Project Name
Vault template
First committed by asakin
Published in asakin/llm-context-base. Publisher page
74 GitHub stars
What it is
A git template for building your own LLM-powered personal wiki. Training period, metadata standard, lint system included. Clone and go.
AutoGineer overview, from the brain’s own files
llm-context-base is an opinionated template designed to turn a local git repository into an LLM-powered personal wiki. It implements a three-layer pattern consisting of raw source documents, an LLM-maintained wiki, and a schema configuration. The system is designed to adapt to a user's workflow over time, eliminating the need for upfront taxonomy design. It includes a metadata standard for queryable files, an inbox-first capture system with a 7-day TTL, and a linting system to maintain document health. It supports various AI tools including Claude Code, Cursor, and GitHub Copilot, providing a consistent interface for managing personal knowledge.
How it works
Memory model: LLM wiki
AutoGineer overview, from the brain’s own files
The system uses a specific folder structure including 1-Projects, 2-Knowledge, 3-Journal, and 4-Private, alongside configuration and metadata folders. Users capture information into an _inbox folder, which the AI then processes and files according to a metadata standard. The system is bootstrapped via configuration files in .claude, .cursor, and .github, which direct the AI to follow instructions located in _config and _meta. It uses a training period to learn user patterns and maintains documentation through a split README file that separates user-specific content from framework documentation.
| Folder | Role | Files |
|---|---|---|
| .claude | agent-config | 2 |
| .cursor | agent-config | 1 |
| .github | agent-config | 1 |
| .obsidian | obsidian-config | 7 |
| assets | attachments | 2 |
| docs | docs | 13 |
| examples | examples | 4 |
| 1-Projects | folder | 3 |
| 2-Knowledge | folder | 5 |
| 3-Journal | folder | 1 |
| 4-Private | folder | 1 |
| _config | folder | 4 |
| _inbox | folder | 1 |
| _meta | folder | 14 |
| _output | folder | 1 |
| _sources | folder | 2 |
The agent reads
- CONTRIBUTING.md evidence: .claude/CLAUDE.md
- _config/config.md evidence: README.md
The agent writes
- _config/config.md evidence: .claude/CLAUDE.md
- _config/context.md evidence: .claude/CLAUDE.md
- _inbox/ evidence: .claude/CLAUDE.md
- _output/ evidence: README.md
Requirements
Apps: Obsidian
Databases: MongoDB, PostgreSQL
Works with: Claude Code, GitHub Copilot, Cursor, Devin (ex-Windsurf)
Set it up
- Download the brain as a ZIP and unzip it where you keep your notes or project: https://mp-staging.autogineer.com/agent-brains/asakin/llm-context-base/download
In Obsidian, choose “Open folder as vault” and pick the folder.
Claude Code reads .claude/CLAUDE.md from this folder automatically.
cd llm-context-base && claude
AutoGineer scanned these files at commit 3c096ca.
First committed by asakin. Published in asakin/llm-context-base. Licence: Apache License 2.0.
Usage
AutoGineer overview, from the brain’s own files
- Capture unstructured notes into the inbox for later organization by the AI.
- Maintain decision records with context, options, and rationale.
- Query the wiki to synthesize information across multiple documents.
- Run linting commands to identify stale files or missing metadata.
- Manage project initiatives with briefs and timeline tracking.
- Generate reports or summaries from stored knowledge.
- “Capture this: here's how our onboarding process works...”
- Creates a file in the inbox and offers to file it in the appropriate location.
- “We decided to use PostgreSQL over MongoDB, let me document why”
- Generates a decision record with context, options, rationale, and an outcome section.
- “What do we know about our hiring process?”
- Scans file summaries and synthesizes an answer with source citations.
- “Lint the wiki”
- Surfaces stale files, missing metadata, and orphaned documents.
Documentation by asakin
[Project Name]
This README will be personalized during your first session.
<!-- llm-context-base:framework-readme -->
llm-context-base
An opinionated template for building your own LLM Wiki.
Use the template, point your AI assistant at it, and start talking. Over time, it learns how you work and adapts its structure to your needs without you having to design anything upfront.
