Originally published September 20, 2023. Updated to clarify the role of STAMP within Gobekli’s Human Intelligence Layer and its relationship with the broader Learning and Employment Record ecosystem.

Organizations collect more talent data than ever.

Résumés list experience. Learning systems record course completion. Credential platforms issue badges and certificates. HR systems maintain job histories and performance information. Project platforms capture activity. Skills tools attach standardized labels to people, jobs, and learning programs.

Yet most organizations still struggle to answer basic questions:

• What can this person actually do?
• Where did that capability come from?
• How has it been demonstrated?
• In what context was it used?
• How current is it?
• How does it transfer to another role or environment?
• What could this person become next?

The problem is not simply a shortage of data.

The problem is that human capability loses much of its meaning when it is separated from the experiences, relationships, evidence, responsibilities, and conditions that created it.

Gobekli developed the idea of the Standardized Talent Asset Mapping Protocol, or STAMP, to explore how richer human information could be structured without reducing people to lists of skills.

STAMP is a developing conceptual and technical framework—not an independently governed or broadly adopted public standard. It continues to inform how Gobekli approaches the Talent Tree, TalentPass, TalentSync, and the larger Human Intelligence Layer.

Its central premise is simple:

Human capability should be mapped through relationships and context, not represented as a collection of disconnected labels.

Why Skills Tags Are Not Enough

Skills tags can be useful. They help organize information, support search, and create a shared vocabulary for describing jobs, courses, credentials, and people.

But a tag alone tells us very little.

Consider the skill leadership.

Was it developed by supervising a large team, organizing volunteers, leading a classroom project, serving in the military, caring for family members, or founding a business?

What decisions did the person make? What responsibility did they carry? Who was affected? What evidence demonstrates the result? Was the experience recent? Was the person formally recognized, or are they describing an experience that has never been credentialed?

The same skill label can represent radically different experiences.

Even a technical skill cannot be understood fully without context. Knowing that someone has used a programming language does not tell us what they built, how independently they worked, how complex the environment was, how long they used it, or whether they can apply it in a new setting.

When organizations treat a skill tag as a complete description of capability, they create a false sense of precision.

STAMP begins with the opposite assumption: the relationships surrounding a capability are part of its meaning.

From Skills Lists to Talent Maps

STAMP was conceived as a method for representing talent as a connected map.

Rather than attaching skills directly to a person as permanent labels, a richer model can connect them with elements such as:

• Roles a person has held
• Activities they performed
• Projects and challenges they completed
• Evidence of their work
• Outcomes and measures
• Credentials and assessments
• Endorsements and recognition
• The environments in which something occurred
• The amount of time involved
• How recently a capability was used
• The people and teams involved
• The person’s own reflection and context

This produces a more defensible statement.

Instead of simply claiming that someone possesses “project management,” a connected record could show that the person coordinated a particular initiative, identify the responsibilities involved, connect supporting evidence, document the outcome, and show which organization or individual verified parts of the record.

The skill remains useful—but it is no longer asked to carry the entire meaning by itself.

What Is a Talent Asset?

In the original STAMP concept, a talent asset is a reusable piece of information that contributes to an understanding of a person’s capability and potential.

A talent asset might include:

• An experience
• An activity
• A skill or area of knowledge
• A credential
• A work sample
• An assessment
• An accomplishment
• An endorsement
• A reflection
• A goal
• A professional or community relationship
• A measurable outcome

These assets can be combined differently for different purposes.

The same project might contribute to a job application, professional portfolio, learning plan, promotion conversation, team profile, or credential application. A person should not need to recreate the entire story each time.

This does not mean every data point should be shared universally. A person may choose different assets for different situations while keeping the larger record private.

The goal is reusable context—not unlimited access.

Activities Provide the Missing Anchor

One of the most important ideas behind STAMP is that activities can serve as an anchor between people, roles, skills, evidence, and outcomes.

Job titles are often too broad. Skills are often too abstract. Credentials can confirm learning or achievement without showing every way it has been applied.

Activities help connect these levels.

For example:

Role: Operations manager
Activity: Redesigned the onboarding workflow for new employees
Evidence: Workflow documentation and implementation plan
Outcome: Reduced time required for new employees to reach independent work
Capabilities involved: Process improvement, communication, facilitation, change management, and operational judgment
Context: Cross-functional project involving HR, managers, and frontline employees

This structure provides more useful information than a title or skills list alone. It explains how capabilities appeared in practice and gives people reviewing the record a basis for asking better questions.

Activities also make it easier to recognize learning that happens outside formal programs. Military service, caregiving, volunteering, entrepreneurship, creative practice, and community leadership can all produce meaningful evidence of capability even when they do not result in conventional credentials.

The Importance of Time, Scale, and Context

Capability is not static.

A skill practiced for years in a demanding environment is different from one introduced briefly in a course. A capability used last week carries different information from one last used a decade ago. Leading a small project and directing a complex organization may involve related abilities at very different scales.

STAMP therefore considers dimensions that traditional skills tags often omit:

Time

When was a capability developed or used? For how long? Is it still being practiced and strengthened?

Scale

What was the size, complexity, consequence, or scope of the activity?

