Indication Dossier

SkillAI & models

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

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Indication Dossier skill

What this skill tells your AI

The instructions your AI receives, as published by pku-yuangroup/openai4s in skills/indication-dossier/SKILL.md and read by ahel’s review.

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.

Signals

GitHub stars
404
Forks
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Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
indication-dossier-pku-yuangroup
Source
github.com/pku-yuangroup/openai4s