How This Relates to Karpathy's LLM Wiki
Andrej Karpathy's LLM Wiki gist describes a three-layer pattern: raw source documents → an LLM-maintained wiki → a schema config file (like CLAUDE.md or AGENTS.md). The LLM reads sources, maintains structured wiki pages, and follows the schema config to stay consistent.
llm-context-base implements this pattern as a ready-to-use template — so you don't have to build it from scratch. What it adds on top of the base pattern:
Metadata standard — every file gets a queryable header (type, summary, tags, status, date). The LLM scans these instead of loading full files, which scales cleanly to hundreds of documents.
Inbox-first capture — a frictionless landing zone with a 7-day TTL. You capture without deciding where things go. The LLM files them later.
Training period — the system starts chatty (learning your patterns) and goes quiet as it adapts. You never design a taxonomy upfront.
Lint system — surfaces stale files, missing metadata, and orphaned docs on demand.
Multi-LLM support — bootstrap shims for Claude Code, Cursor, Copilot, Windsurf, and others. One config, any tool.
Full comparison with the Karpathy pattern →
Quick Start
1. Create your copy. Click "Use this template" on GitHub. Make it private. Clone it.
git clone https://github.com/YOUR-USERNAME/my-wiki.git
cd my-wiki2. Set up your workspace. Open the repo as an Obsidian vault on one side, your AI agent on the other. You talk to the AI — it builds the wiki. You watch it happen in Obsidian.
Full Obsidian setup → · No Obsidian? Any editor + any supported AI tool works fine.
3. Fill in your profile. Open _config/config.md, fill in the About You section, and set Training Start Date to today.
4. Start talking. Open in your AI tool of choice and go:
"Capture this: here's how our onboarding process works..."
"We decided to use PostgreSQL over MongoDB, let me document why"
"What do we know about our deployment process?"
"Lint the wiki, what needs attention?"
That's it. The system adapts to what you actually need.
Want to stay updated as the framework improves? The template approach gives you a clean start but disconnects you from upstream. If you want to pull future improvements (new protocols, better training behavior, bug fixes), use the Framework Track setup instead.
What Makes This Different
It learns how you work. The first 30 days are a training period. Your AI asks questions, suggests directories, and logs what it learns. By day 45, it's quiet and efficient. You never design a taxonomy upfront. Full details →
Capture first, organize later. Everything lands in _inbox/ with a 7-day TTL. You never hesitate to capture because you don't have to decide where it goes yet. Your AI files it later.
Your README stays yours. This file has two sections split by a marker. Above it: your personal project README, maintained by your AI as the wiki evolves. Below it: the framework docs, which track upstream cleanly. You get living documentation without merge conflicts.
Patterns are captured as they emerge. When your AI learns a new convention or preference during a conversation, it writes it down immediately, not at commit time. Nothing is lost if you commit from the command line without AI involvement.
Every file is queryable. A metadata standard on every document means your AI can scan summaries and tags without loading full files. This scales to hundreds of documents.
It maintains itself. Lint checks surface stale files, missing metadata, and orphaned docs. The system even reviews its own instruction efficiency to prevent context window bloat as your wiki grows.
What You Can Do
| Say this | What happens |
|---|---|
| "Capture this: here's how we set up monitoring..." | Creates a file in _inbox/, offers to file it |
| "We decided to use Terraform over Pulumi, document why" | Decision record with context, options, rationale, and an outcome section to fill in later |
| "What do we know about our hiring process?" | Scans summaries, synthesizes an answer with source citations |
| "Lint the wiki" | Surfaces stale files, missing metadata, orphaned docs |
| "Start a new initiative: migrate to Kubernetes" | Project workspace with brief, timeline, artifact tracking |
| "Create a slide deck summarizing Q1 decisions" | Generates output in _output/, dated and organized |
These form a cycle. Capture feeds queries, queries inform decisions, decisions get tracked, lint keeps it all healthy. The system compounds over time.
