Indication Dossier logo

Indication Dossier

CommunityPopular
HughYau
indication-dossier

Generate a therapeutic indication dossier. Covers the patient population, epidemiology, disease biology, standard of care, regulatory precedent, and landmark clinical trials.

Overview

PublisherHughYau
RepositoryAcademicForge
Skill nameindication-dossier
Stars
2.6K
Forks
152
Bundled files
8
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.

  • 8 bundled files

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

  • Open source

    Published by HughYau 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.
  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/HughYau/AcademicForge.git /tmp/AcademicForge
mkdir -p .claude/skills
cp -r /tmp/AcademicForge/skills/claude-science/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

Produces a structured research dossier on a single indication, framed as a patient population: who they are, what's wrong, how they're treated today, and how clinical trials can be designed to help them. Runs as five phases that write resumable waypoint files; after a brief identity check at the end of Phase 1, the remaining phases run straight through.

Framing

Think of an indication as a patient population. Frame everything from the patient perspective: "Who are these patients?" not "What is this disease?"; "How are these patients identified and managed?" not "What causes this condition?"; population nesting: "all patients in {child} are patients in {parent}".

Some indications don't map to ICD codes or standard disease definitions: "immunosenescence" is a biological state, not a billable diagnosis; "ageing" is not an FDA-accepted indication; "GLP-1 induced sarcopenia" is an iatrogenic population. Note these distinctions explicitly. They matter for regulatory path and trial design.

Inputs

  • indication (required) — indication name (e.g., "sarcopenia", "idiopathic pulmonary fibrosis").
  • additional_context (optional) — areas to focus on, parent indication, or other framing.
  • workdir (optional) — where to write waypoints and the final report. Defaults to ./do_not_commit/indication-dossier-<slug>/.

Tools this skill expects

PurposeTool
ClinicalTrials.govclinical-trials MCP
Literaturepubmed MCP
WebWebSearch, WebFetch — FDA guidance, treatment guidelines (NCCN, AASLD, specialty societies), CDC/WHO epidemiology data
DocumentsWebFetch for remote PDFs; Read for local PDFs
SubagentsAgent for parallel evidence gathering

If a listed MCP isn't connected, say so and fall back to WebSearch against the underlying public source (clinicaltrials.gov, pubmed.ncbi.nlm.nih.gov).

Output layout

<workdir>/
└── waypoints/
    ├── progress.json                 # loop control
    ├── meta.json                     # phase 1
    ├── epidemiology.json             # phase 2
    ├── biology_soc.json              # phase 3
    ├── regulatory_trials.json        # phase 4
    ├── sources_evaluated.json
    ├── research_output.json          # phase 5 — structured output
    └── indication_dossier_report.md  # phase 5 — the deliverable

Schemas for every waypoint file are in references/waypoint-schemas.md. Waypoints are the resumable state. If the workdir already has waypoints, read them, summarize what's done, and ask which phase to resume from.

Before starting

Read references/00-research-standards.md. It governs sourcing and the anti-fabrication rules for every phase. Then create <workdir>/waypoints/.

Workflow

The dossier is built in five phases. After each phase, write the waypoint file and emit a ≤200-word summary of what you found and what's uncertain, then proceed directly to the next phase. The one exception is Phase 1: after writing meta.json, show the resolved indication identity and call ask_user with options Proceed / Revise identity / Stop, so a misread indication name can be caught before the expensive phases run. If ask_user is unavailable, state "proceeding on this interpretation; interrupt now to correct it" and continue.

Phase 1 — Meta initialization

Read references/01-meta-initialization.md. Resolve the indication identity: clinical definition, ICD codes, aliases, parent indication, and whether it's a recognized diagnostic entity. Run a quick CT.gov landscape scan. Stand up waypoints/meta.json.

Phase 2 — Epidemiology research

Read references/02-epidemiology-research.md. Characterize the population: diagnostic criteria, prevalence and incidence, demographics and risk factors, natural history. Use parallel subagents to search PubMed and the web simultaneously. Write waypoints/epidemiology.json.

Phase 3 — Biology & standard-of-care research

Read references/03-biology-soc-research.md. Establish pathophysiology, biomarkers, approved therapies, treatment guidelines, and unmet need. Use parallel subagents: PubMed for biology, web for guidelines, FDA for approvals. Write waypoints/biology_soc.json.

Phase 4 — Regulatory & trials research

Read references/04-regulatory-trials-research.md. Establish FDA/EMA accepted endpoints, regulatory precedents, typical trial design parameters, landmark trials, and notable failures. Use parallel subagents: FDA for guidance/approvals, CT.gov for trial patterns, PubMed for trial-history reviews. Write waypoints/regulatory_trials.json.

Phase 5 — Synthesis

Read references/05-synthesis.md and references/06-writing-style.md. Read all four consolidated waypoint files. Write waypoints/indication_dossier_report.md — narrative sections in the order the synthesis reference specifies, with inline citations per the style guide — and waypoints/research_output.json. No new research threads in this phase. Targeted gap-fills are allowed: a single fetch to resolve a specific missing value in an existing waypoint field (an approval year, an NCT ID, a figure from a sponsor pipeline page). Anything broader than that, name as a gap rather than filling it.

Resuming

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

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?

Generate a therapeutic indication dossier. Covers the patient population, epidemiology, disease biology, standard of care, regulatory precedent, and landmark clinical trials.

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/HughYau/AcademicForge/tree/site-first/skills/claude-science/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 HughYau 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 👇