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Generate a therapeutic indication dossier. Covers the patient population, epidemiology, disease biology, standard of care, regulatory precedent, and landmark clinical trials.
$ npx -y skills add aipoch/open-science --skill indication-dossier --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/indication-dossierContext preview
The summary Claude sees to decide when to auto-load this skill.
Generate a therapeutic indication dossier. Covers the patient population, epidemiology, disease biology, standard of care, regulatory precedent, and landmark clinical trials.
name: indication-dossier description: > Generate a therapeutic indication dossier. Covers the patient population, epidemiology, disease biology, standard of care, regulatory precedent, and landmark clinical trials. license: Apache-2.0
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.
**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.
"idiopathic pulmonary fibrosis").
indication, or other framing.
Defaults to `./do_not_commit/indication-dossier-<slug>/`.
| Purpose | Tool | | ------------------ | -------------------------------------------------------------------------------------------------------------------------- | | ClinicalTrials.gov | `clinical-trials` MCP | | Literature | `pubmed` MCP | | Web | `WebSearch`, `WebFetch` — FDA guidance, treatment guidelines (NCCN, AASLD, specialty societies), CDC/WHO epidemiology data | | Documents | `WebFetch` for remote PDFs; `Read` for local PDFs | | Subagents | `Agent` 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).
<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 deliverableSchemas 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.
Read `references/00-research-standards.md`. It governs sourcing and the anti-fabrication rules for every phase. Then create `<workdir>/waypoints/`.
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.
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`.
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`.
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`.
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`.
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 re
The open-source AI research workbench for scientific research and agent workflows. Local-first, model-agnostic desktop app with extensible skills, MCP tools and connectors, Python/R execution and traceable artifacts for reproducible research on macOS, Windows and Linux.
Repo: aipoch/open-science
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