LLM Knowledge Bases

LLM wiki kit

First committed by rvk7895

Published in rvk7895/llm-knowledge-bases. Publisher page

35 GitHub starsUpdated 2026-06-16

What it is

Claude Code plugin for building LLM-maintained Obsidian wikis from raw research — compile, query, lint, and evolve your personal knowledge base. Inspired by Karpathy's knowledge base workflow.

— the description by rvk7895

AutoGineer overview, from the brain’s own files

LLM Knowledge Bases is a plugin for Claude Code designed to automate the creation and maintenance of a personal knowledge base in Obsidian. By treating the LLM as a knowledge compiler, the system ingests raw materials—such as articles, papers, and transcripts—and organizes them into a structured, interlinked wiki. Users interact with the system by feeding raw data into a designated folder, while the agent handles the compilation, indexing, and health maintenance of the vault. It supports multiple query depths, ranging from quick index lookups to deep, multi-agent research pipelines, ensuring that the knowledge base evolves alongside the user's research interests.

How it works

Memory model: LLM wiki

AutoGineer overview, from the brain’s own files

The system operates by ingesting raw source documents from a raw folder. It then compiles these into an Obsidian vault containing summaries, backlinks, and indexes. Users can query the knowledge base at three levels of depth: quick, standard, or deep. The system also performs automated maintenance tasks like linting for broken links or orphans and provides tools for deep research, which involves generating outlines and deploying parallel agents to research specific topics. All outputs are saved to an output folder or integrated back into the wiki.

Folders
Folder	Role	Files
.claude	agent-config	1
docs	docs	2
.claude-plugin	folder	1
plugins	folder	12

Requirements

Works with: Claude Code, OpenCode

Set it up

This brain runs code automatically: 1 script. Review plugins/kb/setup.sh before opening the folder in Claude Code. No step below turns a hook on for you.
- plugins/kb/setup.sh: sh plugins/kb/setup.sh
Download the brain as a ZIP and unzip it where you keep your notes or project: https://mp-staging.autogineer.com/agent-brains/rvk7895/llm-knowledge-bases/download
Claude Code reads CLAUDE.md from this folder automatically.
cd llm-knowledge-bases && claude
AutoGineer scanned these files at commit 342686f.
Not scanned by AutoGineerThe author’s README also describes installing it as a Claude Code plugin marketplace. Read its install section: AutoGineer did not scan that path. https://github.com/rvk7895/llm-knowledge-bases/blob/342686f4cdf0e969872601eabbcc10466da91431/README.md#installation
First committed by rvk7895. Published in rvk7895/llm-knowledge-bases. Licence: MIT License.

Usage

AutoGineer overview, from the brain’s own files

Initialize a new knowledge base structure
Compile raw research documents into an Obsidian wiki
Query the knowledge base for information at varying depths
Perform automated health checks on the wiki
Execute deep research on specific topics using multi-agent pipelines

“/kb compile”: The agent processes all files in the raw/ directory and updates the Obsidian wiki with new summaries and backlinks.
“/research-deep Artificial Intelligence trends”: The agent generates a research outline and launches parallel agents to conduct deep research on each item in the outline.
“/kb lint”: The agent scans the wiki for broken links, orphans, and inconsistencies, reporting them for resolution.

Documentation by rvk7895

LLM Knowledge Bases

A Claude Code plugin that turns raw research material into an LLM-maintained Obsidian wiki -- inspired by Andrej Karpathy's description of using LLMs as knowledge compilers rather than just code manipulators.

You drop source material into raw/, run a single command, and Claude handles the rest: compiling interlinked wiki articles, maintaining indexes and backlinks, answering complex questions at multiple depth levels, and continuously improving the knowledge base over time.

The LLM owns the wiki. You rarely edit it manually -- just explore in Obsidian and keep feeding it raw data.

