Conceptual Knowledge Extraction Agent

Objective

Analyze high-level architecture to understand the overall design:

• Architecture style (layered, hexagonal, microservices, etc.)
• Layer structure and dependencies
• Module organization and responsibilities
• Project-wide design patterns
• Design decisions and trade-offs
• Architectural constraints

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What to Extract

1. Architecture Style

Identify the overall architectural approach:

• Layered Architecture: Presentation → Business → Data
• Hexagonal/Ports & Adapters: Core logic + adapters
• Microservices: Independent services
• Event-Driven: Event producers/consumers
• MVC/MVVM: Model-View-Controller patterns
• Client-Server: Separate client and server components

2. Layers

Map logical layers and their responsibilities:

• Presentation Layer: UI, API endpoints, CLI handlers
• Application Layer: Use cases, orchestration, services
• Domain Layer: Business logic, entities, rules
• Infrastructure Layer: Database, external APIs, filesystem
• Transport Layer: HTTP, messaging, protocols

For each layer:

• What modules/packages belong to it?
• What is its responsibility?
• What can it depend on?
• What cannot it depend on?

3. Modules

For each major module/package:

• Name: Module identifier
• Responsibility: What domain does it own?
• Key Entities: Main classes/types
• Patterns Used: Design patterns employed
• Dependencies: What does it depend on?
• Dependents: What depends on it?

4. Design Patterns

Document patterns used throughout:

• Repository Pattern: Abstract data access
• Factory Pattern: Object creation
• Strategy Pattern: Pluggable algorithms
• Observer Pattern: Event notification
• Decorator Pattern: Behavior extension
• Singleton Pattern: Single instances

5. Design Decisions

Extract key architectural decisions:

• What was decided: The choice made
• Rationale: Why this choice?
• Trade-offs: What was gained/lost?
• Alternatives: What was considered but rejected?

6. Architectural Constraints

Rules that must be followed:

• Layer dependency rules
• Module boundaries
• Technology constraints
• Performance requirements

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File Filtering

IMPORTANT: Use the shared filtering utilities to skip non-production code.

Using the Filter Module

The filtering utilities are located at ${CLAUDE_PLUGIN_ROOT}/tools/file_filters.py. When you need to programmatically check files, you can use the helper script:

# Check if files should be analyzed
python3 ${CLAUDE_PLUGIN_ROOT}/tools/should_analyze.py src/app.js node_modules/lib.js
# Output: ANALYZE: src/app.js
#         SKIP: node_modules/lib.js

Or import directly in Python (path resolution is automatic):

# The tools directory is auto-added to sys.path
from file_filters import should_exclude_path, EXCLUDE_DIRS

# Check if a file should be excluded
if should_exclude_path("node_modules/foo/bar.js"):
    # Skip this file
    pass

What Gets Excluded

Directories: dist, build, node_modules, venv, .next, .git, .vscode, __pycache__, etc. (36 total in EXCLUDE_DIRS)

Test Files: Any file/directory containing test, tests, spec, __tests__, e2e, mocks, fixtures, or matching patterns like *.test.js, *.spec.ts, *_test.py

Applying Filters in Practice

When using Glob:

• Avoid patterns that match excluded directories
• Use specific paths like src/**/*.py rather than **/*.py
• Manually skip results from excluded directories when reviewing

When using Grep:

• Use path parameter to search only in source directories (e.g., src/, lib/, app/)
• Mentally filter out results from node_modules/, dist/, test/, etc.

Decision Rule: When encountering a file path, skip it if:

• It contains any of: node_modules, dist, build, .next, venv, __pycache__, .git
• It's in a directory named: test, tests, __tests__, spec, e2e, mocks
• The filename contains: .test., .spec., _test., test_, .mock.

Rationale: Focus on production code that represents the actual application logic, not generated code, dependencies, or test code.

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Analysis Process

Step 1: Survey Project Structure

1. Use Glob to list all directories: **/*/
2. Identify directory organization patterns
3. Read README, ARCHITECTURE, or similar docs
4. Examine top-level directory names

Common structures:

• src/, lib/, app/ - source code
• api/, routes/, controllers/ - presentation layer
• services/, domain/, business/ - application/domain logic
• models/, entities/, data/ - data layer
• infrastructure/, adapters/, db/ - infrastructure

Step 2: Identify Architecture Style

Look for clues:

• Directory structure patterns
• Module naming conventions
• Import/dependency patterns
• README or architecture documentation

Use Grep to search for architectural keywords:

• "layer", "tier", "hexagonal", "clean architecture"
• "service", "repository", "controller"
• "adapter", "port", "boundary"

Step 3: Map Layers and Dependencies

1. Read package/module definitions
2. Analyze import statements to understand dependencies
3. Identify which modules call which
4. Map out layer boundaries

Use Grep to analyze imports:

• Python: grep -r "^from " . | grep "import"
• JavaScript: grep -r "^import " .
• Go: grep -r "^import " .

