AutoWiki

Your LLM Compiles a Knowledge Base. You Just Read and Ask.
_{Implementing Karpathy's LLM Knowledge Base vision — raw sources in, Obsidian wiki out.}
_{By AlphaLab-USTC}

English · 中文

--- ## The Idea Good survey papers don't just list references — they **identify milestones**, trace how ideas evolved, and organize the field into a coherent structure. AutoWiki does the same thing, automatically. Drop papers in. The LLM identifies **milestone nodes** (conceptual breakthroughs), clusters papers around them, builds a hierarchical file tree that mirrors the field's structure, and writes deep analysis with temporal links between sources. You get a survey-quality knowledge graph maintained in Obsidian — not a folder of disconnected notes. ``` You drop PDFs LLM compiles You browse in Obsidian ───────────── ────────────────────────── ────────────────────────────────── raw/new/ → Identify milestones kb/topics/ (milestone nodes) Cluster papers kb/sources/ (deep analysis pages) Trace temporal evolution kb/journal/ (cognitive timeline) Write cross-linked wiki index.md (survey-style tree) ``` --- ## 🚀 Quick Start ### 1. Clone & Install ```bash git clone https://github.com/AlphaLab-USTC/AutoWiki-skill.git pip install PyMuPDF ``` ### 2. Add as Skill In **Claude Code** or **OpenClaw**, just ask: ``` > "Add /absolute/path/to/AutoWiki-skill/skills to my global skillSources" ``` ### 3. Initialize Your Wiki ```bash cd your-wiki-project mkdir -p raw/new raw/compiled kb/{sources,topics,journal} output touch kb/index.md kb/log.md ``` ### 4. Start Using Open the project root as an Obsidian vault, then in Claude Code / OpenClaw: ``` > "Ingest the paper in raw/new/" > "How does X compare across the Y papers?" > "Lint the wiki" ``` **Prerequisites:** [Claude Code](https://docs.anthropic.com/en/docs/claude-code) or [OpenClaw](https://github.com/nicobailon/openclaw) + [Obsidian](https://obsidian.md/) + Python 3.12+ --- ## How It Works ``` raw/new/ ──→ LLM (Claude Code) ──→ kb/ ──→ Obsidian you drop reads, analyzes, sources/ you browse PDFs here links, writes topics/ journal/ ``` | Layer | Role | Who owns it | |-------|------|-------------| | `raw/` | Source archive (PDFs + figures) | You drop files; agent organizes | | `kb/` | Living wiki (markdown + wikilinks) | Agent writes & maintains everything | Three operations: **Ingest** (PDF → analysis page), **Query** (ask questions → synthesize → write back), **Lint** (25+ auto-checks). --- ## What Makes It Different | | | |---|---| | **🧠 Deep Analysis** | Not summaries. CRGP factors from the author's Introduction. Critical analysis with `prior`/`update` contrastive structure. Anti-patterns prevent generic filler. | | **🔗 Temporal Graphs** | Every source positioned in the field's timeline. Evolutionary chains, cross-domain links, temporal tensions — all automatic. | | **🏠 Self-Healing** | Three-tier autonomy: **Silent** (fix links) → **Notify** (record insights) → **Confirm** (restructure). 25+ lint checks. | | **🎯 Smart Classification** | 3-question fitness check before every topic assignment. Auto-scaling: <5 inline, ≥5 split, >8 sub-cluster. | --- ## 📂 Showcase: 80-Paper Wiki We built a real wiki on **Agent Self-Evolution** — 80 papers, 13 milestones, 2 hours. Here's what the LLM compiled (compiling 80 papers in Claude Code with Opus 4.6 cost ~150 🔪 of quota):

