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

Add the marketplace to Claude Code:

/plugin marketplace add agenticnotetaking/arscontexta

Install the plugin:

/plugin install arscontexta@agenticnotetaking

Restart Claude Code, then run:
```
/arscontexta:setup
```
Answer 2-4 questions about your domain (~20 minutes -- token-intensive but one-time)
The engine generates your complete knowledge system
Restart Claude Code again to activate generated hooks and skills
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:

Phase	What Happens
Detection	Detects Claude Code environment and capabilities
Understanding	2-4 conversation turns where you describe your domain
Derivation	Maps signals to eight configuration dimensions with confidence scoring
Proposal	Shows what will be generated and why, in your vocabulary
Generation	Produces all files: context file, folders, templates, skills, hooks, manual
Validation	Checks all 15 kernel primitives, runs pipeline smoke test

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:

Space	Purpose	Growth
self/	Agent persistent mind -- identity, methodology, goals	Slow (tens of files)
notes/	Knowledge graph -- the reason the system exists	Steady (10-50/week)
ops/	Operational coordination -- queue state, sessions	Fluctuating

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

Commands

Plugin-Level (always available)

Command	What It Does
`/arscontexta:setup`	Conversational onboarding -- generates your full system
`/arscontexta:help`	Contextual guidance and command discovery
`/arscontexta:tutorial`	Interactive walkthrough (learn by doing)
`/arscontexta:ask`	Query the research graph for methodology answers
`/arscontexta:health`	Run diagnostic checks on your vault
`/arscontexta:recommend`	Get architecture advice for your use case
`/arscontexta:architect`	Research-backed evolution guidance
`/arscontexta:add-domain`	Add a new knowledge domain to an existing system
`/arscontexta:reseed`	Re-derive from first principles when drift accumulates
`/arscontexta:upgrade`	Apply plugin knowledge base updates to your system

Generated (available after setup)

Command	What It Does
`/reduce`	Extract insights from sources
`/reflect`	Find connections, update MOCs
`/reweave`	Update older notes with new connections
`/verify`	Combined quality check: description + schema + health
`/validate`	Schema compliance checking
`/seed`	Create extraction task with duplicate detection
`/ralph`	Queue-based orchestration with fresh context per phase
`/pipeline`	End-to-end source processing
`/tasks`	Queue management
`/stats`	Vault metrics
`/graph`	Graph analysis
`/next`	Next-action recommendation
`/learn`	Research and grow
`/remember`	Mine session learnings
`/rethink`	Challenge system assumptions
`/refactor`	Structural improvements

Processing Pipeline

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

Phase	What Happens	Command
Record	Zero-friction capture into inbox/	Manual
Reduce	Extract insights with domain-native categories	`/reduce`
Reflect	Find connections, update MOCs	`/reflect`
Reweave	Update older notes with new context	`/reweave`
Verify	Description + schema + health checks	`/verify`
Rethink	Challenge system assumptions	`/rethink`

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:

Hook	Event	What It Does
Session Orient	`SessionStart`	Injects workspace tree, loads identity, surfaces maintenance signals
Write Validate	`PostToolUse` (Write)	Schema enforcement on every note write
Auto Commit	`PostToolUse` (Write, async)	Git auto-commit, non-blocking
Session Capture	`Stop`	Persists session state to `ops/sessions/`

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

Dependency	Required	Purpose
Claude Code v1.0.33+	Yes	Plugin host
`tree`	Yes	Workspace structure injection
`ripgrep` (`rg`)	Yes	YAML queries, schema validation
qmd	Optional	Semantic search

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 connections
|   |-- reweave/                 # Backward pass
|   |-- verify/                  # Combined quality check
|   +-- ...                      # 12 more processing commands
|-- agents/
|   +-- knowledge-guide.md       # Pipeline subagent
|-- hooks/
|   |-- hooks.json               # Hook configuration
|   +-- scripts/                 # Hook implementations
|-- generators/
|   |-- claude-md.md             # CLAUDE.md template
|   +-- features/                # 17 composable feature blocks
|-- methodology/                 # 249 research claims
|-- reference/                   # Core reference documents
|   |-- kernel.yaml              # 15 kernel primitives
|   |-- three-spaces.md          # Architecture spec
|   +-- use-case-presets.md      # Pre-validated configs
|-- platforms/                   # Platform-specific adapters
|   |-- claude-code/
|   +-- shared/
|-- presets/                     # Pre-validated configurations
|-- scripts/                     # Utility scripts
+-- README.md

Development

Clone this repo and add the marketplace to Claude Code:

/plugin marketplace add ~/path-to-arscontexta

Install the plugin:

/plugin install arscontexta@agenticnotetaking

Every time you make changes, re-install the plugin:

/plugin uninstall arscontexta@agenticnotetaking
/plugin install arscontexta@agenticnotetaking

Key Files for Contributors

reference/kernel.yaml -- 15 primitives every system must include
generators/features/*.md -- composable feature blocks
skill-sources/*/SKILL.md -- generated command templates
skills/setup/SKILL.md -- the derivation engine
reference/use-case-presets.md -- preset definitions

Presets

Three pre-validated configurations for common use cases:

Preset	For	What You Get
Research	Academic work, literature reviews, synthesis	Atomic claims, citation tracking, methodology MOCs
Personal	Life management, journaling, relationships	Reflective notes, goal tracking, relationship MOCs
Experimental	Testing, iteration, rapid prototyping	Lightweight structure, fast capture, minimal ceremony

Presets provide starting defaults. The derivation engine adapts from there based on your conversation.

Roadmap

Feature	Status
Claude Code plugin	Available
Marketplace listing	Available
Multi-agent processing	In progress

Philosophy

The name connects to a tradition. Ars Combinatoria, Ars Memoria, Ars Contexta: the art of context.

Llull's rotating wheels generated truth through combination. Bruno's memory wheels created millions of image combinations. They were external thinking systems -- tools to think with rather than just store in. The missing piece: they required a human mind to do the traversing. Now LLMs can traverse. The wheels can spin again.

Built on Tools for Thought for Agents research.

License

MIT

Arscontexta

Verified AI-Agent Built

README