • SKILL.md covers When to trigger, Design standards, Powering the critical contrast… and Pre-data lockdown checklist, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Cogpsych Study Design is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing the experiments for a Cognitive Psychology (Elsevier) manuscript so they tightly control confounds, discriminate competing models, and have adequate power across a multi-experiment program. Hardens stimulus construction, counterbalancing, design logic, and sample-size justification; it does not write analysis or modeling code.

Its SKILL.md is about 1.8k 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 Research & Science, covering Experimental design. 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

  • Designing the experiments for a Cognitive Psychology (Elsevier) manuscript so they tightly control confounds
  • Discriminate competing models
  • Have adequate power across a multi-experiment program

Example prompts

  • “/cogpsych-study-design”

Workflow steps

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

  1. Design for discrimination. Build the design so the data produce the **signature that separates
  2. Control researcher and stimulus degrees of freedom. Counterbalance condition/item assignment;
  3. Power the critical contrast. Justify N (and trials per cell) for the discriminating effect —
  4. Multi-experiment logic. Say what each experiment adds: rules out a confound, extends scope,
  5. Validity. Argue construct validity (does the task measure the process the model is about) and the

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

Cogpsych Study Design loads about 1.8k tokens when it runs. Until then it costs about 92 tokens; SKILL.md has 629 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~92
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

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). 629 words, ~1,765 tokens.

Download SKILL.mdSave it as .claude/skills/cogpsych-study-design/SKILL.md (or your agent's skills folder).
name
cogpsych-study-design
description
Use when designing the experiments for a Cognitive Psychology (Elsevier) manuscript so they tightly control confounds, discriminate competing models, and have adequate power across a multi-experiment program. Hardens stimulus construction, counterbalancing, design logic, and sample-size justification; it does not write analysis or modeling code.

Experiment Design (cogpsych-study-design)

Cognitive Psychology expects tightly controlled cognitive experiments whose design is engineered to discriminate models, organized as a multi-experiment program in which each experiment adds inference. The craft is in stimulus construction, counterbalancing, confound control, and powering the critical contrast — not just the main effect. Co-design the experiments with the model (cogpsych-theory-and-hypotheses).

When to trigger

  • Designing an experiment or a multi-experiment series
  • Constructing stimuli, item sets, and counterbalancing schemes
  • A reviewer questioned confounds, power, design logic, or whether the design discriminates the models
  • Justifying sample size for the critical contrast (often an interaction)

Design standards

  1. Design for discrimination. Build the design so the data produce the signature that separates the models (e.g., a manipulation that the rival accounts predict to diverge). A design that both models predict equally well wastes the experiment.
  2. Control researcher and stimulus degrees of freedom. Counterbalance condition/item assignment; control low-level confounds (frequency, length, familiarity, response mapping); randomize order; use attention/manipulation checks. Document the full stimulus pool, not a curated subset.
  3. Power the critical contrast. Justify N (and trials per cell) for the discriminating effect — often an interaction or a model parameter — not the easy main effect. State the assumed effect size and its source. Trials-per-participant is part of power for within-subjects designs.
  4. Multi-experiment logic. Say what each experiment adds: rules out a confound, extends scope, replicates the critical pattern, or tests a further model prediction. Avoid near-duplicate runs.
  5. Validity. Argue construct validity (does the task measure the process the model is about) and the generality of the claim across the stimulus space and population.

Powering the critical contrast — worked example (illustrative)

For the recognition-memory program, power the z-ROC shape contrast, not just overall accuracy.

Critical contrast: the diagnostic difference in z-ROC curvature between
            UVSD and DPSD predictions.
Within-subjects: trials per participant drive ROC precision — target enough
            old/new trials per confidence bin to estimate the slope reliably
            (state the per-bin minimum, not just N).
Sample size: justified by simulation under each model (generate data from
            UVSD and DPSD at plausible parameters; find N + trials at which
            the model-recovery rate exceeds the target).
Across experiments: Exp 1 establishes the pattern; Exp 2 rules out a list-
            composition confound; Exp 3 tests a further divergent prediction.
Stopping rule: fixed N + fixed trials; no optional stopping.

Justify sample size by model/parameter recovery simulation where the contrast is a model parameter, not only by a textbook power formula for a mean difference — this is the venue-appropriate move.

