Protocol Drafting logo

Protocol Drafting

Organization
Amazon-Quick
protocol-drafting

Draft clinical trial protocol sections grounded in ICH guidelines and FDA regulations, starting from a grant document or study synopsis. Generates Objectives, Background & Rationale, Study Design, and assembles them into a cohesive protocol document. Use when asked to 'draft a clinical trial protocol', 'write protocol objectives from this grant', 'generate a study design section', 'create a protocol from my synopsis', 'draft IND protocol sections', or any request to produce ICH/FDA-compliant protocol text from research source documents.

Overview

PublisherAmazon-Quick
RepositoryAmazon-Quick-official-catalog
Skill nameprotocol-drafting
Stars
50
Forks
4
Bundled files
1
LicenseMIT-0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Amazon-Quick on GitHub. Read the source before you install it.

Installation

Install the Protocol Drafting AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Amazon-Quick/Amazon-Quick-official-catalog/tree/main/skills/healthcare/protocol-drafting
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Amazon-Quick/Amazon-Quick-official-catalog.git /tmp/Amazon-Quick-official-catalog
mkdir -p .claude/skills
cp -r /tmp/Amazon-Quick-official-catalog/skills/healthcare/protocol-drafting .claude/skills/protocol-drafting
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Protocol Drafting in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Protocol Drafting on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Protocol Drafting is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Overview

Drafts clinical trial protocol sections from a user-provided grant document or study synopsis. Produces four protocol sections in sequence (Objectives, Background & Rationale, Study Design, Assembly), each grounded in ICH guidelines and FDA regulations retrieved via MCP tools. Works interactively: presents each section for user review and approval before proceeding. Flags data gaps and inconsistencies rather than fabricating content. The final deliverable is an assembled protocol document ready for PI and IRB review.

Prerequisites

This skill requires two MCP servers connected to Amazon Quick as remotely hosted MCP servers before use:

  1. fda-ecfr - Retrieves 21 CFR regulatory text from the public FDA eCFR API. Source: https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences/tree/main/mcp-servers/agentcore-gateway/fda-ecfr
  2. awslabs.bedrock-kb-retrieval-mcp-server - Queries ICH guideline content (E6, E8, E9) via an Amazon Bedrock Knowledge Base. Source: https://github.com/awslabs/mcp/tree/main/src/bedrock-kb-retrieval-mcp-server

Without these MCP servers, the skill cannot retrieve authoritative regulatory text and will not function.

<Definition - ICH E6(R2)> Good Clinical Practice: Integrated Addendum. The international ethical and scientific quality standard for designing, conducting, recording, and reporting trials involving human subjects. Section 6 specifies protocol content requirements. </Definition - ICH E6(R2)>

<Definition - ICH E8(R1)> General Considerations for Clinical Studies. Provides a framework for quality-by-design in clinical development, including study design rationale and protocol planning. </Definition - ICH E8(R1)>

<Definition - ICH E9> Statistical Principles for Clinical Trials. Covers randomization, blinding, sample size justification, control group selection, and analysis populations. </Definition - ICH E9>

<Definition - 21 CFR Part 312> FDA regulation governing Investigational New Drug Applications (IND). Section 312.23(a)(6) specifies required protocol content for IND submissions, including objectives, design, endpoints, and statistical methods. </Definition - 21 CFR Part 312>

<Definition - Protocol Section Set> The four deliverable sections this skill produces in sequence:

  1. Objectives (primary, secondary, exploratory, with endpoint mapping)
  2. Background & Rationale (disease overview, unmet need, agent summary, preclinical/clinical evidence, study rationale)
  3. Study Design (overall design, treatment arms, schema, randomization, sample size, duration)
  4. Assembly (title page, TOC, cross-references, consistency harmonization) </Definition - Protocol Section Set>

<Definition - Source Document> The user-provided grant document or study synopsis from which protocol content is extracted. May be PDF, DOCX, or plain text. Contains specific aims, endpoints, preliminary data, population, design parameters, and dosing information. </Definition - Source Document>

Workflow

<Workflow - Intake and Objectives description="Read the source document, extract study parameters, and draft the Objectives section." tools=[file_read, file_read_pdf, file_read_docx, awslabs.bedrock-kb-retrieval-mcp-server] triggers=["when the user provides a grant or synopsis", "draft protocol objectives", "start protocol drafting"]>

  1. [Ask user] Request the source document (grant or study synopsis). Accept a file path or uploaded document.

