989 Skill Intelligence System Bc55f4a5

Skill Seekers Intelligence System - Roadmap

Status: 🔬 Research & Design Phase Target: Open Source, Individual Developers Timeline: 6-12 months (iterative releases) Version: 1.0 (Initial Design) Last Updated: 2026-01-20

🎯 Vision

Build an auto-updating, context-aware, multi-skill codebase intelligence system that:

1. Detects your tech stack automatically
2. Generates separate skills for libraries and codebase modules
3. Updates skills when branches merge (git-based triggers)
4. Clusters skills intelligently based on what you're working on
5. Integrates with Claude Code via plugin architecture

Think of it as: A self-maintaining RAG system for your codebase that knows exactly which knowledge to load based on context.

🏗️ System Architecture Overview

┌─────────────────────────────────────────────────────────────┐
│               Skill Seekers Intelligence System             │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  Layer 1: PROJECT CONFIGURATION                            │
│  ┌──────────────────────────────────────────┐              │
│  │ .skill-seekers/                          │              │
│  │ ├── config.yml          (user editable)  │              │
│  │ ├── skills/             (auto-generated) │              │
│  │ ├── cache/              (embeddings)     │              │
│  │ └── hooks/              (git triggers)   │              │
│  └──────────────────────────────────────────┘              │
│                                                             │
│  Layer 2: SKILL GENERATION ENGINE                          │
│  ┌──────────────────────────────────────────┐              │
│  │ • Tech Stack Detector                    │              │
│  │ • Modular Codebase Analyzer (C3.x)       │              │
│  │ • Library Skill Downloader               │              │
│  │ • Git-Based Trigger System               │              │
│  └──────────────────────────────────────────┘              │
│                                                             │
│  Layer 3: SKILL CLUSTERING ENGINE                          │
│  ┌──────────────────────────────────────────┐              │
│  │ Phase 1: Import-Based (deterministic)    │              │
│  │ Phase 2: Embedding-Based (experimental)  │              │
│  └──────────────────────────────────────────┘              │
│                                                             │
│  Layer 4: CLAUDE CODE PLUGIN                               │
│  ┌──────────────────────────────────────────┐              │
│  │ • File Open Handler                      │              │
│  │ • Branch Merge Listener                  │              │
│  │ • Context Manager                        │              │
│  │ • Skill Loader                           │              │
│  └──────────────────────────────────────────┘              │
│                                                             │
└─────────────────────────────────────────────────────────────┘

📋 Development Phases

Phase 0: Research & Validation (2-3 weeks)

Status: 🔬 Current Phase Goal: Validate core assumptions, design architecture

Deliverables:

• ✅ Technical architecture document
• ✅ Roadmap document (this file)
• ✅ POC design for Phase 1
• ✅ Research clustering algorithms
• ✅ Design config schema

Success Criteria:

• Clear technical direction
• Validated assumptions (import analysis works, etc.)
• Ready to build Phase 1

Phase 1: Git-Based Auto-Generation (3-4 weeks)

Status: 📅 Planned Goal: Auto-generate skills on branch merges

Milestones

Milestone 1.1: Project Initialization (Week 1)

# Command
skill-seekers init-project --directory .

# Creates
.skill-seekers/
├── config.yml          # Project configuration
├── hooks/
│   ├── post-merge      # Git hook
│   └── post-commit     # Optional
└── skills/
    ├── libraries/      # Empty (Phase 2)
    └── codebase/       # Will be generated

Config Schema (v1.0):

# .skill-seekers/config.yml
version: "1.0"
project_name: skill-seekers
watch_branches:
  - main
  - development

# Phase 1: Simple, no modules yet
skill_generation:
  enabled: true
  output_dir: .skill-seekers/skills/codebase

git_hooks:
  enabled: true
  trigger_on:
    - post-merge
    - post-commit  # optional

Deliverables:

• skill-seekers init-project command
• Config schema v1.0
• Git hook installer
• Project directory structure creator

Success Criteria:

• Running init-project sets up directory structure
• Git hooks are installed correctly
• Config file is created with sensible defaults

Milestone 1.2: Git Hook Integration (Week 2)

Git Hook Logic:

