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Agent skill

Sample Size Basic

by aipoch in aipoch/medical-research-skills

Basic sample size estimator for clinical research planning. An agent skill from aipoch/medical-research-skills.

MITAuto-check passedResearch & Science

Install Sample Size Basic

skills CLI
$ npx skills add aipoch/medical-research-skills --skill sample-size-basic -a claude-code

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

GitHub CLI
$ gh skill install aipoch/medical-research-skills sample-size-basic --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/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Protocol Design/sample-size-basic' .claude/skills/sample-size-basic && 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
sample-size-basic
GitHub stars
2k
Token cost
~1.6k tokens
SKILL.md length
702 words
Files
5 (incl. scripts, references)
Skills in repo
567
Repo updated
First seen
Licence
MIT

At a glance

Basic sample size estimator for clinical research planning. An agent skill from aipoch/medical-research-skills.

  • Works in 7 steps: Identify test type: Determine… → Collect parameters: Gather alpha… → Validate inputs: Verify alpha in (0,1),… → …
  • Tasks that involve Experimental design
  • SKILL.md covers Quick Check, Audit-Ready Commands, When to Use and Workflow, plus 14 more sections
  • Runs Python scripts from its folder; calls python

What it does

Sample Size Basic is an agent skill from aipoch/medical-research-skills. Basic sample size estimator for clinical research planning. Computes per-group and total N for two-sample/paired t-tests, chi-square tests, and proportion comparisons, reporting alpha, power, effect size, and statistical assumptions summary for grant proposals and preliminary ...

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `POLISH_CHANGELOG.md`, `eval_report_sample-size-basic_result.json` and `references/audit-reference.md`).

It sits in Research & Science, covering Experimental design, Grant writing and Clinical and healthcare research. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • Tasks that involve Experimental design
  • Tasks that involve Grant writing
  • Tasks that involve Clinical and healthcare research

Example prompts

  • “/sample-size-basic”

Requirements

  • Python 3

Workflow steps

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

  1. Identify test type: Determine statistical test from user request — t_test (two-sample or paired), chi_square, or proportion comparison.
  2. Collect parameters: Gather alpha (default 0.05), power (default 0.80), effect_size (Cohen's d for t-test, or difference in proportions)…
  3. Validate inputs: Verify alpha in (0,1), power in (0,1), effect_size > 0, and baseline_rate in [0,1] when applicable. If invalid, report…
  4. Checkpoint: Display input summary with assumed test type and parameters to user for confirmation before computing.
  5. Compute sample size: Calculate per-group N and total N using the appropriate formula for the test type.
  6. Output: Return required sample size per group, total sample size, and statistical assumptions summary (test type, alpha, power, effect…
  7. Fallback: If test type is ambiguous, present options (t-test for means, chi-square for proportions) and ask user to clarify.

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. 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

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    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

Sample Size Basic loads about 1.6k tokens when it runs, and up to ~1.7k if it reads all its reference files. Until then it costs about 75 tokens; SKILL.md has 702 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~75
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.7k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 702 words, ~1,552 tokens.

Download SKILL.mdSave it as .claude/skills/sample-size-basic/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
sample-size-basic
description
Basic sample size estimator for clinical research planning. Computes per-group and total N for two-sample/paired t-tests, chi-square tests, and proportion comparisons, reporting alpha, power, effect size, and statistical assumptions summary for grant proposals and preliminary ...
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

Sample Size (Basic)

Basic sample size estimation for clinical research planning.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

bash
python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

bash
python -m py_compile scripts/main.py
python scripts/main.py --help

When to Use

  • Use this skill when estimating sample size for grant proposals or preliminary study design.
  • Use this skill when the user says "sample size", "power analysis", "how many subjects", or "n per group".
  • Use this skill for basic t-test, chi-square, and proportion test sample size calculations.

Workflow

  1. Identify test type: Determine statistical test from user request — t_test (two-sample or paired), chi_square, or proportion comparison.
  2. Collect parameters: Gather alpha (default 0.05), power (default 0.80), effect_size (Cohen's d for t-test, or difference in proportions), and baseline_rate (for proportion tests).
  3. Validate inputs: Verify alpha in (0,1), power in (0,1), effect_size > 0, and baseline_rate in [0,1] when applicable. If invalid, report exact error and stop.
  4. Checkpoint: Display input summary with assumed test type and parameters to user for confirmation before computing.
  5. Compute sample size: Calculate per-group N and total N using the appropriate formula for the test type.
  6. Output: Return required sample size per group, total sample size, and statistical assumptions summary (test type, alpha, power, effect size, assumptions made).
  7. Fallback: If test type is ambiguous, present options (t-test for means, chi-square for proportions) and ask user to clarify.

