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Intelligent Resume Orchestration - Usage Examples

This document provides practical examples of using the Phase 2 intelligent orchestration system.

Table of Contents

  1. Quick Start
  2. Parsing Job Postings
  3. Matching Resumes
  4. Generating CRUD Operations
  5. Complete Workflow
  6. Natural Language Commands
  7. Agent Integration

Quick Start

Scenario: Tailor Resume for a Job Posting

# Step 1: Parse the job posting
python src/parsers/job_posting_parser.py "data/job_listings/Subscription Billing Software Engineering Manager.md"

# Step 2: Match your resume with the job
python src/orchestrator/resume_matcher.py \
  "data/job_listings/Subscription Billing Software Engineering Manager.md" \
  "data/resumes/1e2c70dc-0103-4fa1-b591-93a4fae05de5.json"

# Step 3: Generate tailoring operations (dry-run)
python src/orchestrator/crud_orchestrator.py \
  "data/job_listings/Subscription Billing Software Engineering Manager.md" \
  "data/resumes/1e2c70dc-0103-4fa1-b591-93a4fae05de5.json" \
  "Ford"

Parsing Job Postings

Example 1: Parse a Job Posting File

python src/parsers/job_posting_parser.py "data/job_listings/GM Job.md"

Output:

=== Job Posting Analysis ===

Title: Subscription Billing Software Engineering Manager
Company: General Motors
Location: Austin, TX
Work Arrangement: hybrid

Required Skills (16):
  - zuora
  - revpro
  - java
  - spring boot
  - microservices
  - rest api
  - aws
  - azure
  - gcp
  - ci/cd
  - git
  - datadog
  - subscription billing
  - sox
  - audit
  - compliance

Responsibilities (6):
  - Lead the end-to-end implementation and ongoing operation of Zuora...
  - Collaborate with cross-functional teams...
  - Ensure compliance with SOX and audit requirements...
  - Drive technical excellence through code reviews...
  - Mentor and develop engineering talent...
  - Establish and maintain observability practices...

Experience Required: 5+ years management

Example 2: Programmatic Usage

from parsers import JobPostingParser

parser = JobPostingParser()
job_data = parser.parse_file("data/job_listings/GM Job.md")

print(f"Job Title: {job_data['title']}")
print(f"Company: {job_data['company']}")
print(f"Required Skills: {', '.join(job_data['required_skills'][:5])}")

Matching Resumes

Example 1: Calculate Match Score

python src/orchestrator/resume_matcher.py \
  "data/job_listings/Subscription Billing Software Engineering Manager.md" \
  "data/resumes/1e2c70dc-0103-4fa1-b591-93a4fae05de5.json"

Output:

=== Resume Match Analysis ===

Overall Match Score: 65.5%

Skill Match: 50.0%
  - Matching: 8 skills
  - Missing: 8 skills

Matching Skills:
  ✓ python
  ✓ java
  ✓ microservices
  ✓ rest api
  ✓ aws
  ✓ docker
  ✓ kubernetes
  ✓ ci/cd

Missing Skills:
  ✗ zuora
  ✗ revpro
  ✗ azure
  ✗ gcp
  ✗ datadog
  ✗ subscription billing
  ✗ sox
  ✗ compliance

Relevant Experience (2 entries):
  - Tech Corp (Senior Software Engineer)
    Relevance: 8.5, Matching bullets: 3
  - Previous Company (Software Engineer)
    Relevance: 5.0, Matching bullets: 1

Suggestions:
  1. Add these missing skills: zuora, revpro, azure, gcp, datadog
  2. Consider updating title to match job: 'Subscription Billing Software Engineering Manager'
  3. Emphasize experience at Tech Corp - it has 3 relevant bullets
  4. Align experience bullets with key responsibilities
  5. Highlight experience with: SOX, audit, compliance

Example 2: Programmatic Usage

from orchestrator import ResumeMatcher
from parsers import JobPostingParser
import json

# Parse job
parser = JobPostingParser()
job_data = parser.parse_file("data/job_listings/GM Job.md")

# Load resume
with open("data/resumes/resume_id.json", 'r') as f:
    resume_data = json.load(f)

# Match
matcher = ResumeMatcher()
result = matcher.match(job_data, resume_data)

print(f"Match Score: {result['score']}%")
print(f"Missing Skills: {', '.join(result['missing_skills'][:5])}")

