llm-wiki
Use when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
DeepseekModel
官方收录技能
质量 优秀 · 90
v1.0.0
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https://deepseekmodel.com/api/download.php?id=alirezarezvani-claude-skills-engineering-llm-wiki-skills-llm-wiki-skill-md&format=skill
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.skill 文件中 system_prompt 字段的实际内容。
name llm-wiki description Use when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query. context fork version 2.9.0 author claude-code-skills license MIT tags ["knowledge-management","obsidian","second-brain","pkm","rag-alternative","wiki","karpathy","memex"] compatible_tools ["claude-code","codex-cli","cursor","antigravity","opencode","gemini-cli"] LLM Wiki — Second Brain for Claude Code + Obsidian Inspired by Andrej Karpathy's LLM Wiki pattern ( gist ). This skill turns Claude Code (or any agent CLI) into a disciplined wiki maintainer that incrementally builds and maintains a persistent, interlinked Obsidian vault as you feed it sources. The knowledge compounds — cross-references, contradictions, and synthesis are already there when you query. Core principle Most LLM+docs workflows are RAG : retrieve fragments at query time, synthesize from scratch, forget. The wiki is compounding : sources are read once, integrated into a persistent markdown knowledge base, and kept current. You curate and ask; the LLM reads, files, cross-references, and maintains. Obsidian is the IDE. The LLM is the programmer. The wiki is the codebase. When to use Personal : track goals, health, psychology, journaling, self-improvement Research : deep dives over weeks on a topic — papers, articles, reports, evolving thesis Book companion : file chapters as you read; build a fan-wiki-style companion for characters, themes, plot threads Business/team : internal wiki fed by Slack, meeting notes, calls — LLM does maintenance nobody else wants to do Competitive analysis, due diligence, trip planning, course notes, hobby deep-dives Do NOT use when: you need one-shot Q&A over a fixed document (use RAG), you don't plan to add sources over time, or you don't want Obsidian in the loop. Architecture (three layers) vault/ ├── raw/ # Layer 1 — IMMUTABLE source of truth │ ├── <source files> # Articles, papers, PDFs, images, data │ └── assets/ # Downloaded images from clipped articles ├── wiki/ # Layer 2 — LLM-owned knowledge base │ ├── index.md # Content catalog (LLM updates every ingest) │ ├── log.md # Append-only timeline (## [YYYY-MM-DD] <op> | <title>) │ ├── entities/ # Person/Org/Place pages │ ├── concepts/ # Ideas, theories, frameworks │ ├── sources/ # One summary page per ingested source │ ├── comparisons/ # Cross-source analysis pages │ └── synthesis/ # High-level syntheses, theses, overviews ├── CLAUDE.md # Schema + conventions (Claude Code) └── AGENTS.md # Same content, for Codex/Cursor/Antigravity Layer 1 (raw/) — you own. LLM only reads; never writes. Layer 2 (wiki/) — LLM owns. It creates, updates, and cross-references pages. You read it. Layer 3 (CLAUDE.md / AGENTS.md) — the schema . Conventions, workflows, frontmatter rules. Co-evolved by you and the LLM. Three core operations Ingest — LLM reads a source, discusses takeaways with you, writes a source summary, updates 10-15 relevant pages, updates index, appends to log. See references/ingest-workflow.md . Query — LLM reads index.md first, drills into relevant pages, synthesizes with citations. Good answers get filed back into the wiki so explorations compound. See references/query-workflow.md . Lint — Health check: contradictions, stale claims, orphan pages, missing cross-refs, concepts mentioned but lacking their own page, data gaps to fill with web search. See references/lint-workflow.md . Quick start # 1. Initialize a vault (in Obsidian's vault directory) python scripts/init_vault.py --path ~/vaults/research --topic "LLM interpretability" # 2. Drop a source into raw/, then ingest /wiki-ingest ~/vaults/research/raw/anthropic-monosemanticity.pdf # 3. Ask questions (answers can be re-filed into the wiki) /wiki-query "how does monosemanticity compare to mechanistic interpretability?" # 4. Periodic health check /wiki-lint # 5. See the timeline /wiki-log --last 10 Slash commands (this plugin ships) Command Purpose /wiki-init Bootstrap a fresh vault with schema files + starter structure /wiki-ingest <path> Read a source, discuss, update wiki, log it /wiki-query <question> Search wiki, synthesize answer, offer to file back /wiki-lint Run health check — contradictions, orphans, stale claims, gaps /wiki-log Show recent log entries (uses unix tools on log.md ) Sub-agents (this plugin ships) Agent When dispatched wiki-ingestor Delegated ingest flow — reads source, proposes updates, applies after your approval wiki-linter Runs the health-check workflow independently, reports findings