Go Deeper
| Topic | What's there |
|---|---|
| Training Phases | How the system adapts to you over 30 days |
| Directory Structure | What each folder is for, why there are only 4 |
| Journal Sync & Privacy | Syncing 3-Journal/ across devices — plaintext, git-crypt, or external |
| Metadata Standard | The format that makes everything queryable |
| Supported Tools | Claude Code, Cursor, Copilot, Windsurf, ChatGPT, and others |
| Obsidian Setup | Pre-configured vault, plugins, web clipper |
| Design Decisions | Why the system works the way it does |
| FAQ | Common questions |
| Linked Code Repos | Connect wiki project entries to real code repos — workspace files, context files, Obsidian plugin dev |
| Extending & Contributing | Customize locally vs. contribute upstream |
| Future Directions | Where this is headed |
| Philosophy | The deeper thinking behind the system |
The examples/ directory has four sample files showing the metadata standard in action. Read them, then delete them when you're comfortable.
Credits
Built on Andrej Karpathy's LLM Wiki Pattern. Implementation by Ariel Sakin, refined over several months of daily use managing business operations.
License
Apache 2.0. See LICENSE.
_config/standard.md
type: knowledge summary: The canonical metadata standard for all files in this system — makes every document queryable by LLMs and writable by agents. tags: [meta, standard] status: active updated: 2026-04-09
Document Standard
Standard Tiers
The standard has two tiers. This prevents the standard from becoming a blocker on legitimate use cases while keeping the core queryable.
Tier 1 — Universal (always enforced) Required on every content file. Non-negotiable. Lint will flag missing Tier 1 fields.
type— what kind of document this issummary— one sentence a future query can usetags— domain and type vocabularystatus— lifecycle stateupdated— date of last meaningful change
Tier 2 — Advisory (context-dependent) Use when relevant. Not required. Lint will not flag missing Tier 2 fields.
owner— relevant for team wikis, shared knowledgeconfidence— relevant for research, claims, uncertain knowledgefailure_reason— required whenstatus: archivedorsupersededrelated— helpful for navigating dense knowledge graphs
Why This Standard Exists
As your knowledge base grows, documents accumulate across directories. Without a common metadata layer, finding information requires manual navigation or loading entire files into context. This standard makes every document in the system:
Queryable — LLMs can scan summaries and tags without reading full files
Writable — Agents and automations know exactly how to create compliant files
Cross-cuttable — "What's happening with #deployment this week?" returns results across all content types
Fresh-trackable — The
Updatedfield makes it easy to flag stale content
Based on Andrej Karpathy's LLM Wiki Pattern, extended to cover structured knowledge management.
The Metadata Block
Every wiki file gets a YAML frontmatter block at the very top, before the # Title line. Exceptions: docs/ files, _meta/ system files, and README.md files do not get frontmatter.
---
type: [see types below]
summary: One sentence describing what this file contains or its current state.
tags: [domain, type, topic]
status: [see statuses below]
owner: Person or role responsible
updated: YYYY-MM-DD
related: "[file1](path/file1.md), [file2](path/file2.md)"
---Required vs Optional by Type
| Type | Required Fields | Optional Fields |
|---|---|---|
knowledge | Type, Summary, Tags, Status, Updated | Owner, Related, Confidence |
decision | Type, Summary, Tags, Status, Owner, Updated | Related |
initiative | Type, Summary, Tags, Status, Owner, Updated | Related |
meeting | Type, Summary, Tags, Updated | Status, Owner, Related |
journal | Type, Summary, Updated | Tags, Status, Owner, Related |
todo | Type, Summary, Tags, Status, Updated | Owner, Related |
status | Type, Summary, Updated | Tags, Status, Owner, Related |
template | Type, Summary, Tags | Status, Owner, Updated, Related |
Field Definitions
Type — What kind of document this is (see Types section below).
Summary — One sentence, present tense, describing what the file contains OR its current state. This is the most important field — a future query will use it to decide relevance without reading the whole file.
Tags — YAML array, no # prefix (e.g. [engineering, how-to]). Start with tags that make sense for your domain. During the training period, your AI will help you build a consistent tag vocabulary.