Generating CRUD Operations

Example 1: Generate Operations (Dry-Run)

python src/orchestrator/crud_orchestrator.py \
  "data/job_listings/Subscription Billing Software Engineering Manager.md" \
  "data/resumes/1e2c70dc-0103-4fa1-b591-93a4fae05de5.json" \
  "Ford"

Output:

=== CRUD Orchestration Plan ===

Resume: Ford
Job: Subscription Billing Software Engineering Manager at General Motors
Match Score: 65.5%

Generated 10 operations:

1. Update title to: Subscription Billing Software Engineering Manager
   Command: python src/crud/basic_info.py --resume "Ford" --update-title "Subscription Billing Software Engineering Manager"

2. Add languages skills: java, microservices, rest
   Command: python src/crud/technical_skills.py --resume "Ford" --append-to-category "languages" "java, microservices, rest"

3. Add cloud skills: aws, azure, gcp
   Command: python src/crud/technical_skills.py --resume "Ford" --append-to-category "cloud" "aws, azure, gcp"

4. Add devops skills: ci/cd, datadog, git
   Command: python src/crud/technical_skills.py --resume "Ford" --append-to-category "devops" "ci/cd, datadog, git"

5. Add billing skills: revpro, subscription billing, zuora
   Command: python src/crud/technical_skills.py --resume "Ford" --append-to-category "billing" "revpro, subscription billing, zuora"

6. Update summary to highlight: Tech Corp experience [MANUAL]
   Command: # Manual: Update summary to emphasize: Tech Corp experience

7. Add expertise: Billing
   Command: python src/crud/expertise.py --resume "Ford" --add "Billing"

8. Add expertise: Subscription
   Command: python src/crud/expertise.py --resume "Ford" --add "Subscription"

9. Add expertise: Revenue
   Command: python src/crud/expertise.py --resume "Ford" --add "Revenue"

10. Highlight compliance experience: compliance, audit, SOX [MANUAL]
    Command: # Manual: Emphasize compliance, audit, SOX experience


=== Executing Operations (DRY RUN) ===

[1/10] Update title to: Subscription Billing Software Engineering Manager
  → DRY RUN: python src/crud/basic_info.py --resume "Ford" --update-title "Subscription Billing Software Engineering Manager"
[2/10] Add languages skills: java, microservices, rest
  → DRY RUN: python src/crud/technical_skills.py --resume "Ford" --append-to-category "languages" "java, microservices, rest"
...

Results:
  Total: 10
  Successful: 8
  Failed: 0
  Skipped: 2

Example 2: Programmatic Usage with Custom Callback

from orchestrator import CRUDOrchestrator
from parsers import JobPostingParser
import json

# Parse job
parser = JobPostingParser()
job_data = parser.parse_file("data/job_listings/GM Job.md")

# Load resume
with open("data/resumes/resume_id.json", 'r') as f:
    resume_data = json.load(f)

# Match
from orchestrator import ResumeMatcher
matcher = ResumeMatcher()
match_result = matcher.match(job_data, resume_data)

# Generate operations
orchestrator = CRUDOrchestrator(dry_run=True)

# Set progress callback
def progress_callback(message):
    print(f"[PROGRESS] {message}")

orchestrator.set_progress_callback(progress_callback)

# Generate and execute
operations = orchestrator.generate_operations(job_data, match_result, "Ford")
results = orchestrator.execute_operations(operations)

print(f"\nCompleted: {results['successful']}/{results['total']} operations")

Complete Workflow

Scenario: Tailor Resume for GM Subscription Billing Role

#!/bin/bash
# complete_workflow.sh

JOB_FILE="data/job_listings/Subscription Billing Software Engineering Manager.md"
RESUME_ID="1e2c70dc-0103-4fa1-b591-93a4fae05de5"
RESUME_FILE="data/resumes/${RESUME_ID}.json"
RESUME_NAME="Ford"
OUTPUT_DIR="out"

echo "=== Step 1: Parse Job Posting ==="
python src/parsers/job_posting_parser.py "$JOB_FILE"

echo -e "\n=== Step 2: Match Resume ==="
python src/orchestrator/resume_matcher.py "$JOB_FILE" "$RESUME_FILE"

echo -e "\n=== Step 3: Generate Operations (Dry-Run) ==="
python src/orchestrator/crud_orchestrator.py "$JOB_FILE" "$RESUME_FILE" "$RESUME_NAME"

