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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 & AnalyticsIntermediate1,323212AI 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 via scripts/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.md in 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

  1. 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").
  2. Ingest a source: hand it a semiconductor sector report; it splits the content into company / industry / analyst pages wired together with wikilinks.
  3. 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-record skill to backtest an analyst's hit rate.

· · · Install guide · · ·

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
Download 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.

  1. Open a terminal and clone the repo: git clone https://github.com/daymade/claude-code-skills.git
  2. Create the skills directory if needed: mkdir -p ~/.claude/skills
  3. Copy just this skill: cp -r claude-code-skills/llm-wiki-setup ~/.claude/skills/
  4. Check prerequisites: Python 3, git, and uv for running the linter (or pip install pyyaml as a fallback).
  5. Restart Claude Code, then say something like "help me set up an investment research wiki" to trigger the skill.
  6. 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 in examples/.
  7. Inside the vault run git init, then git config core.hooksPath .githooks to enable the pre-commit lint.
  8. Finish by ingesting one real research document so you see the full workflow end to end.