What Is the Skills Passport? AIEH's Candidate-Owned Credential Explained

The Skills Passport is AIEH’s name for the candidate-owned, calibrated, multi-vendor credential that sits at the center of the AI Employment Hub. It’s a single number on a 300–850 scale, backed by a four-pillar breakdown, sourced from any combination of validated assessments the candidate has taken — and crucially, the URL belongs to the candidate, not to a vendor.

This article walks through what the Skills Passport actually is, why AIEH built it, how the four pillars and the calibrated scale work, and how multi-vendor aggregation makes a HackerRank score and an IPIP personality result and an AIEH-native AI Output Evaluation roll up into one comparable composite.

Data Notice: Validity coefficients and assessment science referenced here reflect peer-reviewed selection-research at time of writing. Specific weights and decay parameters are documented in the scoring methodology and may evolve as calibration data accrues during launch.

Why AIEH built the Skills Passport

The dominant pattern in modern hiring is that every recruiter runs their own assessment stack. Engineering hiring loop: a HackerRank coding test plus a behavioral interview plus sometimes a take-home. ML hiring loop at a different employer: a CodeSignal coding round plus a case-study presentation. UX hiring at a third: maybe iMocha or Mercer Mettl plus a portfolio review. Sales: Caliper or DDI. The candidate takes each test fresh, the score lives in the employer’s vendor account, and the next time the candidate applies somewhere else, the work starts over.

This produces three structural problems that AIEH set out to fix:

Repeated friction. Strong candidates with multiple options absorb hours of assessment burden per application. The candidates who most flexibly absorb this — early-career, no caregiving responsibilities, schedules that accommodate midweek 90-minute take-homes — aren’t always the candidates whose underlying capability is strongest.
Apples-to-oranges comparison. Recruiters comparing two candidates who took different assessment platforms can’t meaningfully compare the scores. “She got 720 on HackerRank” vs. “He got 1450 on CodeSignal” produces no defensible ranking. Hiring decisions default to “the one we know” or “the one whose recruiter pushed harder.”
Vendor lock-in. The score lives in the vendor’s database, accessible via a vendor account. The candidate doesn’t own it; the employer who paid for the assessment does. Moving the score to a different employer’s pipeline means re-paying for re-assessment.

The Skills Passport addresses these by making the credential candidate-owned, vendor-agnostic, and calibrated to a common scale.

The four pillars

AIEH’s Skills Passport composite is built on four pillars, each with a published default weight (configurable per role bundle):

Cognitive (0.25 default weight). General mental ability — the broad reasoning, learning-rate, and problem-solving capacity that selection-research literature has documented for over a century as the highest-validity single predictor of job performance for most knowledge work. See cognitive-ability in hiring for the underlying validity evidence.
Domain (0.35 default weight). Role-specific knowledge and skills. For a Software Engineer this might combine Python Fundamentals, JavaScript Fundamentals, AI-Augmented SQL, and a structured technical-screen result. For a UX Designer it might combine portfolio-review evidence, Communication scenarios, and design-judgment situational items. The pillar is the highest-weighted in the default composite because role-specific evidence is the most diagnostic for role performance.
AI fluency (0.25 default weight). AI Collaboration Literacy (eval design, prompt-to-spec translation, model-handoff communication) plus AI Output Evaluation (judging model outputs against rubrics, distinguishing fluent-and-wrong from halting-and-right). AI fluency is meaningfully different from general cognitive or domain-skill measurement; the pillar exists because modern knowledge work increasingly involves working alongside AI systems.
Communication (0.15 default weight). Written and verbal communication skill — covered through Communication scenario assessments and (for some roles) work-sample evidence. Lower default weight than the other three because the role-specific dependence is high and weighting it uniformly across all bundles would over-weight it for technical-IC roles where it matters less.

The default weights apply to the modal AIEH role bundle. Specific roles can override the weights — a senior leadership role weights communication higher, a research scientist role weights cognitive higher. The role pages document each role’s bundle composition; see ai-product-manager for an example.

For full coverage of how the weights compose into the final composite plus the recency-decay model, see the scoring methodology.

The 300–850 calibrated scale

The Skills Passport composite produces a single number on a 300–850 scale. The choice is deliberate: it mirrors the FICO credit score range that a generation of consumers has internalized as a calibrated measure (without any of the financial implications). Three reference points calibrate the scale:

300–550: below baseline. Substantial gaps in evidence; not yet ready for senior-role consideration.
550–700: baseline-to-strong. Most working professionals fall in this range; differentiation within the range is meaningful for hiring decisions.
700–850: strong-to-exceptional. Top decile or above on the underlying assessment evidence; meaningful differentiator for competitive senior roles.

The scale is calibrated so a 720 in one role bundle is directly comparable to a 720 in another role bundle. This matters because it lets recruiters compare candidates apples-to-apples even when they came in via different role funnels — a Frontend Engineer 720 and a Backend Engineer 720 are both top-quartile-strong against their respective role bundles.