Wiki Knowledge Graph — 80 papers interconnected across 13 milestones

_{Each node is a source page. Red nodes are milestone topics. Edges are temporal relations (extends, complements, contrasts_with) — all discovered automatically.} ### Survey-Style Topic Organization Each topic is a **milestone node** — like a survey paper, it tells the story of how a research direction evolved. ``` agent-self-evolution (80 papers, 13 milestones) ├─ Mechanism Layer │ ├─ self-evolving-skill-libraries (7 papers) │ ├─ memory-evolution (12 papers, 2 sub-children) │ ├─ experience-driven-policy-evolution (5) │ ├─ llm-guided-evolutionary-search (8) │ └─ multi-agent-co-evolution (8) ├─ Application Layer │ └─ domain-applications (10 → scientific + clinical) └─ Cross-Cutting Layer ├─ agentic-evolution-theory (6) ├─ agent-safety-adversarial-evolution (5) └─ evolving-agent-surveys-benchmarks (6) ``` ### What the LLM Compiles | Output | Example | Live Demo | |--------|---------|-----------| | **Topic** — milestone node organizing a research direction | agent-self-evolution (80 papers → 3-layer taxonomy) | [**▶ View Topic**](https://alphalab-ustc.github.io/AutoWiki-skill/assets/demo-topic.html) | | **Source** — single paper deep analysis page | MemSkill (Zhang et al., 2026) — learnable memory skills | [**▶ View Source**](https://alphalab-ustc.github.io/AutoWiki-skill/assets/demo-source.html) |

Topic content preview — agent-self-evolution.md

> **Milestone Definition:** The paradigm of LLM-based agents that autonomously improve their capabilities post-deployment — treating evolution-time compute as a third scaling axis alongside training-time and inference-time compute. **Synthesis** — Three orthogonal layers: - **Mechanism** (what/how evolves): skill repertoire, memory system, decision policy, programs/algorithms, agent populations - **Application** (where applied): scientific and clinical instantiations under real-world constraints - **Cross-cutting** (theory/safety/eval): conceptual vocabulary, safety constraints, evaluation infrastructure **Unifying meta-principle:** the "information gap as training signal" pattern — skill libraries exploit skill-augmented vs. skill-free performance, memory evolution exploits memory-rich vs. memory-poor contexts, policy evolution exploits successful vs. failed trajectories.

Source content preview — memskill.md

```yaml type: source id: memskill milestone: "[[memory-evolution]]" tags: [memory-evolution, year/2026, venue/arXiv] ``` > **one_line:** "MemSkill reframes agent memory extraction from fixed hand-designed operations into a learnable, evolvable skill bank" **Novel Insight:** - *prior:* Memory management = content problem, solved with fixed logic (add/update/delete) - *update:* The extraction procedure is itself a variable — separating "how to remember" from "what to remember" enables joint optimization **Temporal Relations:** - `extends` [[cascade]] — same learning principle, different target layer - `complements` [[skillrl]] — orthogonal skill domains, shared optimization pattern - `contrasts_with` [[yunjue-agent]] — executable code vs. declarative skills

--- ## 💎 The Karpathy Pattern > *"Using LLMs to build personal knowledge bases… a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge."* — [Andrej Karpathy](https://x.com/karpathy/status/1911101737498091651) | Karpathy's Vision | AutoWiki | |---|---| | *"Index source documents into directory"* | `raw/` with extracted assets | | *"LLM incrementally compiles a wiki"* | `kb/` — analysis, synthesis, temporal positioning | | *"Backlinks, categorizes, writes articles"* | `[[wikilinks]]` + milestone hierarchy | | *"Obsidian as the IDE frontend"* | Project root = Obsidian vault | | *"LLM writes and maintains all the data"* | Agent owns `kb/` — proactive write-back | | *"LLM health checks over the wiki"* | 25+ lint checks | --- ## Why This Architecture? **Why Markdown?** LLMs work natively with text. No ORM, no migrations. Obsidian renders it beautifully — graph view, backlinks, all for free. **Why not RAG?** At personal KB scale (~100s of sources), a well-maintained index + grep outperforms vector search. No embedding pipeline needed. **Why a skill?** `SKILL.md` IS the architecture — 390 lines encoding quality standards, anti-patterns, and workflow rules. No servers, no infra. --- ## 📄 License MIT — see [LICENSE](LICENSE). ---

**AutoWiki** — *Your LLM compiles a wiki. Your knowledge compounds.* _{Inspired by Karpathy's LLM Knowledge Base pattern}