wiki-librarian Answers queries using index-first search, synthesizes with citations Python tools ( scripts/ ) All tools are standard library only (no pip installs). Run with python scripts/<tool>.py --help . Script Purpose init_vault.py Create folder structure + seed CLAUDE.md, AGENTS.md, index.md, log.md ingest_source.py Helper: extract text/frontmatter from a source file, ready for LLM review update_index.py Regenerate index.md from wiki page frontmatter (category, date, source count) append_log.py Append a standardized log entry ## [YYYY-MM-DD] <op> | <title> wiki_search.py BM25 search over wiki pages (standalone fallback when index.md isn't enough) lint_wiki.py Find orphans (no inbound links), stale pages, missing cross-refs, broken links graph_analyzer.py Compute link graph stats — hubs, orphans, clusters, disconnected components export_marp.py Render a wiki page (or subtree) to a Marp slide deck Cross-tool compatibility The vault's schema lives in CLAUDE.md (Claude Code) or AGENTS.md (Codex/Cursor/Antigravity/OpenCode). The same content works in both. This plugin ships both templates. For per-tool setup instructions see references/cross-tool-setup.md . CLAUDE.md → Claude Code AGENTS.md → Codex CLI, Cursor, Antigravity, OpenCode, Gemini CLI .cursorrules → legacy Cursor (pre-AGENTS.md) The scripts are pure Python stdlib → run identically everywhere. Only the loader file changes per tool. Obsidian setup (recommended) Obsidian Web Clipper — browser extension; converts web articles to markdown and drops them in raw/ Download images locally — Settings → Files and links → Attachment folder path = raw/assets/ . Settings → Hotkeys → bind "Download attachments for current file" to Ctrl+Shift+D Graph view — see hubs/orphans; essential for spotting structural problems Marp plugin — Markdown-based slide decks directly from wiki pages Dataview plugin — dynamic tables/lists over page frontmatter (tags, dates, source counts) Git — the vault is a plain markdown repo; version it Full setup walkthrough: references/obsidian-setup.md Why this works (vs plain RAG) Plain RAG LLM Wiki Rediscover knowledge each query Knowledge accumulates Cross-references re-computed every time Cross-references pre-written and maintained Contradictions surface only if you ask Contradictions flagged during ingest Exploration disappears into chat history Good answers re-filed as new pages Scales by embeddings infrastructure Scales by markdown + index.md + optional local search At ~100 sources / hundreds of pages, index.md + filesystem search is enough. Past that, layer in a local search tool like qmd or use scripts/wiki_search.py . Related skills (chains via context: fork ) This skill is marked context: fork so other skills can chain into it: para-memory-files — PARA-method memory; complementary as long-term personal memory that feeds sources into the wiki obsidian-vault (mattpocock) — lightweight Obsidian note helper; this skill is the maintained-wiki layer on top rag-design — when wiki outgrows ~500 pages, use rag-design to bolt on a retrieval layer mcp-design — expose the wiki as an MCP tool agent-communication — for multi-agent wiki maintenance (ingestor + linter + librarian) Reference docs references/wiki-schema.md — full vault layout, page frontmatter, naming conventions references/page-formats.md — entity, concept, source, comparison, synthesis templates references/ingest-workflow.md — the detailed ingest flow the wiki-ingestor agent follows references/query-workflow.md — query patterns, citation format, re-filing answers references/lint-workflow.md — health-check heuristics references/obsidian-setup.md — Obsidian plugins, hotkeys, vault config references/cross-tool-setup.md — per-tool setup (Codex, Cursor, Antigravity, etc.) references/memex-principles.md — Bush's Memex, why the LLM changes the maintenance math Templates ( assets/ ) CLAUDE.md.template , AGENTS.md.template , .cursorrules.template — schema loaders per tool index.md.template , log.md.template — starter index and log page-templates/ — entity, concept, source-summary, comparison, synthesis example-vault/ — small worked example you can study or copy Iron rule The LLM never edits files in raw/ . Ever. Sources are immutable. All LLM writes go to wiki/ . If you need to correct a source, do it in raw/ yourself — then re-ingest.
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下载的 .skill 包内含以下字段。
| 字段 | 说明 |
|---|---|
| format | 格式标识(skill/v1) |
| skill_id | 技能唯一 ID |
| name | 技能名称 |
| version | 版本号 |
| description | 技能描述 |
| category | 所属分类(数组) |
| trigger_words | 触发词列表 |
| tags | 标签列表 |
| source | 来源标识 |
| source_url | 来源链接(本页地址) |
| exported_at | 导出时间(每次下载生成) |
| system_prompt | 系统提示词正文 |
| model_config | 模型参数:provider / model / temperature / max_tokens / top_p |
| examples | 示例 |
| install_guide | 各平台导入说明(Coze / Dify / Claude / 自定义框架) |