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Manage Project

Organization
Aperivue
manage-project

Use when managing a manuscript project over time. Scaffolds the project structure, tracks writing progress across phases, maintains project memory files, and generates submission checklists and backwards timelines (commands init, status, sync-memory, checklist, timeline).

Overview

PublisherAperivue
Repositorymedsci-skills
Skill namemanage-project
Stars
349
Forks
82
Bundled files
12
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.

  • 12 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 Manage 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/manage-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/manage-project .claude/skills/manage-project
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Manage 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 Manage 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 Manage 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.

Manage-Project Skill -- Research Project Management

Commands

/manage-project init {name} --type {type} --journal {journal} [--ssot] [--zotero-collection NAME]

Parameters:

  • {name} -- Project identifier (e.g., nnunet-skull-fracture, rfa-meta-analysis)
  • --type -- Paper type: original | meta | case | animal | technical | ai_validation | letter
  • --journal -- Target journal: RYAI | AJR | Radiology | European_Radiology | KJR | INSI | AJNR | generic
  • --ssot -- Emit SSOT.yaml (schema v1) from templates/SSOT.yaml.template instead of legacy project.yaml. Required, but not sufficient, for Phase 1C auto-enforce (the PostToolUse verify-refs hook blocks instead of warns): enforce also needs qc/migration_complete, which init does not write. After init, run migrate_project_to_ssot.py --project-root {target_dir} --mark-complete to validate the SSOT.yaml and write the marker. New projects should pass --ssot; legacy in-flight projects stay on project.yaml until /manage-project migrate-ssot is run.
  • --zotero-collection NAME -- Optional. Create a Zotero collection via pyzotero and populate library_id + collection_key in the contract. Requires ZOTERO_API_KEY + ZOTERO_LIBRARY_ID (optionally ZOTERO_LIBRARY_TYPE, default user). With pyzotero or the credentials missing, the contract is scaffolded with library_id: null / collection_key: null and a WARN is printed.

SSOT template substitutions: {{PROJECT_ID}} → {name}; {{PROJECT_TYPE}} → the SSOT project_type enum mapped from --type (original → original_research, meta → meta_analysis, case → case_report, ai_validation → ai_validation, else other). Without --zotero-collection, library_id / collection_key stay null until the owner links an existing collection.

Run scripts/init_project.py:

bash
python3 "${CLAUDE_SKILL_DIR}/scripts/init_project.py" \
    --name {name} --type {type} --journal {journal} [--ssot] \
    --project-root {target_dir}

It writes the contract file (SSOT.yaml with --ssot, else legacy project.yaml), the directory scaffold, the minimal stubs scripts/validate_project_contract.py requires (manuscript/index.qmd, artifact_manifest.json, qc/status.json), the memory-file stubs, and project_state.json. qc/migration_complete is NOT written by init — that marker belongs to the migrate script. For an --ssot project, run it with --mark-complete (no project.yaml needed): it validates the existing SSOT.yaml and writes the marker only on PASS. Until then the reference hook stays in warn mode.

Do not hand-build the scaffold. The script is the source of truth for the tree and for project_state.json; a hand-built one drifts from what the contract validator expects. Read ${CLAUDE_SKILL_DIR}/references/init_scaffold.md only when you need to know where a scaffolded file lands or what a project_state.json field means.

After the script, copy the matching reporting-guideline checklist from ${CLAUDE_SKILL_DIR}/../check-reporting/references/checklists/ into references/checklist_{GUIDELINE}.md.

/manage-project migrate-ssot [--no-mark-complete]

Converts a legacy project.yaml project into SSOT.yaml form and, by default, touches qc/migration_complete so Phase 1C auto mode switches from warn to enforce. On an SSOT-native project (SSOT.yaml present, no project.yaml — e.g. init --ssot) there is nothing to convert: --mark-complete validates the existing SSOT.yaml and writes the marker on PASS; SSOT.yaml is not rewritten.

bash
python3 "${CLAUDE_SKILL_DIR}/scripts/migrate_project_to_ssot.py" \
    --project-root . --write --mark-complete
  • --no-mark-complete: run with --write only. Use it when the project still has open QC failures — enforcement is deferred until the migration is validated.
  • The migrate script refuses to touch qc/migration_complete unless the generated SSOT.yaml passes validate_project_contract.py AND contract_mode=ssot. Do not touch qc/migration_complete manually.

Re-run after resolving failures; the script is idempotent.

/manage-project status

Report current progress: read project_state.json, scan the existing files, and check whether the project memory files are present and aligned. Take word-count limits from the target journal's profile, ${CLAUDE_SKILL_DIR}/../write-paper/references/journal_profiles/{JOURNAL}.md, not from memory. Lay the report out as in ${CLAUDE_SKILL_DIR}/references/status_output_format.md.

/manage-project sync-memory

Audit and refresh the project memory files:

  • check for PROJECT.md, STATUS.md, CLAIMS.md, DATA_DICTIONARY.md, ANALYSIS_PLAN.md, REVIEW_LOG.md
  • identify stale or contradictory project metadata
  • propose the minimum files to create or update; create them from ${CLAUDE_SKILL_DIR}/references/scaffold_templates.md
  • align project_state.json with the current manuscript phase

/manage-project checklist

Read the pre-submission checklist from ${CLAUDE_SKILL_DIR}/references/pre_submission_checklist.md and write it to submission/pre_submission_checklist.md. Then recommend /self-review (manuscript quality gate), /verify-refs (citation verification) and AI pattern removal (/write-paper Phase 7).

/manage-project timeline {submission_date}

Generate a backwards timeline from the submission date, in the layout of ${CLAUDE_SKILL_DIR}/references/timeline_example.md.


Project State Conventions

Phase numbers:

  • 0 = Init (scaffold created)
  • 1 = Outline approved
  • 2 = Table/Figure shells approved
  • 3 = Methods (critic >= 85)
  • 4 = Results (critic >= 85)
  • 5 = Discussion (critic >= 85)
  • 6 = Introduction + Abstract (critic >= 85)
  • 7 = Polish complete (AI pattern removal + checklist + self-review >= 80)

Phase status values: pending | in_progress | complete | blocked

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 Manage Project AI skill do?

Use when managing a manuscript project over time. Scaffolds the project structure, tracks writing progress across phases, maintains project memory files, and generates submission checklists and backwards timelines (commands init, status, sync-memory, checklist, timeline).

Why use Manage Project on TypingMind?

Because you install it once and use it with any model. Manage 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 Manage Project in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Aperivue/medsci-skills/tree/main/skills/manage-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 Manage 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 Manage Project?

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

Is the Manage 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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