#!/bin/bash
# .skill-seekers/hooks/post-merge

# Check if we're on a watched branch
CURRENT_BRANCH=$(git rev-parse --abbrev-ref HEAD)
WATCH_BRANCHES=$(yq '.watch_branches[]' .skill-seekers/config.yml)

if echo "$WATCH_BRANCHES" | grep -q "$CURRENT_BRANCH"; then
  echo "🔄 Branch merge detected on $CURRENT_BRANCH"
  echo "🚀 Regenerating skills..."

  skill-seekers regenerate-skills --branch "$CURRENT_BRANCH"

  echo "✅ Skills updated"
fi

Deliverables:

• Git hook templates
• Hook installer/uninstaller
• Branch detection logic
• Hook execution logging

Success Criteria:

• Merging to watched branch triggers skill regeneration
• Only watched branches trigger updates
• Hooks can be enabled/disabled via config

Milestone 1.3: Basic Skill Regeneration (Week 3)

Command:

skill-seekers regenerate-skills --branch main

# Runs:
# 1. Detects changed files since last generation
# 2. Runs codebase analysis (existing C3.x features)
# 3. Generates single skill: codebase.skill
# 4. Updates .skill-seekers/skills/codebase/codebase.skill

Phase 1 Scope (Simple):

• Single skill for entire codebase
• No modularization yet (Phase 3)
• No library skills yet (Phase 2)
• No clustering yet (Phase 4)

Deliverables:

• regenerate-skills command
• Change detection (git diff)
• Incremental vs full regeneration logic
• Skill versioning (timestamp)

Success Criteria:

• Manual regeneration works
• Git hook triggers regeneration
• Skill is usable in Claude Code

Milestone 1.4: Dogfooding & Testing (Week 4)

Test on skill-seekers itself:

cd Skill_Seekers/
skill-seekers init-project --directory .

# Make code change
git checkout -b test-auto-regen
echo "# Test" >> README.md
git commit -am "test: Auto-regen test"

# Merge to development
git checkout development
git merge test-auto-regen
# → Should trigger skill regeneration

# Verify
cat .skill-seekers/skills/codebase/codebase.skill

Deliverables:

• End-to-end test on skill-seekers
• Performance benchmarks
• Bug fixes
• Documentation updates

Success Criteria:

• Works on skill-seekers codebase
• Regeneration completes in <5 minutes
• Generated skill is high quality
• No major bugs

Phase 2: Tech Stack Detection & Library Skills (2-3 weeks)

Status: 📅 Planned (After Phase 1) Goal: Auto-detect tech stack and download library skills

Milestones

Milestone 2.1: Tech Stack Detector (Week 1)

Detection Strategy:

# src/skill_seekers/intelligence/stack_detector.py

class TechStackDetector:
    """Detect tech stack from project files"""

    def detect(self, project_dir: Path) -> dict:
        stack = {
            "languages": [],
            "frameworks": [],
            "databases": [],
            "tools": []
        }

        # Python ecosystem
        if (project_dir / "requirements.txt").exists():
            stack["languages"].append("Python")
            deps = self._parse_requirements()

            if "fastapi" in deps:
                stack["frameworks"].append("FastAPI")
            if "django" in deps:
                stack["frameworks"].append("Django")
            if "flask" in deps:
                stack["frameworks"].append("Flask")

        # JavaScript/TypeScript ecosystem
        if (project_dir / "package.json").exists():
            deps = self._parse_package_json()

            if "typescript" in deps:
                stack["languages"].append("TypeScript")
            else:
                stack["languages"].append("JavaScript")

            if "react" in deps:
                stack["frameworks"].append("React")
            if "vue" in deps:
                stack["frameworks"].append("Vue")
            if "next" in deps:
                stack["frameworks"].append("Next.js")

        # Database detection
        if (project_dir / ".env").exists():
            env = self._parse_env()
            db_url = env.get("DATABASE_URL", "")

            if "postgres" in db_url:
                stack["databases"].append("PostgreSQL")
            if "mysql" in db_url:
                stack["databases"].append("MySQL")
            if "mongodb" in db_url:
                stack["databases"].append("MongoDB")

        # Docker services
        if (project_dir / "docker-compose.yml").exists():
            services = self._parse_docker_compose()
            stack["tools"].extend(services)

        return stack

Supported Ecosystems (v1.0):

