Indication Dossier logo

Indication Dossier

CommunityPopular
xuzhougeng
indication-dossier

Build a sourced research dossier for one therapeutic indication — patient population, epidemiology, disease biology, standard of care, regulatory path, and landmark trials. Use when the user asks for an indication overview, disease landscape, or trial-design background.

Overview

Publisherxuzhougeng
Repositorywisp-science
Skill nameindication-dossier
Stars
1K
Forks
104
Bundled files
3
LicenseApache-2.0
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.

  • 3 bundled files

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

  • Open source

    Published by xuzhougeng on GitHub. Read the source before you install it.

Installation

Install the Indication Dossier 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/xuzhougeng/wisp-science/tree/main/skills/indication-dossier
  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/xuzhougeng/wisp-science.git /tmp/wisp-science
mkdir -p .claude/skills
cp -r /tmp/wisp-science/skills/indication-dossier .claude/skills/indication-dossier
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Indication Dossier 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 Indication Dossier 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 Indication Dossier 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.

Indication dossier

Five research phases, each writing one waypoint JSON under <workdir>/waypoints/, ending in a cited Markdown report. Waypoints make the run resumable: a later invocation reads which files exist and continues from the first missing one. The only pause for user input is after Phase 1.

The framing rule

Treat the indication as a patient population, not a disease entry. Every section answers a population question — who are these patients, how are they identified and managed, which trials would help them — rather than a textbook question about the condition. Nesting is population nesting: everyone in the child indication is in the parent.

Some inputs are not billable diagnoses at all: a biological state ("immunosenescence"), a non-accepted indication ("ageing"), an iatrogenic population ("GLP-1 induced sarcopenia"). Detect and label this early — it changes the epidemiology evidence base, the regulatory path, and what a "complete" dossier even looks like.

Inputs

InputRequiredMeaning
indicationyese.g. "sarcopenia", "idiopathic pulmonary fibrosis"
additional_contextnofocus areas, parent indication, framing
workdirnowaypoint/report location; default ./do_not_commit/indication-dossier-<slug>/

Tooling

Preferred: clinical-trials MCP for CT.gov, pubmed MCP for literature, WebSearch/WebFetch for FDA guidance, specialty-society guidelines (NCCN, AASLD, …), and CDC/WHO data; WebFetch for remote PDFs, Read for local ones; Agent subagents for parallel evidence gathering. When a listed MCP is not connected, say so and fall back to WebSearch against the public site itself.

Run protocol

Read references/standards.md first — it defines what counts as a citable finding, the anti-fabrication rules, and the report style. Phase-by-phase instructions live in references/phases.md; waypoint formats in references/waypoints.md.

  1. Identity. Resolve definition, ICD codes, aliases, parent, diagnostic status; quick CT.gov landscape count. Write meta.json. Then show the resolved identity and end the turn asking Proceed / Revise identity / Stop — the expensive phases wait for the answer (Wisp has no separate interactive-question tool, so this is a normal turn end).
  2. Epidemiology. Case definition, prevalence/incidence, demographics, natural history → epidemiology.json.
  3. Biology & standard of care. Mechanism, biomarkers, approved therapies, guidelines, unmet need → biology_soc.json.
  4. Regulatory & trials. Accepted endpoints, precedents, design parameters, landmark trials, failures → regulatory_trials.json.
  5. Synthesis. No new research threads (single targeted gap-fills only). Write indication_dossier_report.md and research_output.json, then mark progress.json complete.

After each of phases 2–5, write the waypoint, emit a ≤200-word summary of findings and open uncertainties, and continue directly.

Resuming

When workdir already contains waypoints: list which phases are complete (file exists and is non-empty), show the meta summary, and ask which phase to run. Never overwrite an existing waypoint without confirmation.

Output layout

<workdir>/waypoints/
├── progress.json                 # loop control, flipped last
├── meta.json                     # phase 1
├── epidemiology.json             # phase 2
├── biology_soc.json              # phase 3
├── regulatory_trials.json        # phase 4
├── sources_evaluated.json        # appended by every phase
├── research_output.json          # phase 5, structured
└── indication_dossier_report.md  # phase 5, the deliverable

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 Indication Dossier AI skill do?

Build a sourced research dossier for one therapeutic indication — patient population, epidemiology, disease biology, standard of care, regulatory path, and landmark trials. Use when the user asks for an indication overview, disease landscape, or trial-design background.

Why use Indication Dossier on TypingMind?

Because you install it once and use it with any model. Indication Dossier 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 Indication Dossier in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/xuzhougeng/wisp-science/tree/main/skills/indication-dossier. 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 Indication Dossier?

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 Indication Dossier?

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

Is the Indication Dossier AI skill free?

Yes. It is published on GitHub by xuzhougeng under the Apache-2.0 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