How It Works

Ingest -- Raw documents (articles, papers, repos, YouTube transcripts, images, datasets) go into raw/
Compile -- Claude builds a structured Obsidian vault with summaries, backlinks, concept articles, and auto-generated indexes
Query -- Three depth levels:
- Quick -- Answers from wiki indexes and summaries alone
- Standard -- Cross-references the full wiki, supplements with web search
- Deep -- Multi-agent research pipeline with parallel web search agents
Output -- Markdown reports, Marp slides, matplotlib charts -- saved to output/ and optionally filed back into the wiki
Maintain -- Automated health checks (broken links, orphans, inconsistencies) and suggestions for new articles and connections

Prerequisites

Claude Code (CLI), installed and authenticated
Obsidian

Obsidian plugins (Dataview, Obsidian Git, etc.) are installed automatically by the setup script during /kb-init.

Installation

In Claude Code, run:

/plugin marketplace add rvk7895/llm-knowledge-bases
/plugin install kb@llm-knowledge-bases

All skills are installed as a single plugin.

Quick Start

# Initialize a new knowledge base
/kb-init

# Add sources to raw/, then compile the wiki
/kb compile

# Query the knowledge base
/kb query

# Run a health check
/kb lint

# Deep multi-agent research on a topic
/research <topic>
/research-deep

Available Skills

Skill	Purpose
`kb-init`	One-time setup: scaffolds directories, generates config, writes project files
`kb`	Main skill with four workflows: compile, query, lint, evolve
`research`	Generate a structured research outline for a topic
`research-deep`	Launch parallel agents for deep research on each outline item
`research-add-fields`	Add field definitions to an existing research outline
`research-add-items`	Add research targets to an existing outline
`research-report`	Compile deep research results into a markdown report

Directory Structure (after `/kb-init`)

raw/           -- Raw source documents
wiki/          -- LLM-compiled Obsidian vault
output/        -- Query results, slides, charts, reports
kb.yaml        -- Configuration
CLAUDE.md      -- Project instructions for Claude

X/Twitter Integration (Optional)

To ingest tweets, threads, and bookmarks from X, install Smaug:

npm install -g @steipete/bird
git clone https://github.com/alexknowshtml/smaug && cd smaug && npm install
npx smaug setup  # configures X session cookies

Once configured, paste any x.com link and the kb skill will fetch and compile it automatically. Without Smaug, you can still add X content by pasting tweet text directly or using Thread Reader App for threads.

Note: Smaug uses X session cookies for read-only access to your own data. This is not officially supported by X's TOS. Practical risk for personal use is very low, but be aware.

Attribution

Andrej Karpathy -- Original vision for LLM-maintained knowledge bases
Weizhena -- Deep Research skills adapted for the research pipeline

plugins/kb/skills/research/SKILL.md

user-invocable: true allowed-tools: Read, Write, Glob, WebSearch, Task, AskUserQuestion description: Conduct preliminary research on a target topic and generate a research outline. Used for academic research, benchmark research, technology selection, and similar scenarios.

Attribution: Originally authored by Weizhena. Included with attribution for use in the Deep query workflow.

Research Skill - Preliminary Research

Trigger Method

/research <topic>

Execution Flow

Step 1: Generate Initial Framework Using Model's Internal Knowledge

Based on the topic, use the model's existing knowledge to generate:

A list of main research objects/items in the field
A suggested framework of research fields

Output {step1_output} and use AskUserQuestion to confirm:

Does the items list need additions or removals?
Does the field framework meet the requirements?

Step 2: Web Search Supplement

Use AskUserQuestion to inquire about the time range (e.g., last 6 months, 2024 to present, no limit).

Parameter Collection:

{topic}: Research topic input by user
{YYYY-MM-DD}: Current date
{step1_output}: Complete output content generated in Step 1
{time_range}: Time range specified by user

Hard Constraint: The following prompt must be strictly reproduced, only replacing variables in {xxx}, no rewriting of structure or wording allowed.

Launch 1 web-search-agent (background), Prompt Template:

prompt = f"""## Task
Research Topic: {topic}
Current Date: {YYYY-MM-DD}

Based on the following preliminary framework, supplement the latest items and recommended research fields.

