Indigenous AI Domain-Specific Language Server

AI Studio Scaffolding Package

This package contains everything AI Studio needs to fully generate a working Claude Code LSP plugin that makes your Indigenous AI research framework semantically legible.

---

What You'll Have When This Is Done

A Claude Code plugin that runs in your terminal alongside Claude Code. When you're writing documentation, code comments, or having conversations about your work:

• Instant definitions - Hover over or query "creative orientation" and instantly get full definition + relational context
• Smart lookups - "findReferences" shows everywhere this concept appears in your docs
• Relational navigation - "What's the relationship between structural tension and creative orientation?" answered immediately with context
• Indigenous knowledge integration - Every concept operation includes Two-Eyed Seeing, polycentric relations, relational knowledge, and ceremonial context
• Real-time diagnostics - Server flags terminology inconsistencies and suggests framework-aligned language
• Semantic coherence - Framework becomes first-class citizen in your development environment

Performance: 50ms response times instead of searching past conversations (45 seconds).

---

Package Contents

1. initial_prompt.md

What it is: The complete project vision and requirements for AI Studio.

Includes:

• Project purpose and vision
• Core concepts to implement
• Technical architecture overview
• Success criteria

For AI Studio: Use this as your primary input prompt. It tells the implementation agent exactly what to build.

---

2. rispecs.md

What it is: Detailed RISE specifications for five major components.

Components specified:

1. Concept Registry Module - Data structure that stores all framework concepts, definitions, and relationships
2. LSP Operations Handler - How to adapt standard LSP operations (goToDefinition, hover, findReferences, etc.) for concepts instead of code
3. Relational Graph Engine - Knowledge graph showing how concepts relate to each other
4. Indigenous Knowledge Mapper - Ensures all concepts are embedded in Indigenous knowledge systems (Two-Eyed Seeing, polycentric, relational, ceremonial)
5. Plugin Configuration & Lifecycle - How the plugin installs, starts, and manages LSP server lifecycle

For AI Studio: These specs define the module architecture and success metrics. Implementation agent uses these to build the system.

---

3. concept-registry.yaml

What it is: The semantic foundation - all core concepts, definitions, and relationships in YAML format (human-readable and editable).

Contains:

• Creative Orientation Framework: creative-orientation, structural-tension, current-reality, desired-state
• Eight Feelings Framework: All eight emotional/developmental capacities (Trust, Autonomy, Initiative, Accomplishment, Identity, Intimacy, Generativity, Integrity) plus relational healing
• Indigenous Knowledge Systems: Two-Eyed Seeing, polycentric relations, relational knowledge, ceremonial context, land responsibility, seven generations principle, reciprocity, emergence

Each concept includes:

• Clear definition
• Related concepts
• Four Indigenous knowledge system connections (Two-Eyed Seeing, polycentric, relational, ceremonial)
• Usage contexts and examples
• Source references

For AI Studio: This is the semantic content the server will serve. The implementation agent will:

• Convert to JSON for efficient server loading
• Index concepts for fast lookups
• Build relational graph from "relatedConcepts" mappings
• Create LSP diagnostic rules based on Indigenous knowledge mappings

---

How to Use This with AI Studio

Step 1: Create New Project

In Google AI Studio:

• Project name: "Indigenous AI DSL Server"
• Description: "Claude Code LSP plugin providing semantic understanding of Indigenous AI research framework"

Step 2: Provide Initial Prompt

Copy the contents of initial_prompt.md and paste into AI Studio as your project prompt.

Step 3: Attach Supporting Materials

Upload or reference:

• rispecs.md - Detailed specifications
• concept-registry.yaml - The semantic content

Step 4: Run Implementation

AI Studio's agent will:

1. Parse your requirements
2. Design the Python LSP server architecture
3. Implement the concept registry (YAML → JSON)
4. Build LSP operations handlers (goToDefinition, hover, getDiagnostics, etc.)
5. Create the relational graph engine
6. Generate Claude Code plugin configuration (plugin.json, .lsp.json)
7. Build lifecycle management (hooks for auto-installation, error handling)
8. Provide complete, runnable code

Step 5: Install & Use

Generated code will include:

• Python package (indigenous_ai_dsl_server/)
• Configuration files (.lsp.json, plugin.json)
• Registry files (concept-registry.json)
• Setup/installation scripts
• Documentation for manual testing

You'll install via:

/plugin install indigenous-ai-dsl@marketplace

Then it runs automatically when you open Claude Code.

