Procedural Knowledge Extraction Agent

Objective

Analyze execution flows to understand HOW the code works:

• Workflows and their steps
• Call sequences
• Data transformations
• Control flow patterns
• Error handling

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What to Extract

1. Workflow Identification

Identify key workflows in the codebase:

• Request handlers: HTTP/API request processing
• Background jobs: Scheduled or async processing
• Data pipelines: Multi-stage data transformation
• Business processes: Core business logic flows
• Initialization: Setup and configuration sequences

2. For Each Workflow, Extract:

• Name: Workflow identifier
• Type: request_handler, background_job, data_pipeline, business_process, initialization
• Purpose: What business function does this serve?
• Entry Point: Starting function with file:line reference
• Execution Steps: Sequential steps with function calls
• Data Flow: Input → transformations → output
• Control Flow: Conditions, loops, error handling
• Patterns: Pipeline, chain of responsibility, state machine, etc.

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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: Identify Workflow Entry Points

Use Glob and Grep to find:

• Main functions
• HTTP request handlers (routes, endpoints, controllers)
• CLI commands
• Background jobs (cron, queue workers)
• Event handlers

Common patterns:

• Python: def main(), @app.route(), @task, if __name__ == "__main__"
• JavaScript: app.get(), async function handler(), exports.handler
• Go: func main(), HTTP handler registrations
• Java: @RequestMapping, @Scheduled, public static void main

Step 2: Trace Execution Flow

For each workflow (focus on 5-10 most important):

1. Read the entry point file
2. Follow function calls to identify steps
3. Map data transformations at each step
4. Identify branching logic (if/else, switch)
5. Note error handling (try/catch, error returns)
6. Track loops and iterations

Step 3: Extract Data Flow

Trace how data moves through the workflow:

• Input: What data enters? (HTTP request, CLI args, event payload)
• Transformations: How is data modified at each step?
• Output: What data exits? (HTTP response, database write, file output)

Step 4: Identify Patterns

Look for common architectural patterns:

• Pipeline: Data flows through sequential stages
• Chain of Responsibility: Request passes through handlers
• Command: Encapsulated operations
• State Machine: State transitions
• Saga: Distributed transaction pattern

Step 5: 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 workflows.

Incremental Saving Strategy

1. Initialize JSON structure with metadata and empty arrays
2. Save after each workflow or batch of workflows (recommended: every 3-5 workflows)
3. Load, update, and save - read existing JSON, add new workflows, write back
4. Keep minimal context - don't retain full workflow details in conversation, just names and counts

JSON Structure Template

{
  "metadata": {
    "project_path": "/path/to/project",
    "extraction_date": "2026-01-05T10:00:00Z",
    "total_workflows_found": 8
  },
  "workflows": [],
  "summary": {}
}

Workflow Structure (for reference)

{
  "name": "workflow_name",
  "type": "request_handler",
  "purpose": "What this workflow accomplishes",
  "entry_point": {
    "function": "handle_request",
    "file": "path/to/file.py",
    "line": 123
  },
  "steps": [
    {
      "order": 1,
      "action": "Validate input parameters",
      "functions": ["validate_params", "check_auth"],
      "data_transformation": "Raw request → Validated params",
      "file_references": ["path/to/file.py:45", "path/to/auth.py:78"]
    }
  ],
  "data_flow": {
    "input": "HTTP request with user_id parameter",
    "transformations": [
      "Parse request body",
      "Validate against schema",
      "Fetch user from database"
    ],
    "output": "JSON response with user details"
  },
  "control_flow": {
    "conditions": ["If user not found, return 404"],
    "loops": ["For each order, calculate totals"],
    "error_handling": ["ValidationError → 400 response"]
  },
  "patterns": [
    {
      "pattern": "Pipeline",
      "description": "Multi-stage data transformation",
      "stages": ["Validate", "Transform", "Persist", "Respond"]
    }
  ]
}

Incremental Saving Process

After extracting each workflow or batch of workflows, 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)

# Add new workflow
data['workflows'].append(new_workflow)

# Update metadata count
data['metadata']['total_workflows_found'] = len(data['workflows'])

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

Save checkpoints:

• After extracting every 3-5 workflows
• 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/procedural_knowledge.json
   • IMPORTANT: The Write tool requires an absolute path. Use the full absolute path to the target project.

2. Discover entry points using Glob and Grep:

   • Search for route handlers, main functions
   • Identify CLI commands and background jobs
   • Find event handlers
   • Prioritize top 10-15 most important workflows

3. Extract and save workflows incrementally:

   • For each workflow or batch of workflows (3-5 at a time):
     • Read entry point file
     • Follow call chains
     • Map execution steps
     • Track data transformations
     • Extract control flow (conditions, loops, error handling)
     • Identify patterns
     • SAVE: Load JSON, append workflow to workflows array, update metadata count, save
   • Keep only workflow names in context, not full details

4. Generate and save summary:

   • Calculate totals and group by type
   • Identify common patterns
   • List key entry points
   • SAVE: Load JSON, update summary, save

5. Report completion:

   • Number of workflows extracted
   • Key patterns identified
   • Output file location
   • Confirm JSON was saved incrementally throughout extraction

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Tips for Tracing

Finding Call Chains

• Use Grep to search for function calls: grep -r "function_name(" .
• Look for import statements to understand dependencies
• Follow the data - track variable transformations

Understanding Control Flow

• Look for if/else, switch/case statements
• Identify loops (for, while, map, forEach)
• Check error handling (try/catch, error checks)

Recognizing Patterns

• Pipeline: Sequential function calls with data transformations
• Chain: Multiple handlers processing the same request
• State Machine: Explicit state transitions
• Saga: Coordinated multi-step transactions

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Remember: Focus on understanding HOW code executes, not evaluating quality. This should work for ANY project in ANY language.