## Existing Framework
{step1_output}

## Objectives
1. Verify if existing items miss important objects
2. Supplement items based on missing objects
3. Continue searching for {topic}-related items within {time_range} and supplement
4. Supplement new fields

## Output Requirements
Return structured results directly (do not write files):

### Supplemented Items
- item_name: Brief explanation (why should it be included)
...

### Recommended Additional Fields
- field_name: Field description (why this dimension is needed)
...

### Information Sources
- [Source 1](url1)
- [Source 2](url2)
"""

One-shot Example (assuming research on AI Coding development history):

## Task
Research Topic: AI Coding Development History
Current Date: 2025-12-30

Based on the following preliminary framework, supplement the latest items and recommended research fields.

## Existing Framework
### Items List
1. GitHub Copilot: Developed by Microsoft/GitHub, first mainstream AI programming assistant
2. Cursor: AI-first IDE, based on VSCode
...

### Field Framework
- Basic Info: name, release_date, company
- Technical Features: underlying_model, context_window
...

## Objectives
1. Verify if existing items miss important objects
2. Supplement items based on missing objects
3. Continue searching for AI Coding Development History-related items within 2024 to present and supplement
4. Supplement new fields

## Output Requirements
Return structured results directly (do not write files):

### Supplemented Items
- item_name: Brief explanation (why should it be included)
...

### Recommended Additional Fields
- field_name: Field description (why this dimension is needed)
...

### Information Sources
- [Source 1](url1)
- [Source 2](url2)

Step 3: Inquire About User's Existing Fields

Use AskUserQuestion to ask if the user has predefined field files, read and merge if available.

Step 4: Generate Outline (Separate Files)

Merge {step1_output}, {step2_output} and user's existing fields to generate two files:

outline.yaml (items + configuration):

topic: Research topic
items: List of research objects
execution:
- batch_size: Number of parallel agents (requires AskUserQuestion confirmation)
- items_per_agent: Number of items per agent for research (requires AskUserQuestion confirmation)
- output_dir: Results output directory (default ./results)

fields.yaml (field definitions):

Field categories and definitions
Each field's name, description, detail_level
detail_level hierarchy: Minimal → Brief → Detailed
uncertain: List of uncertain fields (preserved fields, automatically filled in deep phase)

Step 5: Output and Confirmation

Create directory: ./{topic_slug}/
Save: outline.yaml and fields.yaml
Display to user for confirmation

Output Path

{current_working_directory}/{topic_slug}/
  ├── outline.yaml    # items list + execution configuration
  └── fields.yaml     # field definitions

Follow-up Commands

/research-add-items - Supplement items
/research-add-fields - Supplement fields
/research-deep - Start deep research

21 files

Advantages

Automates the maintenance of interlinked wiki articles and indexes, reducing manual effort. evidence: README.md
Supports multi-agent research pipelines for deep, parallel investigation of topics. evidence: README.md

Limitations

Requires Claude Code CLI and Obsidian to function. evidence: README.md
The LLM owns the wiki, and manual editing of articles outside of the provided workflows is discouraged. evidence: CLAUDE.md

Security

Risk level: Medium risk

This brain runs

Install scripts (1)

plugins/kb/setup.sh: sh plugins/kb/setup.sh · uses the network

Agent instructions that run or fetch something (44)