  2. [Agent] Read the source document using the appropriate tool (file_read_pdf for PDF, file_read_docx for DOCX, file_read for plain text). Extract:

    • Specific aims
    • Primary endpoint
    • Secondary endpoints
    • Exploratory endpoints (if any)
    • Study phase
    • Therapeutic area Print a summary of extracted parameters and confirm with the user.
  3. [Ask user] Confirm extracted parameters are correct. If the user identifies errors or gaps, re-extract or ask for clarification.

  4. [Agent] Query the ICH Guidelines MCP (awslabs.bedrock-kb-retrieval-mcp-server) for ICH E6(R2) Section 6 guidance on how protocol objectives should be stated, including the distinction between primary and secondary objectives.

  5. [Agent] Draft the Objectives section with the following structure:

    • Primary Objective (linked to the primary endpoint)
    • Secondary Objectives (linked to each secondary endpoint)
    • Exploratory Objectives (if any are implied by the grant aims) Each objective: clear, measurable, single-sentence statement following ICH conventions. Map each objective to its corresponding endpoint.
  6. [Ask user] Present the Objectives section for review. Do not proceed until the user approves or requests changes. Iterate if needed.

</Workflow - Intake and Objectives>

<Workflow - Background and Rationale description="Draft the Background and Rationale section from the source document and regulatory guidance." tools=[file_read, file_read_pdf, file_read_docx, awslabs.bedrock-kb-retrieval-mcp-server, fda-ecfr] triggers=["when Objectives are approved", "draft background section", "write protocol rationale"]>

  1. [Agent] Extract from the source document:

    • Disease context and epidemiology
    • Preliminary data (preclinical and clinical results)
    • Unmet medical need
    • Scientific rationale for the investigational agent
    • Mechanism of action and selectivity profile
  2. [Agent] Query FDA eCFR MCP (fda-ecfr) for 21 CFR Part 312.23(a)(6) requirements on nonclinical and clinical background information for an IND protocol.

  3. [Agent] Query ICH Guidelines MCP (awslabs.bedrock-kb-retrieval-mcp-server) for:

    • ICH E6(R2) Section 6 on protocol background section requirements
    • ICH E8(R1) guidance on contextualizing the study within existing evidence
  4. [Agent] Draft the Background & Rationale section with the following structure:

    • Disease Overview (epidemiology, molecular subtype prevalence, current standard of care)
    • Unmet Medical Need (limitations of existing therapies)
    • Investigational Agent Summary (mechanism of action, selectivity profile)
    • Relevant Nonclinical Findings (IC50 data, selectivity over wild-type, as available)
    • Clinical Experience to Date (prior phase results, RP2D, preliminary efficacy/safety)
    • Study Rationale (why this agent, this population, this design)

    Tone: scientific, appropriate for IRB submission. Cite source document preliminary data where applicable. Flag any subsections where the source document lacks sufficient data.

  5. [Ask user] Present the Background & Rationale section for review. Do not proceed until the user approves or requests changes. Iterate if needed.