Pre-data lockdown checklist

Degree of freedomLock before data?Where it lives
Hypotheses + discriminating predictionyespreregistration / analysis plan
Models to be fit + comparison criteriayesanalysis plan
Full stimulus pool + counterbalancingyesmaterials deposit
Trials per cell / per confidence binyesdesign + power justification
Exclusion rules (RT, accuracy, dropout)yespreregistration
Stopping ruleyesanalysis plan
Exploratory analyses / model explorationallowed, labeledreported separately
Show full SKILL.md (249 more words)Show less

Design-stage reviewer pushback and the venue fix

  • "Both models predict this design equally" → redesign so a manipulation makes the model predictions diverge; the signature must be diagnostic.
  • "Possible stimulus confound (frequency/length)" → control or counterbalance it; report the matched pools; this objection lands hard here.
  • "Underpowered for the interaction / too few trials" → power the critical contrast via simulation; report trials per cell, not only N.
  • "Three near-identical experiments" → make each add inference (confound control, scope, further prediction).

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the design, don't only describe it. Full map: execution-with-mcp. Cognitive Psychology is experimental — within-subject designs and mixed models dominate; report the model, the effect size, and multiple-comparison control.

  • detect_design → recommend → fit with as_handle=true → audit_result.
  • Observational causal claims: staggered DiD (callaway_santanna / sun_abraham + bacon_decomposition + honest_did_from_result); IV (effective_f_test + anderson_rubin_ci); RDD (rdrobust + mccrary_test).
  • Experiments: randomization-based inference, romano_wolf for many-outcome family-wise control, and mediate for mediation (not naive controlling-away).
  • Sensitivity: oster_delta / sensemakr for observational claims.

Report the effect size in interpretable units; route the full battery to the appendix/supplement. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.

Anti-patterns

  • A design that both rival models predict equally well (non-diagnostic)
  • Uncontrolled low-level stimulus confounds or a curated stimulus subset
  • Powering the easy main effect while the critical interaction/parameter is underpowered
  • Too few trials per condition to estimate the model quantities reliably
  • A multi-experiment paper of near-duplicate runs with no added inference

Output format

【Discrimination】does the design produce the model-separating signature? [Y/N]
【Confound control】counterbalancing + low-level controls + checks? [Y/N]
【Power】N + trials/cell justified for the critical contrast (simulation)? [Y/N]
【Degrees of freedom】stimuli, models, exclusions, stopping fixed in advance? [Y/N]
【Multi-experiment logic】what each experiment adds
【Next】cogpsych-data-analysis

Supplementary resources

© 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 Cognitive-Psychology-Skills/skills/cogpsych-study-design of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

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Questions about Cogpsych Study Design

What does Cogpsych Study Design do?

A skill your agent uses when designing the experiments for a Cognitive Psychology (Elsevier) manuscript so they tightly control confounds, discriminate competing models, and have adequate power…. Cogpsych Study Design is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing the experiments for a Cognitive Psychology (Elsevier) manuscript so they tightly control confounds, discriminate competing models, and have adequate power across a multi-experiment program.

When should I use Cogpsych Study Design?

Cogpsych Study Design fits situations like: designing the experiments for a Cognitive Psychology (Elsevier) manuscript so they tightly control confounds; discriminate competing models; have adequate power across a multi-experiment program.

How do I install Cogpsych Study Design in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cogpsych-study-design -a claude-code`. Or copy the skill folder (Cognitive-Psychology-Skills/skills/cogpsych-study-design in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/cogpsych-study-design in your project. Claude Code loads it when a task matches its description.

How do I install Cogpsych Study Design in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cogpsych-study-design -a codex`. Or copy the skill folder (Cognitive-Psychology-Skills/skills/cogpsych-study-design in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/cogpsych-study-design in your project. Codex loads it when a task matches its description.

Can I use Cogpsych Study Design 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 cogpsych-study-design -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cogpsych-study-design, .gemini/skills/cogpsych-study-design, .github/skills/cogpsych-study-design and .opencode/skills/cogpsych-study-design in your project.

What does Cogpsych Study Design need to run?

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

Does Cogpsych Study Design 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 Cogpsych Study Design 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 Cogpsych Study Design use?

Cogpsych Study Design 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 Cogpsych Study Design use?

About 1.8k tokens (SKILL.md is roughly 7.1k 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 Cogpsych Study Design?

Skills that share tags, products or a category with Cogpsych Study Design: Scientific Critical Thinking (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars), Benchmark Paper Template (HKUSTDial/Supervisor-Skills, 8.8k stars), Claim-Driven Experiment Planner (zjYao36/Auto-Research-Refine, 128 stars) and Research Refine Pipeline (zjYao36/Auto-Research-Refine, 128 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cogpsych Study Design?

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.