echo -e "\n=== Step 4: Review and Execute Operations ==="
read -p "Execute operations? (y/n) " -n 1 -r
echo
if [[ $REPLY =~ ^[Yy]$ ]]
then
    # Execute each operation manually or modify orchestrator to execute
    echo "Executing operations..."
    # Add execution logic here
fi

echo -e "\n=== Step 5: Export Resume ==="
python src/tailor.py \
  --resume "$RESUME_NAME" \
  --jd "$JOB_FILE" \
  --out "${OUTPUT_DIR}/gm_subscription_billing.html" \
  --format html \
  --theme modern \
  --docx

echo -e "\n✅ Workflow complete!"
echo "Output files:"
echo "  - ${OUTPUT_DIR}/gm_subscription_billing.html"
echo "  - ${OUTPUT_DIR}/gm_subscription_billing.docx"

Natural Language Commands

Example 1: Parse Natural Language Commands

from parsers import NLCommandParser

parser = NLCommandParser()

commands = [
    "Add Python and Java to my technical skills",
    "Update my title to Principal Architect",
    "List my certifications",
    "Duplicate the Ford resume as GM_Resume"
]

for cmd in commands:
    result = parser.parse(cmd, default_resume="Master Resume")
    print(f"Command: {cmd}")
    print(f"Intent: {result['intent']}")
    print(f"Entity: {result['entity']}")
    print(f"CRUD Command: {result['command']}")
    print()

Output:

Command: Add Python and Java to my technical skills
Intent: add
Entity: technical_skills
CRUD Command: python src/crud/technical_skills.py --resume "Master Resume" --append-to-category "languages" "Python, Java"

Command: Update my title to Principal Architect
Intent: update
Entity: basic_info
CRUD Command: python src/crud/basic_info.py --resume "Master Resume" --update-title "Principal Architect"

Command: List my certifications
Intent: list
Entity: certification
CRUD Command: python src/crud/certifications.py --resume "Master Resume" --list

Command: Duplicate the Ford resume as GM_Resume
Intent: duplicate
Entity: resume
CRUD Command: python src/duplicate_resume.py --resume "Ford" --new-name "GM_Resume"

Agent Integration

Example 1: Using the Agent

# Start the agent
python agent.py

Conversation:

You: Tailor my Ford resume for the GM Subscription Billing position

Agent: I'll analyze the job posting and intelligently tailor your Ford resume.

run: python src/orchestrator/crud_orchestrator.py 'data/job_listings/Subscription Billing Software Engineering Manager.md' 'data/resumes/1e2c70dc-0103-4fa1-b591-93a4fae05de5.json' 'Ford'

[Execution output shows match score and operations]

✅ Generated 10 tailoring operations:
   - Update title to match job
   - Add missing skills (aws, azure, zuora, etc.)
   - Add relevant expertise areas
   - Suggestions for manual updates

Would you like me to execute these operations to update your resume?

You: Yes, execute them

Agent: [Executes operations one by one with progress feedback]

✅ Successfully updated your Ford resume!
   - Title updated
   - Added 12 new skills
   - Added 3 expertise areas
   - 2 manual suggestions provided

Would you like me to export the resume to HTML/DOCX?

Example 2: Agent API Usage

import requests

# Chat with agent
response = requests.post('http://localhost:5000/api/agent/chat', json={
    'message': 'Tailor my Ford resume for the GM position'
})

print(response.json()['response'])

# Get agent memory
memory = requests.get('http://localhost:5000/api/agent/memory')
print(f"Memory: {len(memory.json()['memory'])} messages")

Best Practices

  1. Always start with dry-run mode to review operations before executing
  2. Review match scores to understand resume gaps
  3. Manually review suggestions for summary and experience updates
  4. Test with sample data before modifying production resumes
  5. Keep backups of resume JSON files before bulk operations
  6. Use version control to track resume changes over time

Troubleshooting

Issue: Low Match Score

Solution: Review missing skills and add them using CRUD operations:

python src/crud/technical_skills.py --resume "Ford" --append-to-category "cloud" "aws, azure, gcp"

Issue: Parser Not Finding Skills

Solution: Check job posting format and ensure it follows expected structure:

  • Title on first line
  • Company on second line
  • Skills in "Required Skills" or "Your Skills & Abilities" section

Issue: Operations Not Executing

Solution: Ensure resume name is correct and exists in index:

cat data/resumes/index.json | grep -i "ford"

Next Steps