plugins/kb/skills/kb-init/SKILL.md: After confirming the vault path, run the bundled setup script via Bash tool:
plugins/kb/skills/kb-init/SKILL.md: The script is idempotent — safe to re-run.
plugins/kb/skills/kb-init/SKILL.md: python -c "import pptx" 2>/dev/null && echo "python-pptx: available" || echo "python-pptx: not installed (needed for slide extraction)"
plugins/kb/skills/kb-init/SKILL.md: python -c "import pandas" 2>/dev/null && echo "pandas: available" || echo "pandas: not installed (needed for dataset analysis)"
plugins/kb/skills/kb-init/SKILL.md: When the user sets up Smaug (during init or later), save the install path here.
plugins/kb/skills/kb-preferences/SKILL.md: If missing, tell the user to run `kb-init` and stop.
plugins/kb/skills/kb-preferences/SKILL.md: | Invocation | Mode | Action | |---|---|---| | `/kb-preferences` (bare) | interactive | Show current prefs, ask what to update | | `/kb-preferences init` | init | Re-run the 5 init questions, regenerate all fields from style-guide + user answers | | `/kb-preferences update <field>` | up …[+30 chars]
plugins/kb/skills/kb-preferences/SKILL.md: re-ask matching question or accept free-text | | `/kb-preferences add-note "..."` | add-note | Append the quoted string as a new line/paragraph under `notes:` | | `/kb-preferences reflect` | reflect-now | Run the reflection step against the current conversation explicitly | | `/kb-pref …[+164 chars]
plugins/kb/skills/kb-preferences/SKILL.md: If missing, tell user to run `/kb-preferences init` first and stop.
plugins/kb/skills/kb-preferences/SKILL.md: Confirm loudly: "This will replace all 9 fields with factory defaults and re-run the 5 init questions.
plugins/kb/skills/kb/SKILL.md: If missing, tell the user to run `kb-init` and stop.
plugins/kb/skills/kb/SKILL.md: Run `/kb-preferences init` to customize."
plugins/kb/skills/kb/SKILL.md: After a compile or query finishes writing its output, run a **lightweight reflection step** — only propose preference updates for **high-confidence signals**:
plugins/kb/skills/kb/SKILL.md: The user can run `/kb-preferences reflect` manually if they want a forced pass.
plugins/kb/skills/kb/SKILL.md: Run lightweight reflection at the end.
plugins/kb/skills/kb/SKILL.md: **Detection heuristic:** Run this check for each file:
plugins/kb/skills/kb/SKILL.md: Please provide a transcript, or install: pip install openai-whisper"
plugins/kb/skills/kb/SKILL.md: Try: `yt-dlp --write-auto-sub --sub-lang en --skip-download --output "/tmp/%(id)s" "<url>"` to get subtitles · uses the network
plugins/kb/skills/kb/SKILL.md: Try extracting with python-pptx: `python -c "from pptx import Presentation;
plugins/kb/skills/kb/SKILL.md: For Parquet: `python -c "import pandas as pd;
plugins/kb/skills/kb/SKILL.md: Run lightweight reflection at the end (after the answer is delivered;
plugins/kb/skills/kb/SKILL.md: Run the appropriate pipeline.
plugins/kb/skills/kb/SKILL.md: Run the graph health script if available:
plugins/kb/skills/kb/SKILL.md: python plugins/kb/scripts/graph_health.py "<vault_path>" --json --fix-suggestions
plugins/kb/skills/kb/SKILL.md: Run the evaluation script if available:
plugins/kb/skills/kb/SKILL.md: python experiments/evaluate.py "<vault_path>" --json
plugins/kb/skills/kb/SKILL.md: If user approves, execute the improvements
plugins/kb/skills/kb/SKILL.md: Re-run evaluation to measure improvement
plugins/kb/skills/kb/SKILL.md: Run: `npx smaug fetch <tweet_id>` then `npx smaug process`
plugins/kb/skills/kb/SKILL.md: **Install Smaug** (recommended) — `git clone https://github.com/alexknowshtml/smaug && cd smaug && npm install`, then `npx smaug setup` to configure X session cookies (`auth_token` + `ct0` from browser DevTools → Cookies → x.com). · uses the network
plugins/kb/skills/kb/SKILL.md: After setup, **save the install path to `kb.yaml`** under `integrations.smaug.path`.
plugins/kb/skills/kb/SKILL.md: Try to fetch transcript: `yt-dlp --write-auto-sub --sub-lang en --skip-download --output "/tmp/%(id)s" "<url>" 2>&1` · uses the network
plugins/kb/skills/kb/SKILL.md: If yt-dlp not installed: try `pip install yt-dlp` then retry
plugins/kb/skills/kb/SKILL.md: Extract video metadata: `yt-dlp --dump-json "<url>" 2>/dev/null | python -c "import sys,json; · uses the network
plugins/kb/skills/kb/SKILL.md: On the next `/kb compile` run (for compile-time context searches)
plugins/kb/skills/kb/SKILL.md: Forgetting to dispatch the auto-evolve subagent after a query -- it must always run, even if it usually exits silently
plugins/kb/skills/research-add-items/SKILL.md: Execute simultaneously:
plugins/kb/skills/research-deep/SKILL.md: Execute in batches by batch_size (need user approval to proceed to next batch after completing one)
plugins/kb/skills/research-deep/SKILL.md: After completing JSON output, run validation script to ensure complete field coverage:
plugins/kb/skills/research-deep/SKILL.md: python ~/.claude/skills/research/validate_json.py -f {fields_path} -j {output_path}
plugins/kb/skills/research-deep/SKILL.md: After completing JSON output, run validation script to ensure complete field coverage:
plugins/kb/skills/research-deep/SKILL.md: python ~/.claude/skills/research/validate_json.py -f /home/weizhena/AIcoding/aicoding-history/fields.yaml -j /home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json
plugins/kb/skills/research-report/SKILL.md: **If `kb.yaml` itself is missing:** tell the user to run `kb-init` and stop.
plugins/kb/skills/research-report/SKILL.md: Run `python {topic}/generate_report.py`

