digital-health-study-planning

digital-health-study-planning is a skill for Claude Code, Codex from StanfordSpezi/SpeziVibe. It costs 33 tokens per session (863 once invoked), scanned A, original, MIT.

A planning process for a digital health study or research protocol. It covers who joins, consent, what data is collected, when assessments happen, and how outcomes are measured.

In plain words
What is it for?
Use it to plan enrollment, eligibility, consent, withdrawal, data collection schedules, study milestones, outcomes, and operational follow-up.
Why use it?
It turns a research question into an organized study plan while considering participant burden and practical staffing needs. This helps align the study’s procedures with its goals.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to plan enrollment, eligibility, consent, withdrawal, data collection schedules, study milestones, outcomes, and operational follow-up.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/stanfordspezi/spezivibe/digital-health-study-planning
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add StanfordSpezi/SpeziVibe --skill digital-health-study-planning
Clone the repo
git clone --depth 1 https://github.com/StanfordSpezi/SpeziVibe

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for digital-health-study-planning

README.md
[![agentmods](https://agentmods.dev/badge/skills/stanfordspezi/spezivibe/digital-health-study-planning/github.svg)](https://agentmods.dev/skills/stanfordspezi/spezivibe/digital-health-study-planning)
Your own site
<a href="https://agentmods.dev/skills/stanfordspezi/spezivibe/digital-health-study-planning"><img src="https://agentmods.dev/badge/skills/stanfordspezi/spezivibe/digital-health-study-planning/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for digital-health-study-planning

Your own site · 80×15
<a href="https://agentmods.dev/skills/stanfordspezi/spezivibe/digital-health-study-planning"><img src="https://agentmods.dev/badge/skills/stanfordspezi/spezivibe/digital-health-study-planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 863 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00033 $0.00863
Opus 5 $0.00016 $0.00432
Sonnet 5 $0.00007 $0.00173
Haiku 4.5 $0.00003 $0.00086

Measured 12d ago against content hash 7f75e68e69c0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

digital-health-study-planning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/digital-health-study-planning/SKILL.md · 144 lines

How it starts

The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Study Planner

Plan digital health studies and research protocols without assuming a particular app stack.

When to Use

Use this skill when you need to:

  • shape a research question into a study plan
  • define enrollment, consent, and participation requirements
  • design data collection and assessment schedules
  • align outcomes, operations, and participant burden

Working Style

Start by understanding the study, not the interface. Ask questions before proposing structure.

Clarify:

  1. objective or hypothesis
  2. participant population
  3. study type and duration
  4. primary and secondary outcomes
  5. expected study procedures and burden
  6. operational constraints such as staffing, review, and follow-up

Planning Framework

1. Study Overview

Define:

  • study name
  • objective or hypothesis
  • population
  • study type such as observational, interventional, feasibility, or survey-based
  • duration and major milestones

2. Enrollment and Consent

Work through:

  • inclusion criteria
  • exclusion criteria
  • recruitment channels
  • screening steps
  • consent requirements
  • participant withdrawal process

This skill covers the participant-facing side of consent — who consents, when, and how it fits the study flow. digital-health-compliance-planning covers the regulatory side (IRB expectations, consent capture and versioning, de-identification); coordinate rather than duplicate if both skills run.

Do not assume device ownership, app literacy, or language access without checking.

3. Data Collection Plan

Create a table like this:

Data Type Source Frequency Purpose Notes
Baseline demographics Intake questionnaire Once Eligibility and cohort description Keep minimal
Symptoms Participant self-report Daily or weekly Outcome tracking Define burden clearly
Clinical measurements Device, sensor, chart, or manual entry As needed Primary or secondary outcomes Clarify validation path
Engagement data Product telemetry Ongoing Feasibility and adherence Avoid collecting unnecessary detail

Read the full file on GitHub · 144 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 144 lines · 33 tokens per session scan A 7f75e68e69c0

Subscribe to this mod's changes

digital-health-study-planning is a skill published in the GitHub repository StanfordSpezi/SpeziVibe (24 stars, last pushed 4d ago), licensed MIT. It adds 33 tokens to every session and 863 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.