</Workflow - Background and Rationale>

<Workflow - Study Design description="Draft the Study Design section including arms, randomization, sample size, and schema." tools=[file_read, file_read_pdf, file_read_docx, awslabs.bedrock-kb-retrieval-mcp-server, fda-ecfr] triggers=["when Background and Rationale is approved", "draft study design section", "write the design"]>

  1. [Agent] Extract from the source document:

    • Study design type (randomized, open-label, blinded, etc.)
    • Randomization scheme and ratio
    • Treatment arms and dosing regimens
    • Sample size and power justification
    • Study duration (enrollment, treatment, follow-up)
    • Number of sites
    • Target population
  2. [Agent] Query ICH Guidelines MCP (awslabs.bedrock-kb-retrieval-mcp-server) for:

    • ICH E8(R1) on general study design considerations
    • ICH E9 on statistical design principles (randomization, blinding rationale, control group selection)
  3. [Agent] Query FDA eCFR MCP (fda-ecfr) for 21 CFR Part 312.23(a)(6) requirements on protocol design elements for IND studies.

  4. [Agent] Draft the Study Design section with the following structure:

    • Overall Design (phase, randomization ratio, blinding/open-label justification, multicenter)
    • Treatment Arms (experimental arm with dose/schedule, control arm with regimen)
    • Study Schema (text-based visual flow: Screening → Randomization → Treatment → Follow-up)
    • Randomization and Stratification (method, stratification factors if applicable)
    • Sample Size Justification (statistical basis, power, expected effect size)
    • Study Duration (enrollment period, treatment duration, follow-up period)

    Flag any design parameters not specified in the source document. For sample size, if the source lacks statistical assumptions, note the gap and provide a placeholder framework the user's biostatistician can complete.

  5. [Ask user] Present the Study Design section for review. Do not proceed until the user approves or requests changes. Iterate if needed.

</Workflow - Study Design>

<Workflow - Assembly description="Assemble all approved sections into a cohesive protocol document with title page, TOC, and cross-reference harmonization." tools=[file_write, open_in_session_tab, run_python] triggers=["when Study Design is approved", "assemble the protocol", "combine all sections"]>

  1. [Agent] Collect the three approved sections (Objectives, Background & Rationale, Study Design) from the conversation history.

  2. [Agent] Create the protocol document structure:

    • Protocol Title Page (study title, PI name, funding source, protocol version, date)
    • Table of Contents
    • Section 1: Objectives (approved text)
    • Section 2: Background & Rationale (approved text)
    • Section 3: Study Design (approved text)
  3. [Agent] Harmonize across sections:

    • Verify consistent terminology (same terms for the same concepts throughout)
    • Confirm cross-references align (endpoints in Objectives match those in Study Design)
    • Ensure consistent formatting, tense, and numbering
    • Do NOT alter scientific content. Only harmonize language and structure.
  4. [Agent] Flag any inconsistencies found as inline reviewer comments (e.g., "[REVIEWER NOTE: Endpoint X appears in Objectives but is not addressed in Study Design. Please reconcile.]").

  5. [Agent] Add the standing disclaimer: "This protocol was drafted with AI assistance and must be reviewed by qualified medical and regulatory professionals before submission to IRB or regulatory agencies."

  6. [Agent] Write the assembled protocol to a Markdown file using file_write. Open it for the user with open_in_session_tab.

  7. [Ask user] Present the assembled document for final review. Note any reviewer comments that require attention.

</Workflow - Assembly>

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Protocol Drafting AI skill do?

Draft clinical trial protocol sections grounded in ICH guidelines and FDA regulations, starting from a grant document or study synopsis. Generates Objectives, Background & Rationale, Study Design, and assembles them into a cohesive protocol document. Use when asked to 'draft a clinical trial protocol', 'write protocol objectives from this grant', 'generate a study design section', 'create a protocol from my synopsis', 'draft IND protocol sections', or any request to produce ICH/FDA-compliant protocol text from research source documents.

Why use Protocol Drafting on TypingMind?

Because you install it once and use it with any model. Protocol Drafting is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Protocol Drafting in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Amazon-Quick/Amazon-Quick-official-catalog/tree/main/skills/healthcare/protocol-drafting. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Protocol Drafting?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Protocol Drafting?

As many as you like. As long as a model supports skills, you can use Protocol Drafting with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Protocol Drafting AI skill free?

Yes. It is published on GitHub by Amazon-Quick under the MIT-0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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