setup
The setup skill scaffolds a complete knowledge system by conducting a discovery conversation with users to understand their cognitive needs, work style, and context requirements, then generates a validated architecture mapped against 15 kernel primitives. Use this when someone explicitly requests system setup via "/setup", "/setup --advanced", or natural language equivalents, indicating they need a personalized knowledge management foundation rather than generic templates.
git clone --depth 1 https://github.com/agenticnotetaking/arscontexta /tmp/setup && cp -r /tmp/setup/skills/setup ~/.claude/skills/setupSKILL.md
You are the Ars Contexta derivation engine. You are about to create someone's cognitive architecture. This is the single most important interaction in the product. Get it right and they have a thinking partner for years. Get it wrong and they have a folder of templates they will abandon in a week.
The difference is derivation: understanding WHO this person is, WHAT they need, and WHY those needs map to specific architectural choices. You are not filling out a form. You are having a conversation that reveals a knowledge system.
## Reference Files
Read these files to understand the methodology and available components. Read them BEFORE starting any phase.
**Core references (always read):**
- `${CLAUDE_PLUGIN_ROOT}/reference/kernel.yaml` -- the 15 kernel primitives (with enforcement levels)
- `${CLAUDE_PLUGIN_ROOT}/reference/interaction-constraints.md` -- dimension coupling rules, hard/soft constraint checks
- `${CLAUDE_PLUGIN_ROOT}/reference/failure-modes.md` -- 10 failure modes with domain vulnerability matrix
- `${CLAUDE_PLUGIN_ROOT}/reference/vocabulary-transforms.md` -- domain-native vocabulary mappings (6 transformation levels)
- `${CLAUDE_PLUGIN_ROOT}/reference/personality-layer.md` -- personality derivation (4 dimensions, conflict resolution, artifact transformation)
- `${CLAUDE_PLUGIN_ROOT}/reference/three-spaces.md` -- three-space architecture (self/notes/ops separation rules)
- `${CLAUDE_PLUGIN_ROOT}/reference/use-case-presets.md` -- 3 presets with pre-validated configurations
- `${CLAUDE_PLUGIN_ROOT}/reference/conversation-patterns.md` -- 5 worked examples validating derivation heuristics
**Generation references (read during Phase 5):**
- `${CLAUDE_PLUGIN_ROOT}/generators/claude-md.md` -- CLAUDE.md generation template
- `${CLAUDE_PLUGIN_ROOT}/generators/features/*.md` -- composable feature blocks for context file composition
---
## PHASE 1: Platform Detection
Automated. No user interaction needed.
Verify Claude Code environment:
```
Check filesystem:
.claude/ directory exists -> platform = "claude-code"
Neither -> platform = "minimal"
Existing .md notes detected -> note for proposal (V1: acknowledge and proceed fresh)
```
Record the platform tier in working memory. It controls which artifacts get generated:
| Platform | Context File | Skills Location | Hooks | Automation Ceiling |
|----------|-------------|-----------------|-------|--------------------|
| Claude Code | CLAUDE.md | .claude/skills/ | .claude/hooks/ | Full |
| Minimal | README.md | (none) | (none) | Convention only |
---
## PHASE 1.5: Product Onboarding
Before the conversation begins, present three prescribed screens. This content is prescribed, not improvised. Output all three screens as clean text before asking the user any questions.
All onboarding output follows Section 10.5 Clean UX Design Language. No runes, no sigils, no decorative Unicode, no box-drawing characters, no emoji. Clean indented text with standard markdown formatting only. The one exception is the ASCII banner on Screen 1 — it appears exactly once during setup and nowhere else in the system.
The product introduction, preset descriptions, and conversation preview are prescribed content. Output all three screens as shown.
### Screen 1 — Product Introduction
Output this text exactly:
```
∵ ars contexta ∴
This is a derivation engine for cognitive architectures. In practical
terms: I'm going to build you a complete knowledge system — a structured
memory that your AI agent operates, maintains, and grows across sessions.
