LLM Wiki Setup (Investment Research Second Brain)
Builds a pure-markdown investment research vault in Karpathy's LLM Wiki style by interviewing you and turning YOUR analysis framework into a living CLAUDE.md.
Data & AnalyticsIntermediate★ 1,323⑂ 212AI score 8/10Last updated: Aug 8, 2026
What it does
- Cleanly separates the mechanism layer (three-tier folders,
[[wikilinks]], a lint script, git pre-commit hook — installed automatically viascripts/init_vault.py) from the rules layer (what you actually look at and how you record judgments). - The core move is an interview, not a template. It walks through 8 dimensions (tracked metrics, how you log a thesis, whether to attribute views to analysts, how you review after earnings, note length, etc.) and writes the rules layer into
CLAUDE.mdin your own words. - Deliberately no RAG, vector DB, or embeddings. Knowledge is pre-compiled into structured pages so it compounds, instead of re-retrieving raw documents on every query — that's the whole point of the pattern.
- Ships SOPs for ingesting sources (5 human-in-the-loop checkpoints) such as sell-side reports, earnings calls and expert notes; a prediction→fulfillment review SOP for post-earnings; plus pruning discipline and counter-review guides.
Who it's for
- Investors tracking companies, industries, macro and analysts over years who want their reasoning to accumulate.
- Analysts and researchers whose reports and call notes pile up unused in a folder.
- Fans of Obsidian-style markdown vaults who don't want heavyweight retrieval infrastructure.
- Anyone who wants to document their own investment brain rather than adopt someone else's standard schema.
Examples
- New vault: ask for an "investment research second brain" — it scaffolds the empty skeleton, then interviews you and records your real criteria (e.g. "do next-quarter orders beat consensus", "management tone on the call").
- Ingest a source: hand it a semiconductor sector report; it splits the content into company / industry / analyst pages wired together with wikilinks.
- Post-earnings review: after results drop, it compares your logged prediction against the actual outcome, writes a review page, and can hand off to the
analyst-track-recordskill to backtest an analyst's hit rate.
· · · Install guide · · ·
Install in the Claude app (no terminal)
- Download the ZIP with the button below.
- In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
- Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
Install in Claude Code
Let Claude do it — paste this into Claude Code
Install the skill I found on Claude Skill Mart. Copy the llm-wiki-setup folder from the GitHub repo daymade/claude-code-skills into my ~/.claude/skills/llm-wiki-setup/. When it's done, tell me in one line what this skill can do.
Install with a command instead
git clone https://github.com/daymade/claude-code-skills.git && mkdir -p ~/.claude/skills && cp -r claude-code-skills/llm-wiki-setup ~/.claude/skills/⚠ This is a third-party skill. Check the source repository before installing.
- Open a terminal and clone the repo:
git clone https://github.com/daymade/claude-code-skills.git - Create the skills directory if needed:
mkdir -p ~/.claude/skills - Copy just this skill:
cp -r claude-code-skills/llm-wiki-setup ~/.claude/skills/ - Check prerequisites: Python 3,
git, anduvfor running the linter (orpip install pyyamlas a fallback). - Restart Claude Code, then say something like "help me set up an investment research wiki" to trigger the skill.
- It runs
python scripts/init_vault.py <folder>to scaffold the vault and then interviews you. Answer in your own language — do not copy the files inexamples/. - Inside the vault run
git init, thengit config core.hooksPath .githooksto enable the pre-commit lint. - Finish by ingesting one real research document so you see the full workflow end to end.
View source on GitHub ↗License: MIT