본문으로 건너뛰기
cccskills
무료GitHub에 공개

indication-dossier

Build a structured research dossier for a defined patient population, covering epidemiology, disease biology, current treatment, regulatory precedents, and major clinical trials. Receive a cited report and structured data to inform therapeutic development and trial planning.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

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

설치 방법 보기

이럴 때 유용합니다

  • Characterizing a target patient population
  • Reviewing current treatments and unmet needs
  • Researching regulatory precedents and landmark trials
  • Resuming a research dossier from saved progress

포함된 파일(10)

  • SKILL.md6.4 KB
  • .catalog_stamp13 B
  • references/00-research-standards.md3.0 KB
  • references/01-meta-initialization.md1.7 KB
  • references/02-epidemiology-research.md2.1 KB
  • references/03-biology-soc-research.md2.3 KB
  • references/04-regulatory-trials-research.md3.1 KB
  • references/05-synthesis.md5.2 KB
  • references/06-writing-style.md1.7 KB
  • references/waypoint-schemas.md2.9 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Predict structures for single proteins or protein complexes with AlphaFold2 through ColabFold. Use confidence scores and comparisons with a designed structure to assess whether a designed sequence folds as intended.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Predicting a single protein's structure from its sequence
  • Predicting structures of protein complexes
  • Checking designed sequences against a target structure
aipoch/open-science5.3천2026년 10월 20일 업데이트

boltz

무료

Use Boltz-2 to predict structures of complexes containing proteins, DNA, RNA, and small molecules. Obtain structure files and confidence scores to assess designed binders, with optional predictions of binding strength between a protein and a small molecule.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Assessing a designed binder against its target
  • Predicting a protein and small-molecule complex
  • Predicting complexes containing DNA or RNA
aipoch/open-science5.3천2026년 10월 20일 업데이트

borzoi

무료

Predict patterns of gene expression and other genomic measurements from DNA sequences with Borzoi. Compare reference and altered sequences to estimate regulatory effects, generate predicted coverage tracks, and prioritize variants in regions that do not encode proteins.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Comparing predicted effects before and after a DNA variant
  • Generating predicted coverage tracks for a genomic region
  • Prioritizing variants in non-coding regions
aipoch/open-science5.3천2026년 10월 20일 업데이트

chai1

무료

Predict three-dimensional structures of complexes containing proteins, RNA, DNA, and small molecules with Chai-1. Compare candidate structures using confidence scores and clash indicators, or run repeated predictions from Python to evaluate designed binders.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Predicting antibody–antigen complex structures
  • Evaluating protein–ligand binding structures
  • Reassessing designed binders
aipoch/open-science5.3천2026년 10월 20일 업데이트

Prepare reproducible setup and removal instructions for software environments on Open-Science SSH hosts, including Slurm clusters. Check available tools, define named activation settings, and validate the environment where jobs run after the user installs it.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Preparing a remote environment for required packages
  • Making job environment activation repeatable
  • Checking software and GPU operation on Slurm nodes
aipoch/open-science5.3천2026년 10월 20일 업데이트

customize

무료

Create and manage focused Specialist agents and Skills through conversation. Draft agent roles, review available Skills and Connectors, configure access, and manage changes, with Skill creation and publishing handled by the application's internal skill-creator.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Creating a Specialist for a focused role
  • Updating an agent's Skills and Connectors
  • Creating, revising, and publishing Skills
aipoch/open-science5.3천2026년 10월 20일 업데이트

aipoch의 스킬 모두 보기

이 스킬의 문제 신고