init-project-light

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Bootstrap a lightweight project with minimal guidance, context, and repository structure. Use when a small non-research project needs durable AI collaboration without the full research or course scaffold. Not for formal research projects; use $init-project-research.

AI & Automation 144 stars 27 forks Updated 3 days ago MIT

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Description 5%
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Skill Content

# Init Project Light > Lightweight project bootstrapper for small projects that do not need a full research-project scaffold. ## When to Use - Small document collections (proposals, applications, meeting notes) - One-off or short-lived projects - Projects without a code pipeline or Overleaf link - When the user says "set up something light", "quick init", "organise this folder" - Any project that does not warrant the installation's full research-project initializer ## When NOT to Use — Escalate to a full research-project initializer - Research papers targeting a journal or conference - Projects with code, data, or computational pipelines - Anything that needs Overleaf, git, or a vault atlas entry --- ## Phase 1: Scan Read everything already in the directory before asking questions. 1. List all files and folders (excluding `.claude/`, `.DS_Store`) 2. Read text files (`.md`, `.tex`, `.bib`, `.txt`) to understand content — respect file size (skip files > 500 lines, note them) 3. Build a mental model: what is this project, what's the main output, who's involved? **Goal:** Minimise interview questions by inferring answers from existing files. --- ## Phase 2: Interview (2-3 questions max) Use `the available structured-question mechanism`. Only ask what you couldn't infer from Phase 1. Pick from these (skip any you can already answer): 1. **What is this project?** — one sentence (e.g., "PhD research proposal for [University]") 2. **What's the main output?** — document...

Details

Author
flonat
Repository
flonat/flonat-research
Created
7 months ago
Last Updated
3 days ago
Language
Python
License
MIT

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