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An autonomous research agent built on Karpathy's LLM Wiki— give it a topic, it researches the internet and builds a stru
An autonomous research agent built on Karpathy's LLM Wiki — give it a topic, it researches the internet and builds a structured wiki automatically.
Karpathy's LLM Wiki is a powerful pattern: instead of RAG, the LLM incrementally compiles raw sources into a persistent, interlinked wiki. The knowledge compounds over time.
autodidact takes it one step further: you don't even need to supply the sources. Give it a research topic, and it drives the entire loop autonomously — generating questions, searching the web, compiling the wiki, auditing gaps, and iterating until the topic is thoroughly covered.
Input: /autodidact options selling strategies, targeting 10% annualized return
Output: 22 wiki articles, 43 sources — ~1 hour unattended
your-project/
├── raw/ (43 files — auto-fetched web sources)
├── wiki/
│ ├── INDEX.md
│ ├── options-basics.md # definitions, payoff structure, key terms
│ ├── options-pricing-greeks.md # Black-Scholes factors, IV, Delta/Gamma/Theta/Vega
│ ├── domestic-options-market.md # tradable instruments, account requirements
│ ├── options-strategies.md # covered call, CSP, wheel, 10% feasibility
│ ├── covered-call-execution.md # strike/expiry selection, rolling, assignment handling
│ ├── options-risk-management.md # stop-loss rules (2-3x premium), position sizing
│ ├── options-tools-resources.md # IV percentile lookup, broker software
│ ├── spread-strategies.md # bull put spread, bear call spread, iron condor
│ ├── historical-performance.md # year-by-year results, bear market playbook
│ ├── beginner-roadmap.md # 4-stage path (1–1.5 years), tax notes
│ ├── iv-percentile-guide.md # IV percentile calc, high/low IV strategy selection
│ ├── wheel-strategy-execution.md # full Wheel cycle, parameter selection
│ ├── options-liquidity-guide.md # liquidity ranking by instrument and strike
│ ├── volatility-skew.md # negative skew mechanics, impact on strike selection
│ ├── short-strangle.md # construction, IV requirements, stop-loss, adjustment
│ ├── calendar-spread.md # same-strike buy-far-sell-near, Theta+Vega long
│ ├── diagonal-spread.md # PMCC poor man's covered call, vs calendar spread
│ ├── pin-risk.md # expiry pinning risk, Pinning Effect, avoidance
│ ├── margin-calculation.md # put/call seller formulas, spread saves 70–82% margin
│ ├── contract-code-decoder.md # 17-digit code structure, direction, expiry, strike
│ ├── csp-vs-covered-call.md # annualized return comparison, assignment handling
│ └── iron-condor.md # Short Strangle + protective legs, margin savings
└── outputs/
├── research_state.json (full research trajectory)
├── research_summary.md (final summary)
└── rounds/ (audit reports per round)
| Karpathy LLM Wiki | autodidact | |
|---|---|---|
| Core idea | LLM incrementally compiles a persistent wiki | Fully inherited ✓ |
| Source material | You collect and feed sources manually | Auto-retrieved from the web |
| Knowledge audit | Manual lint pass | Automated every round, drives next round |
| Research loop | None | Auto question → search → compile → audit → repeat |
| Usage | Manual ingest + query | One command, fully autonomous |
Four steps per round, audit output drives the next round's questions:
┌─────────────┐ ┌────────────┐ ┌──────────────┐ ┌────────────┐
│ questioner │────▶│ researcher │────▶│ wiki compile │────▶│ wiki check │
│ │ │ │ │ │ │ │
│ reads audit │ │ parallel │ │ quality- │ │ gap audit │
│ generates │ │ web search │ │ weighted │ │ outputs │
│ questions │ │ → raw/ │ │ → wiki/ │ │ report │
└─────────────┘ └────────────┘ └──────────────┘ └─────┬──────┘
▲ │
└──────────────── drives next round ───────────────────────┘
Stops when all conditions are met: audit gaps diminishing (≤60% of previous round) + question overlap >70% + no pending user questions + no new raw files
agent-browser — headless browser CLI that significantly improves retrieval success rate and content quality. Without it, autodidact falls back to Claude's built-in WebSearch, which works but with lower coverage.
npm install -g agent-browser
agent-browser install # downloads Chromium
On macOS, if Chrome fails to start (DevToolsActivePort error), set:
export AGENT_BROWSER_ARGS="--no-sandbox"
Copy the four skills into Claude Code's skills directory:
macOS / Linux
git clone https://github.com/your-username/autodidact
cd autodidact
cp -r skills/autodidact ~/.claude/skills/
cp -r skills/questioner ~/.claude/skills/
cp -r skills/researcher ~/.claude/skills/
cp -r skills/wiki ~/.claude/skills/
Windows
git clone https://github.com/your-username/autodidact
cd autodidact
Copy-Item -Recurse skills\autodidact "$env:USERPROFILE\.claude\skills\"
Copy-Item -Recurse skills\questioner "$env:USERPROFILE\.claude\skills\"
Copy-Item -Recurse skills\researcher "$env:USERPROFILE\.claude\skills\"
Copy-Item -Recurse skills\wiki "$env:USERPROFILE\.claude\skills\"
Restart Claude Code and type /autodidact to verify the installation.
Open Claude Code in a directory and run:
/autodidact <topic>
On first run, raw/, wiki/, and outputs/ are created automatically. No setup needed.
/autodidact <topic> [--max-rounds 20] [--background <what you already know>]
| Option | Description | Default |
|---|---|---|
topic | Research subject — the more specific, the better | required |
--max-rounds | Maximum number of research rounds | 20 |
--background | Your existing knowledge, skips basic questions | none |
# Technical research
/autodidact RAG architecture and engineering challenges in production
# Market research
/autodidact home care industry market landscape --max-rounds 5
# Skip basics with background knowledge
/autodidact options selling strategies 10% annual return --background "familiar with options basics and Greeks"
Ctrl+C to stop at any time. Re-run the same command in the same directory to resume from where it stopped.
While research is running, edit outputs/user_questions.md:
## Pending
- How does this strategy perform in a bear market?
- Is there any quantitative backtest data?
These are picked up at the start of the next round with highest priority.
All four skills can be used standalone:
# Compile raw/ sources into wiki/
/wiki compile
# Incremental compile (specific files only)
/wiki compile --files 2026-04-09_article.md
# Structural audit only (no question list)
/wiki check
# Full audit (question coverage + structural audit)
/wiki check outputs/rounds/round_5_questions.json --round 5
Q: Does it work without agent-browser? Yes. It falls back to Claude's built-in WebSearch + WebFetch automatically. Some sites will be blocked and content quality may be lower. Installing agent-browser is recommended.
Q: Does it support non-English topics? Yes. The researcher generates both Chinese and English search queries and retrieves from both language sources.
Q: What if the context window fills up?
autodidact checks context usage after each round. If it exceeds 50%, it automatically runs /compact and continues — no action needed from you.
Q: The wiki isn't deep enough after many rounds — what's wrong?
Usually the topic is too broad. Use --background to skip basics, or insert specific questions via user_questions.md to steer the research.
Wiki architecture based on Andrej Karpathy's LLM Wiki.
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