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

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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

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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.

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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.

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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.

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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

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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