Use Cases

  • Quick sample size estimates for grant proposals
  • Preliminary study design calculations
  • Educational purposes for statistics training

Parameters

  • test_type: Type of test (t_test, chi_square, proportion)
  • alpha: Significance level (default 0.05)
  • power: Statistical power (default 0.80)
  • effect_size: Expected effect size
  • baseline_rate: Baseline proportion (for proportion tests)

Returns

  • Required sample size per group
  • Total sample size
  • Statistical assumptions summary

Example

Input: Two-sample t-test, alpha=0.05, power=0.80, effect_size=0.5 Output: n=64 per group, total=128 subjects

References

Risk Assessment

Risk IndicatorAssessmentLevel
Code ExecutionPython/R scripts executed locallyMedium
Network AccessNo external API callsLow
File System AccessRead input files, write output filesMedium
Instruction TamperingStandard prompt guidelinesLow
Data ExposureOutput files saved to workspaceLow

Security Checklist

  • No hardcoded credentials or API keys
  • No unauthorized file system access (../)
  • Output does not expose sensitive information
  • Prompt injection protections in place
  • Input file paths validated (no ../ traversal)
  • Output directory restricted to workspace
  • Script execution in sandboxed environment
  • Error messages sanitized (no stack traces exposed)
  • Dependencies audited

Prerequisites

text
# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Show full SKILL.md (283 more words)Show less
Success Metrics
  • Successfully executes main functionality
  • Output meets quality standards
  • Handles edge cases gracefully
  • Performance is acceptable
Test Cases
  1. Basic Functionality: Standard input → Expected output
  2. Edge Case: Invalid input → Graceful error handling
  3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

  • Current Stage: Draft
  • Next Review Date: 2026-03-06
  • Known Issues: None
  • Planned Improvements:
    • Performance optimization
    • Additional feature support

Output Requirements

Every final response should make these items explicit when they are relevant:

  • Objective or requested deliverable
  • Inputs used and assumptions introduced
  • Workflow or decision path
  • Core result, recommendation, or artifact
  • Constraints, risks, caveats, or validation needs
  • Unresolved items and next-step checks

Error Handling

  • If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
  • If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
  • If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
  • Do not fabricate files, citations, data, search results, or execution outcomes.

Input Validation

This skill accepts requests that match the documented purpose of sample-size-basic and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

sample-size-basic only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

Response Template

Use the following fixed structure for non-trivial requests:

  1. Objective
  2. Inputs Received
  3. Assumptions
  4. Workflow
  5. Deliverable
  6. Risks and Limits
  7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.

© aipoch, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts, references) in scientific-skills/Protocol Design/sample-size-basic of aipoch/medical-research-skills.

  • SKILL.md
  • POLISH_CHANGELOG.md
  • eval_report_sample-size-basic_result.json
  • references/audit-reference.md
  • scripts/main.py

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Sample Size Basic 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.

Sample Size Basic compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Sample Size Basic this skillaipoch/medical-research-skills2k—~1.6kAutomated safety check: PassMIT
Research Ops Skillsalirezarezvani/claude-skills28k—~2.8kAutomated safety check: PassMIT
Clinical Protocol Draftingaws-samples/amazon-bedrock-agents-healthcare-lifesciences274—~1.4kAutomated safety check: PassMIT-0
Clinical Researchalirezarezvani/claude-skills28k—~2.7kAutomated safety check: PassMIT
Clinical Researchborghei/Claude-Skills874—~3.6kAutomated safety check: PassMIT
Bio Clinical Biostatistics Adaptive DesignsGPTomics/bioSkills1.2k2 repos~7.7kAutomated safety check: PassMIT

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Categories

Questions about Sample Size Basic

What does Sample Size Basic do?

Basic sample size estimator for clinical research planning. An agent skill from aipoch/medical-research-skills. Sample Size Basic is an agent skill from aipoch/medical-research-skills. Basic sample size estimator for clinical research planning.

When should I use Sample Size Basic?

Sample Size Basic fits situations like: tasks that involve Experimental design; tasks that involve Grant writing; tasks that involve Clinical and healthcare research.

How do I install Sample Size Basic in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill sample-size-basic -a claude-code`. Or copy the skill folder (scientific-skills/Protocol Design/sample-size-basic in aipoch/medical-research-skills) into .claude/skills/sample-size-basic in your project. Claude Code loads it when a task matches its description.

How do I install Sample Size Basic in Codex?

Run `npx skills add aipoch/medical-research-skills --skill sample-size-basic -a codex`. Or copy the skill folder (scientific-skills/Protocol Design/sample-size-basic in aipoch/medical-research-skills) into .agents/skills/sample-size-basic in your project. Codex loads it when a task matches its description.

Can I use Sample Size Basic 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 aipoch/medical-research-skills --skill sample-size-basic -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sample-size-basic, .gemini/skills/sample-size-basic, .github/skills/sample-size-basic and .opencode/skills/sample-size-basic in your project.

What does Sample Size Basic need to run?

Going by SKILL.md and its folder, Sample Size Basic needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Sample Size Basic 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 Sample Size Basic 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Sample Size Basic use?

Sample Size Basic is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Sample Size Basic use?

About 1.6k tokens (SKILL.md is roughly 6.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 123 tokens, read only when the agent opens those files.

What are the alternatives to Sample Size Basic?

Skills that share tags, products or a category with Sample Size Basic: Research Ops Skills (alirezarezvani/claude-skills, 28k stars), Clinical Protocol Drafting (aws-samples/amazon-bedrock-agents-healthcare-lifesciences, 274 stars), Clinical Research (alirezarezvani/claude-skills, 28k stars) and Clinical Research (borghei/Claude-Skills, 874 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Sample Size Basic?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,973 GitHub stars. The repository holds 567 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.