repo-people 0.1.0

Collect and export full GitHub user profile data for everyone associated with a repository.

repo-people

repo-people is a Python package that collects and exports the full GitHub profile for every person associated with a repository — contributors, maintainers, stargazers, watchers, issue/PR authors, fork owners, commit authors and dependents.

Table of Contents

• Introduction
• Background
• Requirements
• Installation
• Documentation
• Usage
• Directories
• Issues
• License
• Contact

---

Introduction

repo-people provides a single-call pipeline to collect every GitHub user associated with a repository across 9 role categories, fetch 30+ profile fields for each person from the GitHub API, and export the results to JSON, CSV, or Markdown. It is designed for research, open-source community analysis, and developer intelligence workflows.

Key capabilities:

• Collects users from 9 role categories in a single call
• Fetches 30+ profile fields per user (bio, location, company, followers, orgs, languages, …)
• Computes derived metrics: account age, followers/following ratio, repos/year, recently-active flag, bot detection
• Incremental fetch with save_each_iteration and resume — safe to interrupt and restart on large repos
• Flexible filtering: roles, exclude, exclude_bots, limit, fields
• Concurrent fetching via workers — uses ThreadPoolExecutor to fetch multiple profiles in parallel
• Async fetching via get_users_async() — uses asyncio + aiohttp for high-concurrency scenarios
• Opt-in social accounts via include_social_accounts — fetches linked LinkedIn, Mastodon, npm, and other accounts
• Export to JSON, CSV and Markdown table
• Analysis helpers: summarise() and top_users()
• Token validated on startup — invalid or expired tokens raise ConnectionError immediately
• Rate-limit progress printed every 50 users with remaining request count and reset time

---

Background

Understanding who contributes to, uses, and maintains an open-source project is valuable for community health analysis, academic research, and competitive intelligence. GitHub exposes this information across many endpoints (contributors, stargazers, watchers, forks, issues, pull requests, CODEOWNERS, commit history), but collecting and joining it requires many paginated API calls.

repo-people automates that collection, deduplicates users across all roles, enriches each record with the full GitHub profile, and computes additional signals (account age, activity recency, bot detection) in a single pipeline call.

---

Requirements

• Python ^3.9
• PyGithub ^2.0.0 — GitHub API client
• requests ^2.31.0 — HTTP requests for REST endpoints
• beautifulsoup4 ^4.12.0 — HTML scraping for dependents
• aiohttp ^3.9 — async HTTP client for get_users_async()

A GitHub personal access token is strongly recommended. Unauthenticated requests are limited to 60/hour; authenticated requests allow 5,000/hour.

---

Installation

Install the latest version of pySAR via [PyPi][PyPi] using pip:

pip3 install pysar --upgrade

Installation from source:

git clone -b master https://github.com/amckenna41/pySAR.git
cd pySAR
pip3 install .

---

Documentation

• Read the Docs — full package documentation
• FIELDS.md — full reference table of all 48 output fields with descriptions
• CHANGELOG.md — version history and release notes

---

Usage

Quick Start

from repo_people import RepoPeople

rp = RepoPeople("owner", "repo", token="ghp_...")
user_data = rp.get_users(export=True)
# Returns a dict keyed by username, with 30+ profile fields per user

Authentication

import os
rp = RepoPeople("owner", "repo", token=os.environ["GITHUB_TOKEN"])

The token is validated immediately on construction — an invalid or expired token raises ConnectionError before any collection begins.

RepoPeople() Constructor

RepoPeople(owner, repo, token=None, outdir=None, skip_codeowners=False, skip_collaborators=False)

Parameter	Type	Default	Description
owner	str	—	GitHub username or organisation that owns the repo.
repo	str	—	Repository name.
token	str | None	None	Personal access token. Strongly recommended — validated immediately on init; raises ConnectionError for invalid tokens.
outdir	str | None	"{owner}_{repo}"	Leaf directory inside outputs/. All output files are written under outputs/{outdir}/.
skip_codeowners	bool	False	Skip CODEOWNERS file when collecting maintainers.
skip_collaborators	bool	False	Skip repo collaborators when collecting maintainers.

get_users() Parameters

Parameter	Type	Default	Description
export	bool	False	Write results to a JSON file.
export_csv	bool	False	Write results to a CSV file.
save_each_iteration	bool	False	Save after every single user fetch.
limit	int | None	None	Cap the number of profiles to fetch.
roles	list[str] | None	None (all 9)	Restrict which roles to collect.
exclude	list[str] | None	None	Usernames to skip.
exclude_bots	bool	False	Skip bot accounts automatically.
resume	bool	False	Skip users already in the output file.
verbose	bool	True	Print progress to stdout.
fields	list[str] | str | None	None (all)	Restrict which fields appear in output. Invalid names raise ValueError before any fetch.
include_social_accounts	bool	False	Fetch each user's linked social accounts (LinkedIn, Mastodon, npm, …). Costs one extra API call per user.
workers	int	1	Number of concurrent fetch threads. Increase for faster collection on large repos.