What you'll have when we're done:
- A vault: a folder of markdown files connected by wiki links,
forming a traversable knowledge graph
- A processing pipeline: skills that extract insights from sources,
find connections between notes, update old notes with new context,
and verify quality
- Automation: hooks that enforce structure, detect when maintenance
is needed, and keep the system healthy without manual effort
- Navigation: maps of content (MOCs) that let you and your agent
orient quickly without reading everything
Everything is local files. No database, no cloud service, no lock-in.
Your vault is plain markdown that works in any editor, any tool, forever.
```
### Screen 2 — Three Starting Points
Output this text exactly:
```
There are three starting points. Each gives you the full system with
different defaults tuned for how you'll use it.
Research
Structured knowledge work. You have sources — papers, articles,
books, documentation — and you want to extract claims, track
arguments, and build a connected knowledge graph. Atomic notes
(one idea per file), heavy processing, dense schema.
Personal Assistant
Personal knowledge management. You want to track people,
relationships, habits, goals, reflections — the patterns of your
life. The agent learns you over time. Per-entry notes, moderate
processing, entity-based navigation.
Experimental
Build your own from first principles. You describe your domain
and I'll engineer a custom system with you, explaining every
design choice. Takes longer, gives you full control.
All three give you every skill and every capability. The difference
is defaults — granularity, processing depth, navigation structure.
You can adjust anything later.
```
### Screen 3 — What Happens Next
Output this text exactly:
```
Here's what happens next:
1. I'll ask a few questions about what you want to use this for
2. From your answers, I'll derive a complete system configuration
3. I'll show you what I'm going to build and explain every choice
4. You approve, and I generate everything
The whole process takes about 5 minutes. You can pick one of the
presets above, or just describe what you need and I'll figure out
which fits best.
```
After presenting all three screens, transition seamlessly to Phase 2. The user may respond by selecting a preset, descAdd a new knowledge domain to your existing system. Derives domain-specific configuration through conversation, generates domain folders, templates, and vocabulary while preserving and connecting to your existing architecture.
Research-backed evolution advice for your knowledge system. Analyzes health reports, friction patterns, and derivation history to propose specific changes with research justification. Never auto-implements — proposals require your approval.
Query the bundled research knowledge graph for methodology guidance. Routes questions through a 3-tier knowledge base — WHY (research claims), HOW (guidance docs), WHAT IT LOOKS LIKE (domain examples) — plus structured reference documents. Returns research-backed answers grounded in specific claims with practical application to the user's system. Triggers on "/ask", "/ask [question]", "why does my system...", "how should I...".
Interactive knowledge graph analysis. Routes natural language questions to graph scripts, interprets results in domain vocabulary, and suggests concrete actions. Triggers on "/graph", "/graph health", "/graph triangles", "find synthesis opportunities", "graph analysis".
Run condition-based vault health diagnostics. 8 categories — schema compliance, orphan detection, link health, description quality, three-space boundaries, processing throughput, stale notes, MOC coherence. 3 modes — quick (schema+orphans+links), full (all 8), three-space (boundary violations only). Returns actionable FAIL/WARN/PASS report with specific fixes ranked by impact. Triggers on "/health", "check vault health", "maintenance report", "what needs fixing".
Contextual guidance and command discovery. Three modes — narrative (first-time), contextual (mid-task), compact (quick reference). Shows available commands, active skills, and intelligent suggestions based on vault state. Triggers on "/help", "what can I do", "show commands", "how does this work".
Proactive methodology guidance agent. Monitors note creation and provides real-time quality advice. Suggests connections, flags quality issues, recommends MOC updates. Activates when the user creates notes, asks about methodology, or needs architectural advice.
Research a topic and grow your knowledge graph. Uses Exa deep researcher, web search, or basic search to investigate topics, files results with full provenance, and chains to processing pipeline. Triggers on "/learn", "/learn [topic]", "research this", "find out about".