Amor

About Amor

Amor is a specialized, data-driven sourcing platform designed to identify and connect with the top tier of software engineering talent directly through their activity on GitHub. It addresses a critical gap in technical recruitment by moving beyond traditional resume and LinkedIn-based searches to analyze real, verifiable coding contributions. The platform processes a massive dataset of over 8 million developer profiles, 66 million repositories, and 145 billion stars to surface engineers based on objective metrics like commit frequency, project involvement, and coding consistency. Its core value proposition is enabling recruiters, engineering managers, and sourcers to find highly skilled, actively contributing engineers—including those who are not actively networking on professional social platforms—with unprecedented speed and precision. By applying advanced filters for commit patterns, cleaned location data, and specific repository contributions, Amor transforms the chaotic public data of GitHub into a structured, actionable sourcing database, saving teams hundreds of hours otherwise spent on manual scripting and profile screening.

Features of Amor

Advanced GitHub Profile Filtering

Amor provides granular search capabilities that go beyond basic keyword matching. Users can filter engineers based on specific commit activity thresholds, weekend commit patterns (indicating passion projects or deep work ethic), contributions to particular high-value repositories (e.g., nextjs, astro, remix), and programming languages. This allows for constructing highly targeted queries, such as finding front-end engineers in a specific city who regularly contribute to modern JavaScript frameworks, ensuring candidates meet both technical and circumstantial criteria.

Smart Location Data Cleansing

GitHub location fields are notoriously noisy, containing entries like "Earth," "The Cloud," or inconsistent city names. Amor employs data cleaning algorithms to normalize this information, enabling reliable searching by actual city, region, and country. This feature eliminates guesswork and allows recruiters to pinpoint candidates in specific geographical areas for local roles or to understand talent distribution for remote positions, making location-based sourcing both possible and accurate.

Repository and Contribution Insights

The platform automatically analyzes a developer's public activity to generate a summary of their technical interests and project focus. It showcases the types of repositories they contribute to, the projects they star, and provides an overview of their coding footprint. This gives a hiring manager an immediate, skimmable understanding of whether an engineer's experience aligns with their tech stack and domain, moving beyond a simple commit count to assess relevance and passion.

Candidate List Management & Ashby Export

Amor facilitates collaborative hiring workflows. Users can create, share, and comment on curated lists of candidates within their team. Crucially, it features a one-click export function that formats candidate data into an Ashby-compatible CSV. This export is auto-enriched with emails parsed from commit histories, LinkedIn URLs, and smart tags based on source repositories, enabling seamless import into Applicant Tracking Systems (ATS) and drastically reducing administrative overhead.

Use Cases of Amor

For In-House Technical Recruiters Scaling Teams

In-house recruiters at scaling startups and tech companies use Amor to build robust pipelines of pre-vetted engineering talent. By filtering for consistent commit history and contributions to relevant open-source projects, they can identify candidates with proven technical chops and a strong work ethic before initial contact. This data-centric approach reduces time-to-hire and improves the quality of candidates entering the interview funnel, ensuring a higher bar from the first screening call.

For Recruiting Agencies Placing Specialized Talent

Technical recruiting agencies leverage Amor to gain a competitive edge by accessing a candidate pool often invisible on LinkedIn. They can source passive, highly skilled engineers who are deeply engaged in their craft on GitHub but may not be actively job-seeking. This allows agencies to present exclusive, high-quality candidates to clients faster than competitors relying on conventional methods, accelerating placement cycles and improving client satisfaction.

For Engineering Managers Building High-Performance Teams

Engineering managers utilize Amor to take a hands-on role in sourcing candidates who will thrive in their specific technical and cultural environment. By reviewing contribution patterns and project summaries, managers can assess not just skill fit but also indicators of passion, consistency, and potential team alignment. This empowers them to proactively seek out engineers whose public work demonstrates the exact expertise and drive their projects require.

For Sourcing Underrepresented and Niche Talent

Talent sourcers focused on diversity initiatives or finding experts in niche technologies use Amor's precise filters to uncover talent from underrepresented communities or specialized domains. By searching for contributions to specific, lesser-known repositories or in particular global regions, sourcers can build more diverse and technically specific candidate slates, moving beyond the homogeneous networks often surfaced by traditional platforms.

Frequently Asked Questions

How does Amor define a "Top 1%" engineer?

Amor's "Top 1%" metric is primarily based on contribution volume and consistency. The platform identifies over 100,000 engineers with more than 500 contributions in the past year, a threshold that signifies exceptional activity and engagement on GitHub. This is supplemented by analysis of the quality and relevance of repositories they contribute to, providing a data-backed proxy for high output and dedication.

What kind of data enrichment does Amor provide for exports?

When exporting candidate lists, Amor automatically enriches profiles with professional contact information and context. This includes email addresses parsed from commit histories, discovered LinkedIn profile URLs, and smart tags that categorize candidates based on the source repositories they contributed to and the candidate list names they belong to, facilitating easy organization within an ATS like Ashby.

Can I use Amor to find engineers for very specific tech stacks?

Yes, this is a core functionality. You can filter engineers based on their contributions to specific repositories (e.g., vercel/next.js, withastro/astro) and by primary programming languages. This allows you to target candidates who have demonstrable, recent experience with the exact frameworks, libraries, or tools your role requires, moving beyond claimed skills on a resume to verified activity.

How does Amor handle data privacy and GitHub's terms of service?

Amor operates by analyzing publicly accessible data from GitHub's API, similar to how a user would manually browse profiles. The platform complies with GitHub's Terms of Service and data usage policies. It does not store or expose private repository information, emails, or any non-public data. The email enrichment for exports is performed by parsing publicly visible email addresses from commit logs, which developers have chosen to make public.

Pricing of Amor

Amor offers a simple, transparent pricing structure with two main tiers, billed annually, and a free plan for initial exploration.

Free Plan: Priced at $0, this plan allows users to try the platform with limited search results. It is always free and serves as an introduction to Amor's core filtering capabilities.

Solo Plan: Priced at $48 per month (billed annually), this plan is designed for individual power users. It unlocks full search results, enables users to find specific repository contributors, includes full email enrichment for candidate profiles, and provides access to all export functionalities, including the one-click export to Ashby format.