• Python: FastAPI, Django, Flask, SQLAlchemy
• JavaScript/TypeScript: React, Vue, Next.js, Express
• Databases: PostgreSQL, MySQL, MongoDB, Redis
• Tools: Docker, Nginx, Celery

Deliverables:

• TechStackDetector class
• Parsers for common config files
• Detection accuracy tests
• skill-seekers detect-stack command

Success Criteria:

• 90%+ accuracy on common stacks
• Fast (<1 second)
• Extensible (easy to add new detectors)

Milestone 2.2: Library Skill Downloader (Week 2)

Architecture:

# src/skill_seekers/intelligence/library_manager.py

class LibrarySkillManager:
    """Download and cache library skills"""

    def download_skills(self, tech_stack: dict) -> list[Path]:
        skills = []

        for framework in tech_stack["frameworks"]:
            skill_path = self._download_skill(framework)
            skills.append(skill_path)

        return skills

    def _download_skill(self, name: str) -> Path:
        # Try skillseekersweb.com API first
        skill = self._fetch_from_api(name)

        if not skill:
            # Fallback: generate from GitHub repo
            skill = self._generate_from_github(name)

        # Cache locally
        cache_path = Path(f".skill-seekers/skills/libraries/{name}.skill")
        cache_path.write_text(skill)

        return cache_path

Library Skill Sources:

1. SkillSeekersWeb.com API (preferred)

   • Pre-generated skills for popular frameworks
   • Curated, high-quality
   • Fast download

2. On-Demand Generation (fallback)

   • Generate from framework's GitHub repo
   • Uses existing github_scraper.py
   • Cached after first generation

Deliverables:

• LibrarySkillManager class
• API client for skillseekersweb.com
• Caching system
• skill-seekers download-libraries command

Success Criteria:

• Downloads skills for detected frameworks
• Caching works (no duplicate downloads)
• Handles missing skills gracefully

Milestone 2.3: Config Schema v2.0 (Week 3)

Updated Config:

# .skill-seekers/config.yml
version: "2.0"
project_name: skill-seekers
watch_branches:
  - main
  - development

# NEW: Tech stack configuration
tech_stack:
  auto_detect: true
  frameworks:
    - FastAPI
    - React
    - PostgreSQL

  # Override auto-detection
  custom:
    - name: "Internal Framework"
      skill_url: "https://internal.com/skills/framework.skill"

# Library skills
library_skills:
  enabled: true
  source: "skillseekersweb.com"
  cache_dir: .skill-seekers/skills/libraries
  update_frequency: "weekly"  # or: never, daily, on-branch-merge

skill_generation:
  enabled: true
  output_dir: .skill-seekers/skills/codebase

git_hooks:
  enabled: true
  trigger_on:
    - post-merge

Deliverables:

• Config schema v2.0
• Migration from v1.0 to v2.0
• Validation logic
• Documentation

Success Criteria:

• Backward compatible with v1.0
• Clear upgrade path
• Well documented

Phase 3: Modular Skill Splitting (3-4 weeks)

Status: 📅 Planned (After Phase 2) Goal: Split codebase into modular skills based on config

Milestones

Milestone 3.1: Module Configuration (Week 1)

Config Schema v3.0:

# .skill-seekers/config.yml
version: "3.0"
project_name: skill-seekers

# ... (previous config)

# NEW: Module definitions
modules:
  backend:
    path: src/skill_seekers/
    split_by: namespace  # or: directory, feature, custom

    skills:
      - name: cli
        description: "Command-line interface tools"
        include:
          - "cli/**/*.py"
        exclude:
          - "cli/**/*_test.py"

      - name: scrapers
        description: "Web scraping and analysis"
        include:
          - "cli/doc_scraper.py"
          - "cli/github_scraper.py"
          - "cli/pdf_scraper.py"

      - name: adaptors
        description: "Platform adaptor system"
        include:
          - "cli/adaptors/**/*.py"

      - name: mcp
        description: "MCP server integration"
        include:
          - "mcp/**/*.py"

  tests:
    path: tests/
    split_by: directory
    skills:
      - name: unit-tests
        include: ["test_*.py"]

Splitting Strategies:

class ModuleSplitter:
    """Split codebase into modular skills"""