Medium risk: runs-install-script-network-variable-host (plugins/kb/setup.sh)
Medium risk: network-variable-host (plugins/kb/setup.sh:81)
Medium risk: runs-instruction-instruction-network (plugins/kb/skills/kb/SKILL.md:206)
Medium risk: runs-instruction-instruction-network (plugins/kb/skills/kb/SKILL.md:571)
Medium risk: runs-instruction-instruction-network (plugins/kb/skills/kb/SKILL.md:596)
Medium risk: runs-instruction-instruction-network (plugins/kb/skills/kb/SKILL.md:600)
Low risk: network-instructions (.claude/settings.local.json:14)
Low risk: scripts-present (plugins/kb/setup.sh)
Low risk: network-instructions (plugins/kb/skills/kb/SKILL.md:202)
Low risk: autonomy-phrase (plugins/kb/skills/kb/SKILL.md:402)

Attribution and licence

First committed by

rvk7895

Publisher

Ritvik Aryan Kalra (Community)

Source

rvk7895/llm-knowledge-bases · commit 342686f

Licence

MIT License (files served here)

ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-12

Contributors

ritvik-sarvam, rvk7895

AutoGineer is not affiliated with or endorsed by rvk7895.

Download

The ZIP holds the files AutoGineer serves, with ATTRIBUTION.md and the licence. Download llm-knowledge-bases.zip

What it is

How it works

Requirements

Set it up

Usage

Documentation by rvk7895

LLM Knowledge Bases

How It Works

Prerequisites

Installation

Quick Start

Available Skills

Directory Structure (after /kb-init)

X/Twitter Integration (Optional)

Attribution

user-invocable: true allowed-tools: Read, Write, Glob, WebSearch, Task, AskUserQuestion description: Conduct preliminary research on a target topic and generate a research outline. Used for academic research, benchmark research, technology selection, and similar scenarios.

Research Skill - Preliminary Research

Trigger Method

Execution Flow

Step 1: Generate Initial Framework Using Model's Internal Knowledge

Step 2: Web Search Supplement

Step 3: Inquire About User's Existing Fields

Step 4: Generate Outline (Separate Files)

Step 5: Output and Confirmation

Output Path

Follow-up Commands

Advantages

Limitations

Security

This brain runs

Attribution and licence

Download

Directory Structure (after `/kb-init`)