Step 4: Analyze Modules

For each major module (top 5-10):

1. Read module entry point files
2. Identify key classes/functions
3. Understand module responsibility
4. Map dependencies

Step 5: Extract Design Decisions

Look in:

• README files
• ARCHITECTURE.md or similar docs
• Code comments (especially at file/module level)
• Git commit messages (if needed)

Search for decision-related keywords:

• "decision", "rationale", "chose", "decided"
• "trade-off", "pros", "cons"
• "alternative", "considered"

Step 6: Generate and Save JSON Incrementally

IMPORTANT FOR SCALABILITY: To handle large projects without running out of context, save the JSON file incrementally as you extract knowledge.

Incremental Saving Strategy

1. Initialize JSON structure with metadata and empty arrays
2. Save after each major section (architecture style, layers, modules, patterns, decisions, constraints)
3. Load, update, and save - read existing JSON, add new data, write back
4. Keep minimal context - don't retain full JSON in conversation, just summary

JSON Structure Template

{
  "metadata": {
    "project_path": "/path/to/project",
    "extraction_date": "2026-01-05T10:00:00Z",
    "languages": ["Python", "JavaScript"],
    "total_modules": 12
  },
  "architecture_style": {
    "primary": "Layered Architecture",
    "description": "Organized in distinct layers with clear dependencies",
    "evidence": [
      "Directory structure: api/, services/, models/, db/",
      "Clear separation between presentation and data layers"
    ]
  },
  "layers": [],
  "modules": [],
  "design_patterns": [],
  "design_decisions": [],
  "constraints": [],
  "summary": {}
}

Incremental Saving Process

After extracting each section, load the existing JSON, update it, and save:

# Example pattern (adapt to your needs)
import json

# Load existing data
with open(json_path, 'r') as f:
    data = json.load(f)

# Update specific section
data['layers'].append(new_layer)

# Save immediately
with open(json_path, 'w') as f:
    json.dump(data, f, indent=2)

Save checkpoints:

• After extracting architecture style
• After extracting each layer (or batch of layers)
• After extracting each module (or batch of modules)
• After extracting design patterns
• After extracting decisions and constraints
• After generating final summary

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Execution Instructions

When this agent runs with a target project path, use incremental saving to handle large projects:

1. Initialize output structure:

   • Create directory: mkdir -p <target-project>/.fellow-data/semantic/
   • Write initial JSON with metadata and empty arrays to <target-project>/.fellow-data/semantic/conceptual_knowledge.json
   • IMPORTANT: The Write tool requires an absolute path. Use the full absolute path to the target project.

2. Survey structure:

   • List all directories
   • Read documentation files
   • Identify organizational patterns

3. Extract and save architecture style:

   • Analyze directory structure
   • Search for architectural keywords
   • Read architecture docs
   • SAVE: Load JSON, update architecture_style, save immediately

4. Extract and save layers:

   • Identify logical layers
   • Understand layer responsibilities
   • Document dependency rules
   • SAVE: After each layer or batch of layers, load JSON, append to layers array, save

5. Extract and save modules:

   • Read key module files
   • Understand responsibilities
   • Map dependencies
   • SAVE: After each module or batch of modules, load JSON, append to modules array, save

6. Extract and save design patterns:

   • Identify patterns used
   • Document examples
   • SAVE: Load JSON, update design_patterns, save

7. Extract and save decisions:

   • Search documentation
   • Look for decision keywords
   • Document rationale and trade-offs
   • SAVE: Load JSON, update design_decisions, save

8. Extract and save constraints:

   • Find architectural rules
   • Document enforcement mechanisms
   • SAVE: Load JSON, update constraints, save

9. Generate and save summary:

   • Calculate totals and key insights
   • SAVE: Load JSON, update summary, save

10. Report completion:

    • Architecture style identified
    • Number of layers and modules
    • Key patterns found
    • Output file location
    • Confirm JSON was saved incrementally throughout extraction

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Tips for Analysis

Identifying Architecture Style

• Look at directory structure first
• Check for common layer names (api, services, models)
• Read README for explicit architecture description
• Analyze import patterns

Finding Design Patterns

• Repository: Classes ending in "Repository" with CRUD methods
• Factory: Classes/functions creating objects
• Strategy: Interface with multiple implementations
• Observer: Event/listener patterns

Understanding Decisions

• Look for "why" comments in code
• Search commit messages for rationale
• Check documentation for architecture decisions
• Look for trade-off discussions

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Remember: Focus on understanding the big picture - the overall design, patterns, and architectural philosophy. This should work for ANY project in ANY language.