    STRATEGIES = {
        "namespace": self._split_by_namespace,
        "directory": self._split_by_directory,
        "feature": self._split_by_feature,
        "custom": self._split_by_custom,
    }

    def _split_by_namespace(self, module_config: dict) -> list[Skill]:
        # Python: package.module.submodule
        # JS: import { X } from './path/to/module'
        pass

    def _split_by_directory(self, module_config: dict) -> list[Skill]:
        # One skill per top-level directory
        pass

    def _split_by_feature(self, module_config: dict) -> list[Skill]:
        # Group by feature (auth, api, models, etc.)
        pass

Deliverables:

• Module splitting engine
• Config schema v3.0
• Support for glob patterns
• Validation logic

Success Criteria:

• Can split skill-seekers into 4-5 modules
• Each module is focused and cohesive
• User has full control via config

Milestone 3.2: Modular Skill Generation (Week 2-3)

Output Structure:

.skill-seekers/skills/
├── libraries/
│   ├── fastapi.skill
│   ├── anthropic.skill
│   └── beautifulsoup.skill
│
└── codebase/
    ├── cli.skill            # CLI tools
    ├── scrapers.skill       # Scraping logic
    ├── adaptors.skill       # Platform adaptors
    ├── mcp.skill            # MCP server
    └── tests.skill          # Test suite

Each skill contains:

• Focused documentation (one module only)
• API reference for that module
• Design patterns in that module
• Test examples for that module
• Cross-references to related skills

Deliverables:

• Modular skill generator
• Cross-reference system
• Skill metadata (dependencies, related skills)
• Update generation pipeline

Success Criteria:

• Generates 4-5 focused skills for skill-seekers
• Each skill is 50-200 lines (not too big)
• Cross-references work

Milestone 3.3: Testing & Iteration (Week 4)

Test Plan:

1. Generate modular skills for skill-seekers
2. Use in Claude Code for 1 week
3. Compare vs single skill (Phase 1)
4. Iterate on module boundaries

Success Criteria:

• Modular skills are more useful than single skill
• Module boundaries make sense
• Performance is acceptable

Phase 4: Import-Based Clustering (2-3 weeks)

Status: 📅 Planned (After Phase 3) Goal: Load only relevant skills based on current file

Milestones

Milestone 4.1: Import Analyzer (Week 1)

Algorithm:

# src/skill_seekers/intelligence/import_analyzer.py

class ImportAnalyzer:
    """Analyze imports to find relevant skills"""

    def find_relevant_skills(
        self,
        current_file: Path,
        available_skills: list[SkillMetadata]
    ) -> list[Path]:
        # 1. Parse imports from current file
        imports = self._parse_imports(current_file)
        # Example: editing src/cli/doc_scraper.py
        # Imports:
        #   - from anthropic import Anthropic
        #   - from bs4 import BeautifulSoup
        #   - from skill_seekers.cli.adaptors import get_adaptor

        # 2. Map imports to skills
        relevant = []

        for imp in imports:
            # External library?
            if self._is_external(imp):
                library_skill = self._find_library_skill(imp)
                if library_skill:
                    relevant.append(library_skill)

            # Internal module?
            else:
                module_skill = self._find_module_skill(imp, available_skills)
                if module_skill:
                    relevant.append(module_skill)

        # 3. Add current module's skill
        current_skill = self._find_skill_for_file(current_file, available_skills)
        if current_skill:
            relevant.insert(0, current_skill)  # First in list

        # 4. Deduplicate and rank
        return self._deduplicate(relevant)[:5]  # Max 5 skills

Example Output:

# Editing: src/cli/doc_scraper.py
find_relevant_skills("src/cli/doc_scraper.py")

# Returns:
[
    "codebase/scrapers.skill",    # Current module (highest priority)
    "libraries/beautifulsoup.skill",  # External import
    "libraries/anthropic.skill",      # External import
    "codebase/adaptors.skill",        # Internal import
]

Deliverables:

• ImportAnalyzer class
• Python import parser (AST-based)
• JavaScript import parser (regex-based)
• Import-to-skill mapping logic

Success Criteria:

• Correctly identifies imports from files
• Maps imports to skills accurately
• Fast (<100ms for typical file)

Milestone 4.2: Claude Code Plugin (Week 2)

Plugin Architecture:

# claude_plugins/skill-seekers-intelligence/agent.py

class SkillSeekersIntelligenceAgent:
    """
    Claude Code plugin that manages skill loading
    """

    def __init__(self):
        self.config = self._load_config()
        self.import_analyzer = ImportAnalyzer()
        self.current_skills = []

    async def on_file_open(self, file_path: str):
        """
        Hook: User opens a file
        Action: Load relevant skills
        """
        # Find relevant skills
        relevant = self.import_analyzer.find_relevant_skills(
            file_path,
            self.config.available_skills
        )

        # Load into Claude context
        self.load_skills(relevant)

        # Notify user
        print(f"📚 Loaded {len(relevant)} relevant skills:")
        for skill in relevant:
            print(f"  - {skill.name}")

    async def on_branch_merge(self, branch: str):
        """
        Hook: Branch merged
        Action: Regenerate skills if needed
        """
        if branch in self.config.watch_branches:
            print(f"🔄 Regenerating skills for {branch}...")
            await self.regenerate_skills(branch)
            print("✅ Skills updated")

    def load_skills(self, skills: list[Path]):
        """Load skills into Claude context"""
        self.current_skills = skills

        # Tell Claude which skills are loaded
        # (Implementation depends on Claude Code API)

Plugin Hooks:

• on_file_open - Load relevant skills
• on_file_save - Update skills if needed
• on_branch_merge - Regenerate skills
• on_branch_checkout - Switch skill set

Deliverables:

• Claude Code plugin skeleton
• File open handler
• Branch merge listener
• Skill loader integration

Success Criteria:

• Plugin loads in Claude Code
• File opens trigger skill loading
• Branch merges trigger regeneration
• User sees which skills are loaded

Milestone 4.3: Testing & Dogfooding (Week 3)

Test Plan:

1. Install plugin in Claude Code
2. Open skill-seekers codebase
3. Navigate files, observe skill loading
4. Make changes, merge branch, observe regeneration

Success Criteria:

• Correct skills load for each file
• No performance issues
• User experience is smooth

Phase 5: Embedding-Based Clustering (3-4 weeks)

Status: 🔬 Experimental (After Phase 4) Goal: Smarter clustering using semantic similarity

Milestones

Milestone 5.1: Embedding Generation (Week 1-2)

Architecture:

# src/skill_seekers/intelligence/embeddings.py

class SkillEmbedder:
    """Generate and cache embeddings for skills and files"""

    def __init__(self):
        # Use lightweight model for speed
        # Options: sentence-transformers, OpenAI, Anthropic
        self.model = "all-MiniLM-L6-v2"  # Fast, good quality

    def embed_skill(self, skill_path: Path) -> np.ndarray:
        """Generate embedding for entire skill"""
        content = skill_path.read_text()

        # Extract key sections
        api_ref = self._extract_section(content, "API Reference")
        examples = self._extract_section(content, "Examples")

        # Embed combined text
        text = f"{api_ref}\n{examples}"
        embedding = self.model.encode(text)

        # Cache for reuse
        self._cache_embedding(skill_path, embedding)

        return embedding

    def embed_file(self, file_path: Path) -> np.ndarray:
        """Generate embedding for current file"""
        content = file_path.read_text()

        # Embed full content or summary
        embedding = self.model.encode(content[:5000])  # First 5K chars

        return embedding

Embedding Strategy:

• Skills: Embed once, cache forever (until skill updates)
• Files: Embed on-demand (or cache for open files)
• Model: Lightweight (all-MiniLM-L6-v2 is 80MB, fast)
• Storage: .skill-seekers/cache/embeddings/

Deliverables:

• SkillEmbedder class
• Embedding cache system
• Similarity search (cosine similarity)
• Benchmark performance

Success Criteria:

• Fast embedding (<100ms per file)
• Accurate similarity (>80% precision)
• Reasonable storage (<100MB for typical project)

Milestone 5.2: Hybrid Clustering (Week 3)

Algorithm:

class HybridClusteringEngine:
    """
    Combine import-based (fast, deterministic)
    with embedding-based (smart, flexible)
    """

    def find_relevant_skills(
        self,
        current_file: Path,
        available_skills: list[SkillMetadata]
    ) -> list[Path]:
        # Method 1: Import-based (weight: 0.7)
        import_skills = self.import_analyzer.find_relevant_skills(
            current_file, available_skills
        )

        # Method 2: Embedding-based (weight: 0.3)
        file_embedding = self.embedder.embed_file(current_file)
        similar_skills = self._find_similar_skills(
            file_embedding, available_skills
        )

        # Combine with weighted ranking
        combined = self._weighted_merge(
            import_skills, similar_skills,
            weights=[0.7, 0.3]
        )

        return combined[:5]  # Top 5

Why Hybrid?