Status — Lifecycle state:
active— in use, current, being maintaineddraft— work in progress, not finalizedarchived— no longer active but kept for reference. When setting this, also addfailure_reason:(see below).superseded— replaced by a newer file or approach. Requiresfailure_reason:explaining what replaced it.complete— finished (for todos, initiatives, decisions)blocked— waiting on something external
Failure_reason — Required when status: archived or status: superseded. One sentence explaining why the file was retired or replaced. Prevents "ghost" archived pages with no explanation.
failure_reason: Replaced by 2-Knowledge/new-approach.md after the April 2026 refactor.Confidence — Optional. Use on knowledge articles where claims may be uncertain or based on incomplete information.
high— well-sourced, verified, stablemedium— reasonable confidence, may need revisitinglow— speculative, early-stage, needs more sourcing
Omit the field entirely when confidence is not relevant (tasks, journals, templates).
Owner — Person or role responsible. Use whatever naming makes sense for you.
Updated — ISO date (YYYY-MM-DD) of last meaningful content change. Not just metadata tweaks.
Related — Comma-separated relative links to related files. Use relative paths from the file's location.
Document Types
| Type | Use for | Default directory |
|---|---|---|
knowledge | How-tos, guides, reference material, processes | 2-Knowledge/ |
decision | Recorded decisions with context, options, rationale | 2-Knowledge/Decisions/ |
initiative | Multi-artifact projects in flight | 1-Projects/ |
meeting | Meeting notes and action items | 3-Journal/ or wherever you keep meeting notes |
journal | Reflections, observations, daily notes | 3-Journal/ |
todo | Task lists and action items | Root or wherever you track tasks |
status | Current state snapshots | Root |
template | Templates for generating other files | _meta/templates/ |
Tags
Tags are free-form but should be consistent. During the training period, your AI will help you develop a vocabulary that fits your domain. Some starter categories:
Domain tags — What area does this relate to?
Examples:
engineering,design,operations,personal,finance,health,cooking
Type tags — What kind of content is this?
Examples:
how-to,reference,decision,process,architecture
Status/priority tags — Optional, for filtering:
Examples:
urgent,someday,recurring
Rule: Always use tags from your established vocabulary. If you need a new tag, add it intentionally — don't let tags proliferate randomly. Your AI will help manage this during training.
Query Protocol
When answering a query, the AI should:
Classify the question — what type and domain is this about?
Route to the right directory using the query routing in
_meta/instructions/knowledge-query.mdScan summaries first — read file names and
summary:frontmatter fields before reading full contentRead 1-3 most relevant files — don't read everything, use tags to filter
Synthesize and cite — always include source file path and
Updateddate in the response
Write Protocol
When creating or modifying a file, the AI must:
Add YAML frontmatter at the very top of the file, before the
# TitlelineUse the correct type from the types table above
Write a useful Summary — one sentence a future query can use to decide relevance
Pick 2-5 tags from the established vocabulary
Set Status appropriately (new files default to
draft)Set Updated to today's date
Add Related links to 1-3 related files if obvious connections exist
House Style
All files in this system follow plain CommonMark-compatible markdown. The goal is consistent rendering across GitHub, Obsidian, VS Code, raw text, and AI parsing — with no renderer-specific hacks.
Structural elements to use:
Headings (
#,##,###)Paragraphs (blank line between)
Bullet lists (
-) — tight (no blank lines between items) unless items need internal spacingNumbered lists for sequences where order matters
Fenced code blocks (
```)Mermaid blocks for diagrams
---horizontal rules to separate major sectionsMarkdown links
[text](path)— never bare HTML
Avoid:
Using HTML as a substitute for real markdown structure — if a heading, list, or code block can do it, use that
Soft line breaks as a formatting tool (they render as spaces in CommonMark)
Loose list formatting unless items genuinely need breathing room
Patterns that only render correctly in one tool
HTML is acceptable when there is no markdown equivalent (e.g., <sub>, <kbd>, collapsible sections). The test: does this need HTML, or am I using HTML because I didn't think of the markdown alternative?