---

Key Features AI Studio Will Generate

Core LSP Operations (Language Server Protocol)

1. goToDefinition - Jump to concept definition
2. hover - Show concept info on hover
3. findReferences - Find all uses of a concept
4. documentSymbol - View concept hierarchy
5. getDiagnostics - Real-time terminology validation

Smart Lookups

• Symbol resolution for 25+ core concepts
• Relationship traversal (show connected concepts)
• Indigenous knowledge system context on every query
• <100ms response time guarantee

File Format Support

• Markdown (.md) - documentation, specs, writings
• Plain text (.txt) - notes, logs
• Python (.py) - code, comments
• JavaScript (.js) - comments, documentation
• Extensible to other formats

Relational Understanding

• Shows how concepts relate (enables, contextualizes, builds on, contrasts with)
• Validates concept usage for coherence
• Suggests framework-aligned language
• Prevents terminology drift

---

Technical Details (For Your Reference)

Architecture

Claude Code (Terminal)
    ↓
LSP Client
    ↓
.lsp.json (routing & config)
    ↓
Python LSP Server Process (background)
    ├─ Concept Registry (JSON, indexed in memory)
    ├─ Relational Graph Engine
    ├─ Indigenous Knowledge Mapper
    └─ LSP Operations Handler

Language

• Server: Python 3.8+
• LSP Library: pygls (Python Language Server Protocol library)
• Plugin Type: Claude Code Standard LSP Plugin
• Configuration: JSON (plugin.json, .lsp.json) + YAML (concept registry, editable)

Dependencies

• Python 3.8+
• pygls library (LSP implementation)
• Standard library (no other heavy deps)
• File system access (registry files must be accessible)

Registry Conversion

The YAML registry you have will be automatically converted to JSON for:

• Efficient server loading
• Fast indexing and searching
• Relational graph construction

You'll maintain both formats (YAML for editing, JSON for serving).

---

What Makes This Unique

This is not a code autocomplete LSP. It's a semantic framework server that:

✓ Encodes your research framework as a first-class LSP participant ✓ Makes Indigenous knowledge systems primary, not secondary ✓ Provides relational understanding (how concepts connect) ✓ Integrates ceremonial context into every query ✓ Validates terminology for framework coherence ✓ Works across all file types (not just code) ✓ Serves definitions with full relational context (<50ms)

This is AI development moving from "understanding code" to "understanding thought frameworks." It's a new category of language server.

---

Next Steps

1. Review this scaffolding package
2. Provide to AI Studio - Copy initial_prompt.md as input, reference altri docs
3. Wait for generation - AI Studio builds complete, runnable server
4. Test - Try basic operations in Claude Code (might need slight fixes)
5. Deploy - Install as Claude Code plugin
6. Use & Iterate - Add more concepts, refine relationships, deepen Indigenous knowledge mappings

---

Questions for You Before AI Studio

Before handing this to AI Studio, consider:

1. Priority concepts: Want to start with core 7 concepts or go full 25+?
2. File types: Just Markdown + text, or also Python/JavaScript comments?
3. Query style: Prefer LSP operations (IDE-like) or also support natural language queries?
4. Performance threshold: 50ms okay, or need faster?
5. Auto-installation: Want plugin to auto-install Python dependencies, or manual setup?

These will help AI Studio optimize for your specific needs.

---

Success = You Can Do This in Claude Code

Before this server:

Me: "What does creative orientation mean in your framework?"
You: *needs to explain it again*

After this server:

You hover over "creative orientation" while writing
Server: Returns definition + how it relates to structural tension, 
        eight-feelings, ceremonial context, two-eyed seeing...
Me: I already know what you mean

That's the win. Framework knowledge becomes shared, legible, immediate.

---

This scaffolding package is ready for AI Studio. All specifications, semantic content, and technical requirements are included. You're ready to generate a working Indigenous AI DSL Server.

-by Claude.ai thread https://claude.ai/chat/307e9d71-b172-4785-8bb4-ba8c9bf984ba