• Import-based: Precise but misses semantic similarity
• Embedding-based: Flexible but sometimes wrong
• Hybrid: Best of both worlds

Deliverables:

• Hybrid clustering algorithm
• Weighted ranking system
• A/B testing framework
• Performance comparison

Success Criteria:

• Better than import-only (A/B test)
• Not significantly slower (<200ms)
• Handles edge cases well

Milestone 5.3: Experimental Features (Week 4)

Ideas to Explore:

1. Dynamic Skill Loading: Load skills as conversation progresses
2. Conversation Context: Use chat history to refine clustering
3. Feedback Loop: Learn from user corrections
4. Skill Ranking: Rank skills by usefulness

Deliverables:

• Experimental features (optional)
• Documentation of learnings
• Recommendations for v2.0

Success Criteria:

• Identified valuable experimental features
• Documented what works and what doesn't

📊 Success Metrics

Phase 1 Metrics

• ✅ Auto-regeneration works on branch merge
• ✅ <5 minutes to regenerate skills
• ✅ Git hooks work reliably

Phase 2 Metrics

• ✅ 90%+ accuracy on tech stack detection
• ✅ Library skills downloaded successfully
• ✅ <2 seconds to download cached skill

Phase 3 Metrics

• ✅ Modular skills are 50-200 lines each
• ✅ User can configure module boundaries
• ✅ Cross-references work

Phase 4 Metrics

• ✅ Correct skills load 85%+ of the time
• ✅ <100ms to find relevant skills
• ✅ Plugin works smoothly in Claude Code

Phase 5 Metrics

• ✅ Hybrid clustering beats import-only
• ✅ <200ms to cluster with embeddings
• ✅ Embedding cache < 100MB

🎯 Target Users

Primary: Individual Open Source Developers

• Working on their own projects
• Want better codebase understanding
• Use Claude Code for development
• Value automation over manual work

Secondary: Small Teams

• Onboarding new developers
• Maintaining large codebases
• Need consistent documentation

Future: Enterprise

• Large codebases (1M+ LOC)
• Multiple microservices
• Advanced clustering requirements

📦 Deliverables

User-Facing

• CLI commands (init, regenerate, detect, download)
• Claude Code plugin
• Configuration system (.skill-seekers/config.yml)
• Documentation (user guide, tutorial)

Developer-Facing

• Python library (skill_seekers.intelligence)
• Plugin SDK (for extending)
• API documentation
• Architecture documentation

Infrastructure

• Git hooks
• CI/CD integration
• Embedding cache system
• Skill registry

🚧 Known Challenges

Technical

1. Context Window Limits: Even with clustering, large projects may exceed limits
2. Embedding Performance: Need fast, lightweight models
3. Accuracy: Import analysis may miss implicit dependencies
4. Versioning: Skills must stay in sync with code

Product

1. Onboarding: Complex system needs good UX
2. Configuration: Balance power vs simplicity
3. Debugging: When clustering fails, hard to debug

Operational

1. Maintenance: More components = more maintenance
2. Testing: Hard to test context-aware features
3. Documentation: Need excellent docs for adoption

🔮 Future Ideas (Post v1.0)

Advanced Clustering

• Multi-file context (editing 3 files → load related skills)
• Conversation-aware clustering (use chat history)
• Feedback loop (learn from corrections)

Multi-Project

• Workspace support (multiple projects)
• Cross-project skills (shared libraries)
• Monorepo support

Integrations

• VS Code extension
• IntelliJ plugin
• Web dashboard

Advanced Features

• Skill versioning (track changes over time)
• Skill diff (compare versions)
• Skill analytics (usage tracking)

📚 References

• Existing Features: C3.x Codebase Analysis (patterns, examples, architecture)
• Platform: Claude Code plugin system
• Similar Tools: GitHub Copilot, Cursor, Tabnine
• Research: RAG systems, semantic search, code embeddings

Version: 1.0 Status: Research & Design Phase Next Review: After Phase 0 completion Owner: Yusuf Karaaslan