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ClaudeWave
Skill3.4k estrellas del repoactualizado 3mo ago

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.

Instalar en Claude Code
Copiar
git clone --depth 1 https://github.com/agenticnotetaking/arscontexta /tmp/setup && cp -r /tmp/setup/skills/setup ~/.claude/skills/setup
Después abre una sesión nueva de Claude Code; el skill carga automáticamente.

SKILL.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, desc
knowledge-guideSubagent

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.

graphSkill

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".

learnSkill

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".

nextSkill

Surface the most valuable next action by combining task stack, queue state, inbox pressure, health, and goals. Recommends one specific action with rationale. Triggers on "/next", "what should I do", "what's next".

pipelineSkill

End-to-end source processing -- seed, reduce, process all claims through reflect/reweave/verify, archive. The full pipeline in one command. Triggers on "/pipeline", "/pipeline [file]", "process this end to end", "full pipeline".

ralphSkill

Queue processing with fresh context per phase. Processes N tasks from the queue, spawning isolated subagents to prevent context contamination. Supports serial, parallel, batch filter, and dry run modes. Triggers on "/ralph", "/ralph N", "process queue", "run pipeline tasks".

reduceSkill

Extract structured knowledge from source material. Comprehensive extraction is the default — every insight that serves the domain gets extracted. For domain-relevant sources, skip rate must be below 10%. Zero extraction from a domain-relevant source is a BUG. Triggers on "/reduce", "/reduce [file]", "extract insights", "mine this", "process this".

refactorSkill

Plan vault restructuring from config changes. Compares config.yaml against derivation.md, identifies dimension shifts, shows restructuring plan, executes on approval. Triggers on "/refactor", "restructure vault".