How Many Recruiters Will Actually Call You After Seeing Your GitHub?
Our predictor analyzes your GitHub profile against the 12 signals that recruiters evaluate in their first 30 seconds: commit depth, repo originality, README quality, deploy URLs, contribution frequency, language diversity, and more. Output: an estimated recruiter engagement score and specific improvement recommendations.
What Recruiters Actually Evaluate in 30 Seconds
A technical recruiter spends 30 seconds on your GitHub profile before deciding whether to read your code or move to the next candidate. In those 30 seconds, they pattern-match against 12 signals that predict technical interview performance. Our predictor replicates this evaluation: it scans your GitHub profile for each signal, scores it against our database of profiles that received interview calls versus profiles that were rejected, and estimates your recruiter engagement probability. The output is not a grade — it is a probability estimate paired with the specific signals that are dragging it down.
The 12 signals we evaluate: commit frequency and recency (are you coding regularly or did activity stop 6 months ago?), commit depth per repository (single-commit dumps versus incremental development), repository originality (custom projects versus tutorial clones — we check for common tutorial repository patterns), README completeness (does each repo have a README that explains architecture, setup, and deployment?), deploy URL presence (are your projects live and clickable?), contribution graph density (green squares tell a story of consistency), language diversity (deep expertise in 1-2 languages signals more than superficial breadth in 8), open-source contribution count (merged PRs to non-personal repositories), follower and star counts (social proof of peer recognition), pinned repository selection (are your best projects pinned to your profile?), organization membership evidence (team collaboration outside personal projects), and commit message quality (do your messages describe what and why, or are they 'update' and 'fix'?). The predictor scores each signal and identifies the three that would most increase your engagement probability if improved.
The predictor is not a guarantee of interview calls. It is a diagnostic that tells you which signals on your GitHub profile are working and which are hurting you. A profile with 8 tutorial clones and zero deploy URLs will receive a low engagement probability — not because the predictor is harsh, but because recruiters reject such profiles 95% of the time. The value is not the score. The value is knowing exactly which three changes would most increase it.
System Comparison
| SIGNAL CATEGORY | WHAT HURTS YOUR SCORE | WHAT HELPS YOUR SCORE |
|---|---|---|
| Commit Patterns | Single-commit repos. Activity stopped >3 months ago. Weekend bursts followed by weeks of silence. | 30+ commits per project over 3+ weeks. Consistent activity. Descriptive commit messages following conventional format. |
| Repository Quality | Tutorial clones (Todo, Weather, Netflix, Chat). Auto-generated READMEs. No deploy URLs. | Original projects solving real problems. Architecture-explaining READMEs. Live deploy URLs that actually work. |
| Community Proof | Zero open-source contributions. Zero followers/stars. Profile looks like a solo effort with no external validation. | 1+ merged PR in non-personal repos. Small but genuine follower base. Evidence of collaboration outside personal projects. |
Frequently Asked Questions
How accurate is the engagement probability estimate?
It is a statistical estimate based on pattern-matching against our database of profiles with known interview outcomes. It is directionally accurate — a low score reliably indicates a profile that recruiters will reject, and a high score reliably indicates a profile that will get read. The specific percentage is less important than the gap analysis: knowing which signals are dragging your score down is the actionable output.
Will this tell me if my actual code is good?
No. The predictor evaluates recruiter-visible signals that determine whether a recruiter reads your code. It does not evaluate code quality. A profile with excellent recruiter signals but poor code will get interviews but fail them. A profile with poor signals but excellent code will never get the interview where that code could be evaluated. The predictor fixes the first problem. Our code audit (comprehensive bundle) fixes the second.
Predict Your GitHub Recruiter Engagement
Submit your GitHub username. Our predictor scans your profile against the 12 recruiter-evaluated signals and estimates your engagement probability. Output includes the three highest-impact improvements you can make this weekend.
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