Valid roles values: contributors, maintainers, stargazers, watchers, issue_authors, pr_authors, fork_owners, commit_authors, dependents.

Examples

Filter by role

# Only gather contributors and stargazers
user_data = rp.get_users(roles=["contributors", "stargazers"])

Limit, exclude, and skip bots

user_data = rp.get_users(
    limit=100,
    exclude=["dependabot", "github-actions[bot]"],
    exclude_bots=True,
)

Export to JSON and CSV

user_data = rp.get_users(export=True, export_csv=True)

Export to Markdown table

rp.export_to_markdown(user_data, fields=["login", "name", "location", "followers"])

Resume an interrupted run

# First run
rp.get_users(save_each_iteration=True, export=True)

# Resume after interruption
rp.get_users(save_each_iteration=True, export=True, resume=True)

Concurrent fetching

# Speed up large repos by fetching profiles in parallel
user_data = rp.get_users(workers=4)

Async fetching

import asyncio

user_data = asyncio.run(rp.get_users_async(concurrency=10))

Include social accounts

user_data = rp.get_users(include_social_accounts=True)
# Each record gains a 'social_accounts' dict, e.g. {'linkedin': 'https://linkedin.com/in/...'}

Analysis helpers

stats = rp.summarise(user_data, top_n=5)
# {'total': 134, 'top_locations': [('San Francisco', 18), ...], ...}

leaders = rp.top_users(user_data, n=10, by="followers")

Output Fields

Each user entry contains 30+ fields. See FIELDS.md for the full reference. A summary by category:

Category	Fields
Identity	login, name, company, location, email_public, blog, twitter, bio
Timestamps	created_at, updated_at
Counters	followers, following, public_repos, public_gists
Flags	has_public_email, has_blog, has_twitter, is_bot, hireable
Computed	account_age_days, followers_following_ratio, repos_per_year, recently_active, last_public_event_at
Organisations	public_orgs, orgs_public_count
Sampled	top_languages, total_public_stars_sampled, total_public_forks_sampled, ssh_keys_count, gpg_keys_count, starred_repos_sampled
Social	social_accounts (opt-in via include_social_accounts)
Repo-specific	is_collaborator, permission_on_repo
Metadata	roles (populated by get_users())

---

Directories

repo-people/
├── repo_people/          # Package source
│   ├── __init__.py
│   ├── repo_people.py    # RepoPeople class — main pipeline
│   ├── export.py         # Role-specific username collectors (9 functions)
│   ├── users.py          # GitHubUserInfo wrapper and UserSnapshot dataclass
│   └── utils.py          # Shared helpers: paginate(), _headers(), write_csv()
├── tests/                # Unit and integration tests
│   ├── test_repo_people.py
│   ├── test_export.py
│   └── test_users.py
├── docs/                 # Sphinx documentation source
├── outputs/              # Default output directory (created at runtime)
├── FIELDS.md             # Full output field reference
├── CHANGELOG.md          # Version history
├── pyproject.toml        # Package metadata and dependencies
└── README.md

Output Fields

Each user entry contains 30+ fields including:

Category	Fields
Identity	login, name, company, location, email_public, blog, twitter, bio
Timestamps	created_at, updated_at
Counters	followers, following, public_repos, public_gists
Flags	has_public_email, has_blog, has_twitter, is_bot, hireable
Computed	account_age_days, followers_following_ratio, repos_per_year, recently_active, last_public_event_at
Organisations	public_orgs, orgs_public_count
Sampled	top_languages, total_public_stars_sampled, total_public_forks_sampled, ssh_keys_count, gpg_keys_count, starred_repos_sampled
Social	social_accounts (opt-in via include_social_accounts)
Repo-specific	is_collaborator, permission_on_repo
Metadata	roles (populated by get_users())

Issues

Bugs and feature requests are tracked on GitHub Issues.

When reporting a bug, please include:

• Python version (python --version)
• Package version (pip show repo-people)
• A minimal code snippet that reproduces the issue
• The full traceback if an exception is raised

License

Distributed under the MIT License. See MIT for more details.

Contact

AJ McKenna — [e-mail]

---

Back to top