Indication dossier

SkillMedia

Lets your agent build a sourced research dossier on a disease indication, covering patients, care standards, and trials.

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Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

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Indication dossierStart free
About this skill

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.

What this skill tells your AI

The instructions your AI receives, as published by xuzhougeng/wisp-science in skills/indication-dossier/SKILL.md and read by Ahel’s review.

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

Signals

GitHub stars
1k
Forks
104
Last commit
Oct 2026
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Item type
skill
Key
indication-dossier-xuzhougeng
Source
github.com/xuzhougeng/wisp-science