# Research Second Brain — Starter Workspace

Starter kit for the Mervia Academy workshop **Build Your Research Second Brain with Claude Code**.

Everyone starts from this same workspace. During class you migrate your own paper notes,
highlights, exports, and drafts into it — with Claude Code doing the file work and your
research judgment staying in charge.

## Quick start

1. Unzip this folder anywhere you like (e.g. `~/research-second-brain`).
2. Open a terminal inside the folder.
3. Run `claude` — Claude Code reads `CLAUDE.md` and knows the workspace rules.
4. Run `/setup-check` once — Claude verifies your machine, installs anything
   missing (uv, Python, Graphify), and registers the `/graphify` skill for your
   whole machine. Safe to re-run any time.
5. When it's all green: type `/exit`, run `claude` again — `/graphify` now shows
   up in the command list.
6. Drop your existing notes into `inbox/` (or use the demo notes already there).
7. Run `/graphify` — it reads everything in the folder and builds your knowledge graph.

## The structure

| Folder | What lives there |
| --- | --- |
| `inbox/` | Raw notes land here — `/graphify` reads them as-is. |
| `sources/` | PDFs, exports, and original evidence files. |
| `graphify-out/` | Generated graph artifacts (appears after your first `/graphify` run). |
| `fallback/` | Pre-built `graphify-out/` — break glass if your build fails. |
| `.claude/` | The kit's tooling: the `/setup-check` command. |

## No notes of your own yet?

`inbox/` ships with demo notes from an LLM/AI-agent research corpus — deliberately messy,
in five different formats. Every step of the workshop runs on them, and the workshop
slides close with five knowledge-check questions you can answer once your second brain
is built, to prove the system actually works.

## The workflow

Drop raw notes into `inbox/` → run `/graphify` → it builds the graph
(`graphify-out/`: interactive `graph.html`, `GRAPH_REPORT.md`, persistent
`graph.json`). Then query it (`/graphify query "…"`,
`/graphify path "A" "B"`, `/graphify explain "Node"`) and keep it current
(`/graphify . --update`), adding new papers and notes whenever they arrive.

## If something goes wrong in class

`fallback/graphify-out/` is a pre-built graph from this exact workspace. If your
own `/graphify` build fails or is still running, copy that folder to the
workspace root (so it sits at `graphify-out/`) and continue — every query works
the same. It's a fallback, not the goal: rebuild your own graph when setup is
sorted, and it will overwrite the copied one.

## When you add new notes later

The same loop you ran in class, step by step:

1. Drop the new files into `inbox/` — exports, highlights, drafts. No cleanup needed.
2. Open a terminal in this folder and run `claude`.
3. Refresh the graph: `/graphify . --update` — it re-reads only new and changed files.
4. Ask: `/graphify query "your question"` — answers come from your notes, with sources.
5. Route what you learned into whatever you're writing — the output is yours.

Finding new papers: keep a short list of open research questions, and when you
search arXiv / Semantic Scholar / PubMed and find a paper worth keeping, drop it
into `inbox/` and `sources/` so it flows through the same steps. The second brain
grows the same way every time — there's no separate intake system to learn.