Recency decay is built into the composite. Cognitive ability decays slowly (~5-year half-life because cognitive-ability stability across the lifespan is high); domain skills decay faster (~12-18 months because language norms, frameworks, and ecosystem tools shift); personality traits decay slowly (~5-year half-life). The decay model matches reality: a Python score from 2019 carries less weight than a Python score from 2024.

Why “candidate-owned” matters

Every Skills Passport has a public URL of the form aieh.com/passport/{handle}. The candidate owns it. They share the URL on LinkedIn, in email, on personal websites. Recruiters click the URL and see the calibrated composite plus pillar breakdown plus underlying assessment-source provenance.

This is structurally different from how vendor assessment results currently work:

Vendor-locked credential: A candidate’s HackerRank score lives in HackerRank’s database, accessible through HackerRank’s recruiter platform when an employer has paid for HackerRank seats. The candidate can’t take the score to a different employer who isn’t a HackerRank customer.
Candidate-owned credential: A candidate’s Skills Passport is a URL the candidate controls. Any employer who clicks it sees the same evidence. No vendor account required, no per-employer re-assessment fee, no “well, we don’t use that platform” mismatch.

The candidate-owned framing is what enables the Phase 2 AIEH product to function as a credential rather than as just-another-assessment-platform. See skills-based hiring evidence for the broader treatment of skills-based credential patterns and how they compare to traditional credentials.

Multi-vendor aggregation

A Skills Passport can aggregate evidence from multiple assessment sources. Concrete example: a candidate applying for an AI Product Manager role might have:

HackerRank Python score (vendor-hosted)
IPIP Big Five Personality result (AIEH-native)
AIEH AI Collaboration Literacy result (AIEH-native)
AIEH AI Output Evaluation result (AIEH-native)
Communication scenario score (AIEH-native)

The Skills Passport composite weights each source according to the AI Product Manager bundle’s defaults (see ai-product-manager for the specific weights), normalizes to the 300–850 scale, applies recency decay, and produces one composite number plus the four-pillar breakdown.

The aggregation logic doesn’t require all sources. A candidate with only HackerRank evidence still gets a Skills Passport — but the composite is appropriately weighted to reflect that some pillars have more evidence than others. The weighting framework distinguishes “strong evidence on one pillar, missing on others” from “strong evidence across all four pillars,” and recruiters clicking the Passport see both the composite and the provenance for each pillar.

This multi-vendor structure is why AIEH integrates with HackerRank, CodeSignal, iMocha, and similar assessment-vendor platforms rather than competing head-to-head with them. Vendors produce specific assessments; AIEH produces the candidate-owned credential that aggregates evidence across them. See the hire workspace for the broader recruiter-side view of how Skills Passport evidence integrates into hiring decisions.

Practical example: hiring an AI Product Manager

Consider a recruiter hiring an AI Product Manager. The ai-product-manager role page documents the recommended assessment bundle and the relevance weights. Two candidates apply:

Candidate A: HackerRank Python score 78%, took the AIEH AI Collaboration Literacy full assessment scoring in the high band, completed the AIEH Communication scenarios scoring in the mid band. No Big Five data. Composite Skills Passport score: 695.
Candidate B: No HackerRank data. Took the AIEH AI Collaboration Literacy and AI Output Evaluation full assessments scoring in the strong band, completed the AIEH Communication scenarios scoring in the strong band, completed Big Five Personality scoring at high conscientiousness and openness. Composite Skills Passport score: 720.

The recruiter sees both Skills Passports side by side. Both candidates are in the strong band for AI Product Manager work; Candidate B has more evidence on the AI-fluency pillars (which are weighted at 0.25 in this bundle) and stronger personality fit, while Candidate A has somewhat stronger general technical evidence (HackerRank Python). The recruiter has objective, calibrated evidence to decide the next step rather than “well, A’s resume is more impressive.”

This is the use case the Skills Passport is built for: not replacing recruiter judgment, but giving recruiters calibrated evidence so the judgment decisions are made on the right input.

Takeaway

The Skills Passport is AIEH’s candidate-owned, calibrated, multi-vendor credential. Four pillars (cognitive, domain, AI fluency, communication) compose into a 300–850 score that decays with recency. The credential is candidate-owned (URL controlled by the candidate, not a vendor account), multi-vendor (aggregates evidence from HackerRank, CodeSignal, iMocha, AIEH-native, plus future provider integrations), and calibrated (a 720 in one role bundle is directly comparable to a 720 in another).

For deeper coverage of related concepts, see the scoring methodology for the calibration math, skills-based hiring evidence for the broader research base, the hire workspace for recruiter-side flow, and the ai-product-manager role page for a concrete example of how the four-pillar composition applies to a specific role.

Sources

Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychological Bulletin, 124(2), 262–274.
Sackett, P. R., & Lievens, F. (2008). Personnel selection. Annual Review of Psychology, 59, 419–450.
Roberts, B. W., Walton, K. E., & Viechtbauer, W. (2006). Patterns of mean-level change in personality traits across the life course. Psychological Bulletin, 132(1), 1–25.
Hough, L. M., & Oswald, F. L. (2008). Personality testing and industrial-organizational psychology: Reflections, progress, and prospects. Industrial and Organizational Psychology, 1(3), 272–290.