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Intake Project

Organization
Aperivue
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.

Overview

PublisherAperivue
Repositorymedsci-skills
Skill nameintake-project
Stars
349
Forks
82
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Aperivue on GitHub. Read the source before you install it.

Installation

Install the Intake Project AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Aperivue/medsci-skills/tree/main/skills/intake-project
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Aperivue/medsci-skills.git /tmp/medsci-skills
mkdir -p .claude/skills
cp -r /tmp/medsci-skills/skills/intake-project .claude/skills/intake-project
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Intake Project in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Intake Project on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Intake Project is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

text
## 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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Intake Project AI skill do?

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.

Why use Intake Project on TypingMind?

Because you install it once and use it with any model. Intake Project is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Intake Project in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Aperivue/medsci-skills/tree/main/skills/intake-project. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Intake Project?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Intake Project?

As many as you like. As long as a model supports skills, you can use Intake Project with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Intake Project AI skill free?

Yes. It is published on GitHub by Aperivue under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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