Documentation Index
Fetch the complete documentation index at: https://mintlify.com/Nectr-AI/nectr-ai-pr-review-agent/llms.txt
Use this file to discover all available pages before exploring further.
Overview
Retrieve comprehensive contributor profiles built from Mem0 AI memory, including developer patterns, strengths, and historical activity. This endpoint is designed for team leaders to understand their team’s expertise and contribution patterns.
Authentication
Requires a valid JWT token in the Authorization header:
Authorization: Bearer YOUR_JWT_TOKEN
Query Parameters
Repository to query in owner/repo format (e.g., “acme/api-server”)
Page number for pagination (1-indexed)
Number of contributors per page (1-100)
Response
Repository full name (owner/repo)
Total number of contributors with AI memory
Array of contributor profiles
contributors[].profile_summary
AI-generated profile summary of the developer’s contribution style
Array of identified developer patterns (coding habits, preferences, common approaches)
Array of developer strengths (areas of expertise, technical skills)
Total number of PRs submitted by this contributor
contributors[].commit_count
Total number of commits by this contributor
contributors[].last_seen_pr
Most recent PR number from this contributor
Example Request
curl -X GET "https://api.nectr.ai/api/v1/analytics/contributors?repo=acme/api-server&page=1&per_page=10" \
-H "Authorization: Bearer YOUR_JWT_TOKEN"
Example Response
{
"repo": "acme/api-server",
"contributor_count": 12,
"page": 1,
"per_page": 10,
"total_pages": 2,
"contributors": [
{
"username": "johndoe",
"profile_summary": "Experienced backend engineer focused on API design and database optimization. Consistently delivers well-tested, production-ready code with comprehensive documentation.",
"patterns": [
"Prefers comprehensive error handling with custom exception classes",
"Always includes unit tests and integration tests for new features",
"Favors async/await patterns for database operations"
],
"strengths": [
"Database schema design and optimization",
"RESTful API architecture",
"Performance profiling and optimization"
],
"pr_count": 47,
"commit_count": 312,
"last_seen_pr": 42
},
{
"username": "janedoe",
"profile_summary": "Frontend specialist with strong TypeScript skills. Known for creating polished, accessible user interfaces with excellent attention to detail.",
"patterns": [
"Uses functional React components with hooks",
"Implements comprehensive accessibility features (ARIA labels, keyboard navigation)",
"Prefers composition over inheritance for component architecture"
],
"strengths": [
"TypeScript type safety and advanced patterns",
"CSS-in-JS and responsive design",
"State management with Redux and Context API"
],
"pr_count": 38,
"commit_count": 241,
"last_seen_pr": 39
}
]
}
Memory Types
The contributor data is built from three types of AI memories:
contributor_profile
High-level summary of the developer’s contribution style, generated from analyzing their PR history.
developer_pattern
Specific coding patterns and habits identified across multiple PRs:
- Architectural preferences
- Code organization style
- Testing approaches
- Common techniques
developer_strength
Technical areas where the developer excels:
- Technology expertise
- Problem-solving domains
- Architectural skills
Error Responses
Repository Not Connected
{
"detail": "Repo not connected or access denied"
}
403 Forbidden
Memory Layer Unavailable
{
"repo": "acme/api-server",
"contributor_count": 0,
"contributors": [],
"note": "Memory layer not configured"
}
200 OK (graceful degradation)
Use Cases
Team Overview Dashboard
data = get_contributors(repo="acme/api-server")
print(f"Team Overview - {data['repo']}")
print(f"Total Contributors: {data['contributor_count']}")
print()
for contributor in data['contributors'][:5]:
print(f"👤 {contributor['username']}")
print(f" PRs: {contributor['pr_count']} | Commits: {contributor['commit_count']}")
print(f" Profile: {contributor['profile_summary']}")
if contributor['strengths']:
print(f" Strengths: {', '.join(contributor['strengths'][:3])}")
print()
Code Review Assignment
def suggest_reviewer(pr_files, contributors):
"""Suggest the best reviewer based on file expertise."""
for contributor in contributors:
for strength in contributor['strengths']:
if any(tech in strength.lower() for tech in ['database', 'sql']):
if any('db' in f or 'models' in f for f in pr_files):
return contributor['username']
return contributors[0]['username'] # fallback to most active
data = get_contributors(repo="acme/api-server")
pr_files = ['app/models/user.py', 'app/db/migrations/001.sql']
reviewer = suggest_reviewer(pr_files, data['contributors'])
print(f"Suggested reviewer: {reviewer}")
Skill Gap Analysis
data = get_contributors(repo="acme/api-server")
all_strengths = {}
for contributor in data['contributors']:
for strength in contributor['strengths']:
all_strengths[strength] = all_strengths.get(strength, 0) + 1
print("Team Skills Coverage:")
for skill, count in sorted(all_strengths.items(), key=lambda x: x[1], reverse=True):
coverage = (count / data['contributor_count']) * 100
print(f"{skill}: {count} devs ({coverage:.0f}% coverage)")
def get_all_contributors(repo):
"""Fetch all contributors across all pages."""
all_contributors = []
page = 1
while True:
data = get_contributors(repo=repo, page=page, per_page=20)
all_contributors.extend(data['contributors'])
if page >= data['total_pages']:
break
page += 1
return all_contributors