Metadata format: YAML frontmatter. Renders as Obsidian's Properties panel (reading view), a bordered table on GitHub, and clean structured text in raw. No dots, no special syntax in the document body.
Migration
New files: Always compliant. Templates generate the metadata block automatically.
Existing files: Add the metadata block when you next edit the file. Don't mass-backfill — the maintenance cost outweighs the benefit for rarely-accessed files.
73 files
- .claude/CLAUDE.md
- .claude/settings.json
- .cursor/rules/bootstrap.mdc
- .cursorrules
- .github/copilot-instructions.md
- .gitignore
- .obsidian/app.json
- .obsidian/appearance.json
- .obsidian/core-plugins.json
- .obsidian/daily-notes.json
- .obsidian/graph.json
- .obsidian/snippets/llm-wiki.css
- .obsidian/templates.json
- .windsurfrules
- 1-Projects/README.md
- 1-Projects/_SHIPPED/.gitkeep
- 1-Projects/_parked/README.md
- 2-Knowledge/Decisions/.gitkeep
- 2-Knowledge/HowTo/.gitkeep
- 2-Knowledge/README.md
- 2-Knowledge/References/.gitkeep
- 2-Knowledge/log.md
- 3-Journal/README.md
- 4-Private/README.md
- ATTRIBUTION.md
- CODE_OF_CONDUCT.md
- CONTRIBUTING.md
- LICENSE
- PHILOSOPHY.md
- README.md
- WIKI-LOG.md
- _config/config.md
- _config/context.md
- _config/standard.md
- _config/tools.md
- _inbox/README.md
- _meta/instructions/agent-write.md
- _meta/instructions/blog-posts/instructions.md
- _meta/instructions/definition-of-done.md
- _meta/instructions/general.md
- _meta/instructions/knowledge-lint.md
- _meta/instructions/knowledge-query.md
- _meta/instructions/optimization-review.md
- _meta/templates/blog-post.md
- _meta/templates/decision.md
- _meta/templates/inbox-capture.md
- _meta/templates/initiative.md
- _meta/templates/journal-entry.md
- _meta/templates/knowledge-article.md
- _meta/templates/meeting-notes.md
- _output/README.md
- _sources/.gitkeep
- _sources/README.md
- assets/.gitkeep
- assets/clipper-template.json
- docs/claude-code-setup.md
- docs/design-decisions.md
- docs/directory-structure.md
- docs/extending-and-contributing.md
- docs/faq.md
- docs/future-directions.md
- docs/journal-sync.md
- docs/linked-code-repos.md
- docs/llm-wiki-pattern.md
- docs/metadata-standard.md
- docs/obsidian-setup.md
- docs/supported-tools.md
- docs/training-phases.md
- examples/example-decision-record.md
- examples/example-inbox-capture.md
- examples/example-initiative.md
- examples/example-knowledge-article.md
- migrate-to-yaml-frontmatter.py
Advantages
- The system adapts to user patterns over a 30-day training period without requiring upfront taxonomy design. evidence: README.md
- Metadata standards on every document allow the AI to scan summaries and tags without loading full files, enabling scalability. evidence: README.md
- The inbox-first capture system allows for frictionless entry of information with a 7-day TTL for later filing. evidence: README.md
Limitations
- The template approach disconnects the user from upstream updates unless the Framework Track setup is used. evidence: README.md
Security
Risk level: No risk found
This brain runs
Agent instructions that run or fetch something (1)
.claude/CLAUDE.md:**Exception:** If the user's first message is a `/init` command or a meta question about the repo setup, complete that first — then run the Session Start Protocol on the next exchange.
Attribution and licence
- First committed by
asakin
- Publisher
- Ariel Sakin (Community)
- Source
- asakin/llm-context-base · commit 3c096ca
- Licence
- Apache License 2.0 (files served here)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
- Contributors
- asakin, claude
AutoGineer is not affiliated with or endorsed by asakin.
Download
The ZIP holds the files AutoGineer serves, with ATTRIBUTION.md and the licence. Download llm-context-base.zip