Ars Contexta

A second brain for your agent.

A Claude Code plugin that generates complete knowledge systems from conversation. You describe how you think and work. The engine derives a cognitive architecture -- folder structure, context files, processing pipeline, hooks, navigation maps, and note templates -- tailored to your domain and backed by 249 research claims.

No templates. No configuration. Just conversation.

v0.8.0 · Claude Code plugin · MIT

Installation

1. Add the marketplace to Claude Code:

   /plugin marketplace add agenticnotetaking/arscontexta
   

2. Install the plugin:

   /plugin install arscontexta@agenticnotetaking
   

3. Restart Claude Code, then run:

   /arscontexta:setup
   

4. Answer 2-4 questions about your domain (~20 minutes -- token-intensive but one-time)

5. The engine generates your complete knowledge system

6. Restart Claude Code again to activate generated hooks and skills

7. Run /arscontexta:help to see everything available

What It Does

Most AI tools start every session blank. Ars Contexta changes that by generating a persistent thinking system derived from how you actually work.

What you get:

• A vault -- plain markdown files connected by wiki links, forming a traversable knowledge graph. No database, no cloud, no lock-in.
• A processing pipeline -- skills that extract insights, find connections, update old notes with new context, and verify quality.
• Automation -- hooks that enforce structure on every write, detect maintenance needs, capture session state, and auto-commit.
• Navigation -- Maps of Content (MOCs) at hub, domain, and topic levels.
• Templates -- note templates with _schema blocks as single source of truth.
• A user manual -- 7 pages of domain-native documentation generated alongside.

The key differentiator: derivation, not templating. Every choice traces to specific research claims. The engine reasons from principles about what your domain needs and why.

The Setup Flow

/arscontexta:setup runs a 6-phase process:

The whole process takes about 20 minutes. It's token-intensive because the engine reads research claims, reasons about your domain, and generates substantial output. This is a one-time investment -- after setup, your agent remembers.

For advanced users: /arscontexta:setup --advanced to configure dimensions directly.

Three-Space Architecture

Every generated system separates content into three spaces:

Names adapt to your domain (notes/ might become reflections/, claims/, or decisions/), but the separation is invariant.

Commands

Plugin-Level (always available)

Generated (available after setup)

Processing Pipeline

The vault implements the 6 Rs, extending Cornell Note-Taking's 5 Rs with a meta-cognitive layer:

Fresh Context Per Phase

Each phase runs in its own context window via subagent spawning. LLM attention degrades as context fills. By spawning a fresh subagent per phase, every phase operates in the "smart zone."

/ralph 5
  |-- Read queue, find next unblocked task
  |-- Spawn subagent (fresh context)
  |   +-- Runs skill, updates task file, returns handoff
  |-- Parse handoff, capture learnings
  |-- Advance phase in queue
  +-- Repeat for 5 tasks

Hooks

Four hooks automate quality enforcement:

The Research Graph

The methodology/ directory contains 249 interconnected research claims about tools for thought, knowledge management, and agent-native cognitive architecture. These claims back every configuration decision.

Synthesizes

Zettelkasten -- Cornell Note-Taking -- Evergreen Notes -- PARA -- GTD -- Memory Palaces -- Cognitive Science (extended mind, spreading activation, generation effect) -- Network Theory (small-world topology, betweenness centrality) -- Agent Architecture (context windows, session boundaries, multi-agent patterns)

How Claims Back Decisions

Every kernel primitive includes cognitive_grounding linking to specific research:

• MOC hierarchy -- context-switching cost research (Leroy 2009)
• Description field -- progressive disclosure principles
• Wiki links -- spreading activation theory

Query directly: /arscontexta:ask "Why does my system use atomic notes?"

Semantic Search (optional)

qmd adds concept matching across vocabularies. Not required -- the system works fully with ripgrep + MOC traversal.

/setup should perform this configuration automatically when semantic search is active. The commands below are manual fallback/setup verification.

# Install qmd
npm install -g @tobilu/qmd
# or
bun install -g @tobilu/qmd

cd your-vault/
qmd init
qmd collection add . --name <notes_directory_name> --mask "<notes_directory_name>/**/*.md"
qmd embed

Create or merge .mcp.json in the vault root:

{
  "mcpServers": {
    "qmd": {
      "command": "qmd",
      "args": ["mcp"],
      "autoapprove": [
        "mcp__qmd__search",
        "mcp__qmd__vector_search",
        "mcp__qmd__deep_search",
        "mcp__qmd__get",
        "mcp__qmd__multi_get",
        "mcp__qmd__status"
      ]
    }
  }
}

Keep qmd MCP configuration and tool preapproval in .mcp.json.

Prerequisites

Project Structure

arscontexta/
|-- .claude-plugin/
|   |-- plugin.json              # Plugin manifest
|   +-- marketplace.json         # Marketplace listing
|-- skills/                      # 10 plugin-level commands
|   |-- setup/                   # Conversational onboarding
|   |-- help/                    # Contextual guidance
|   |-- tutorial/                # Interactive walkthrough
|   |-- ask/                     # Query the research graph
|   |-- health/                  # Diagnostic checks
|   |-- recommend/               # Architecture advice
|   |-- architect/               # Evolution guidance
|   |-- reseed/                  # Re-derive from first principles
|   |-- upgrade/                 # Apply knowledge base updates
|   +-- add-domain/              # Multi-domain extension
|-- skill-sources/               # 16 generated command templates
|   |-- reduce/                  # Extract insights
|   |-- reflect/                 # Find