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.
npx skills add StanfordSpezi/SpeziVibe --skill digital-health-study-planninggit clone --depth 1 https://github.com/StanfordSpezi/SpeziVibeWrote 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.
[](https://agentmods.dev/skills/stanfordspezi/spezivibe/digital-health-study-planning)<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.
<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>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.
| Model | Per session | Once 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 |
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.
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:
- objective or hypothesis
- participant population
- study type and duration
- primary and secondary outcomes
- expected study procedures and burden
- 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 |
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.
- 12d ago First seen · 144 lines · 33 tokens per session scan A 7f75e68e69c0
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.
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