본문으로 건너뛰기
cccskills
무료GitHub에 공개

intake-project

Use when starting or inheriting a radiology research project and need to know what it is. Classifies the project type, summarizes its current state, lists missing inputs, recommends next steps and scaffolds lightweight project memory files.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(3)

  • SKILL.md6.5 KB
  • references/memory_templates.md295 B
  • skill.yml1.2 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Intake-Project Skill

Canonical Manuscript Folder Structure

/manage-project init scaffolds new projects; its script is the source of truth for that tree, including the qc/ folder the QC skills write to. Use the layout below to reorganize an existing project that was never scaffolded, mapping each artifact into one of these slots or into a folder the init scaffold or another skill already writes to.

{project_root}/
├── HANDOFF.md                         # session handoff entry point
├── README.md                          # project overview
├── data/                              # raw data (NEVER edit; read-only)
├── analysis/                          # reproducible scripts (00_* → 04_*)
├── output/                            # analysis outputs: CSVs, PNGs, intermediates
├── qc/                                # QC reports the skills write (self-review, checklists, audits)
├── irb/                               # IRB/ethics docs
├── proposal/                          # original protocol / approved proposal
├── reviews/                           # external correspondence
├── manuscript/                        # SOURCE manuscript + drafting
│   ├── manuscript_v{N}.{md,docx,pdf}  # current canonical working version (top level)
│   ├── build_unified_docx.py          # or pandoc wrapper
│   ├── archive/                       # ALL prior versions v1 .. v{N-1}
│   ├── reviews/                       # review notes (QC reports go in qc/ at the root)
│   ├── figures/                       # figure scripts + rendered PNG/PDF
│   └── tables/                        # table scripts + rendered docx
└── submission/                        # per-journal packages
    └── {journal-slug}/                # e.g., chest/, kjr/
        ├── CHECKLIST.md
        ├── cover_letter.{md,docx,pdf}
        ├── title_page.docx            # separated for double-anonymized
        ├── manuscript_anonymized.{docx,pdf}
        ├── supplement.{docx,pdf}
        ├── strobe_checklist.md        # or PRISMA / CONSORT
        ├── circulation_email.md
        └── figures/                   # submission-ready DPI copies

Rules

  • manuscript/ = source; submission/{journal}/ = derived artifacts. Regenerate submission files from manuscript/manuscript_v{N}.md; never edit anonymized/title-page directly.
  • One canonical working version at manuscript/manuscript_v{N}.{md,docx,pdf}. Older versions move to manuscript/archive/ immediately on version bump.
  • No loose files at project root. Only HANDOFF.md, README.md, the project memory files (Phase 3), the contract files /manage-project init writes (SSOT.yaml or project.yaml, project_state.json, artifact_manifest.json), and folder entries.
  • QC artifacts (self-review, reporting checklists, cross-reference and reference audits) live in qc/ at the project root, where the skills that produce them write them, not at manuscript top level.
  • On rejection/retarget: cp -r submission/{old} submission/{new}, then rewrite cover letter and reformat.
  • Double-anonymized journals (Chest, AJRCCM): title page and anonymized manuscript MUST be separate files under submission/{journal}/.
  • Keep existing project labels and file names in the language the workspace already uses.

When to apply

  • At project intake: scaffold through /manage-project init — unless the user only wants a quick assessment.
  • At first submission prep: create submission/{journal}/ and populate.
  • Mid-project cleanup: when manuscript/ has >3 versioned files or QC docs at top level, reorganize.
  • Before session handoff: reorganize if structure is drifting.

Workflow

Phase 1: Discover context

  1. Read top-level folder names and key files.
  2. Detect manuscript-like files, tables, figures, protocols, and analysis outputs.
  3. Extract:
    • project title or working title
    • study question
    • dataset or cohort hints
    • collaborators or institutions
    • venue/journal hints

Phase 2: Classify project and stage

Determine:

  • project type: original | review | meta-analysis | case report | technical note | grant | peer review | challenge | career-doc
  • primary domain: radiology | medical AI | multimodal LLM | intervention | survival/prognostic | diagnostic accuracy | workflow
  • target output: paper | abstract | grant | review | rebuttal | CV
  • likely target journal or venue — only if the files name one
  • one current stage: idea | data assembly | analysis planning | analysis in progress | drafting | revision | submission prep | archived/unclear

If the folder mixes several studies, say so rather than collapsing them into one.

Gate: Present the classification (project type, stage, target output) to the user. Confirm before creating any files — misclassification leads to wrong scaffold and wrong skill routing.

Phase 3: Surface missing inputs

Check for blocking dependencies and common gaps:

  • no explicit study question
  • no target journal
  • no analysis plan
  • no variable dictionary
  • no claims-to-results map
  • no review log for revised manuscripts

If missing, propose or create lightweight anchor files — PROJECT.md, STATUS.md, CLAIMS.md, DATA_DICTIONARY.md, ANALYSIS_PLAN.md, REVIEW_LOG.md — only those the project type justifies. Read ${CLAUDE_SKILL_DIR}/references/memory_templates.md when creating PROJECT.md or STATUS.md.

Phase 4: Produce normalized summary

Output this structure:

## Project Intake Summary
Project: ...
Type: ...
Current stage: ...
Likely target: ...

### What exists
- ...

### What is missing
- ...

### Risks / ambiguities
- ...

### Recommended next actions (3-5, in dependency order)
1. ...
2. ...
3. ...

Handoff Rules

After intake:

  • route to search-lit if the literature basis is weak
  • route to design-study if the research question exists but design logic is unclear
  • route to manage-project if the folder should be scaffolded
  • route to write-paper only after the project phase is clearly drafting

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

Use when a medical AI paper should be found and cited by AI search engines and RAG tools. Optimizes the title, abstract, summary box (Key Points, Research in Context), keywords, preprint, GitHub README/CITATION.cff and Hugging Face card, returning a visible pass/fail checklist.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Aperivue/medsci-skills3462026년 10월 19일 업데이트

Use when a target journal has no profile yet. Reads the journal's author guidelines and writes a detailed /write-paper profile plus a compact /find-journal profile, public or user-local private, in the canonical format with quality gates.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Aperivue/medsci-skills3462026년 10월 19일 업데이트

Use when data needs statistical analysis. Runs reproducible Python/R code for Table 1, diagnostic accuracy, agreement, regression, survival, propensity score, survey-weighted and repeated-measures models, with publication tables. Sample size is /calc-sample-size; pooling studies is /meta-analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Aperivue/medsci-skills3462026년 10월 19일 업데이트

Renamed to /model-selection in v6 (removed in v7).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Aperivue/medsci-skills3462026년 10월 19일 업데이트

Use when analyzing a researcher's publication record from PubMed. Fetches an author's papers, classifies study types and author position, charts the patterns and writes a strategy report, with an optional trajectory-archetype classification. Works from PubMed metadata only.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Aperivue/medsci-skills3462026년 10월 19일 업데이트

Use when one validated cohort analysis must be repeated across many exposure/outcome pairs. Generates one R/Python script per combination from a single methodology template, changing only the variables, and aggregates the results into a summary matrix.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Aperivue/medsci-skills3462026년 10월 19일 업데이트

Aperivue의 스킬 모두 보기

이 스킬의 문제 신고