Role

What responsibility did the person personally hold? Were they observing, contributing, leading, teaching, or owning the outcome?

Context

What environment, constraints, relationships, and goals shaped the work?

Evidence

What supports the claim? Is it self-described, demonstrated through an artifact, endorsed by another person, assessed, or verified through a credential?

These dimensions do not need to produce a single universal score.

In fact, collapsing them into one number would hide much of the value the model is intended to preserve. The purpose is to make the context visible enough for people and organizations to reach better-informed conclusions.

Translating Across Different Talent Languages

Schools, employers, industries, credential issuers, and workforce organizations rarely describe capability in exactly the same way.

One organization may use an occupational taxonomy. Another may maintain a competency framework developed around its culture and workflows. A school may describe learning outcomes. A credential issuer may define assessment criteria. A worker may use the language of their actual experiences.

Forcing every participant to adopt one vocabulary is neither realistic nor necessarily desirable.

STAMP instead explores a translation-oriented approach.

A talent asset can preserve the original language and source while also being connected with relevant concepts from other frameworks. This allows systems to identify relationships without pretending that two differently defined concepts are always identical.

Gobekli has used the phrase ontological refraction to describe this idea: a concept can be viewed through another framework or taxonomy while retaining information about its original meaning.

For example, an activity described in an employer’s internal language might be connected with:

• Skills identified in an occupational framework
• Learning outcomes used by a school
• Requirements associated with a credential
• Capabilities requested in a job opportunity
• Broader dimensions represented within the Talent Tree

This is more responsible than silently replacing one term with another. Translation should preserve the source, context, confidence, and relationship between concepts.

How STAMP Relates to Existing LER Standards

STAMP is not intended to replace Learning and Employment Records, verifiable credentials, digital wallets, credential transparency systems, or established education and workforce standards.

Those technologies address essential questions such as:

• How is a credential structured?
• Who issued it?
• Can its authenticity be verified?
• How can it move between systems?
• How is a learning program or credential described?
• How can an individual receive and share a trusted record?

STAMP addresses a related but different challenge:

How can records, experiences, activities, capabilities, evidence, and context be connected into a larger and more continuously useful understanding of a person?

Existing LER standards can provide trusted inputs. STAMP provides a framework for exploring how those inputs relate to other parts of a person’s story.

The long-term objective is compatibility and translation, not competition with the ecosystem’s existing infrastructure.

From STAMP to the Talent Tree

The thinking behind STAMP contributed to the development of Gobekli’s Talent Tree.

The Talent Tree expands beyond technical and human skills to consider other dimensions that shape what a person can do and become, including:

• Experiences and journey
• Motivations and interests
• Passions and voice
• Abilities and agency
• Leadership and stewardship
• Reputation and relationships
• Domain knowledge
• Technical and human skills
• Potential and continued growth

These dimensions are not meant to become another rigid checklist.

They provide different lenses through which experiences can be understood. Over time, connections among experiences, activities, evidence, feedback, and reflection can reveal patterns that no isolated record could show.

For individuals, this can support greater self-understanding and better ways to communicate what makes them capable and unique.

For organizations, it can provide a more complete view of the human intelligence available across people, teams, workflows, and relationships.

Why This Matters in the Age of AI

AI is dramatically increasing the ability to extract, classify, compare, and generate information about people.

But if the underlying human data is fragmented or stripped of context, AI can amplify the limitations of the systems it is meant to improve.

A résumé parser may infer skills from keywords. A matching engine may compare those skills with a job description. An organizational system may generate recommendations based on job titles, course completions, or performance fields.

These tools can appear intelligent while operating with only a fraction of the picture.

A contextual talent map gives AI a stronger foundation. It can help distinguish claims from evidence, connect capabilities with their sources, recognize relationships among experiences, and explain why a particular interpretation was reached.

However, richer data also creates greater responsibility.

People must retain the ability to:

• Review and correct information
• Add context
• Understand how conclusions were formed
• Decide what is shared
• Challenge inaccurate interpretations
• Benefit from the intelligence created from their contributions

STAMP is therefore not only a data architecture question. It is also a question of governance, agency, transparency, and design.

STAMP Within the Human Intelligence Layer

Gobekli’s Human Intelligence Layer is a sociotechnical architecture for helping people, organizations, and intelligent systems develop a shared, trusted understanding of human experience, capability, judgment, relationships, goals, evidence, and change.

STAMP contributes to the data-modeling foundation of that vision.

TalentPass gives individuals a place to build and control their own source of human intelligence. Pythia helps them reflect on their experiences and turn those experiences into useful profiles. The Talent Tree connects activities, capabilities, evidence, and growth. Passport Pages help people share selected information for particular purposes.

TalentSync helps organizations apply related structures to opportunities, teams, learning, membership, and workforce relationships. It creates the possibility of continuously learning from what happens through real workflows—not only from static HR records.

Together, these systems are intended to create feedback loops through which people and organizations can understand what is happening, act on that understanding, and learn from what happens next.

A Framework That Must Continue to Evolve

STAMP began with the recognition that skills tags were not sufficient to represent human capability.

That remains true.

But the answer is not to replace one oversimplified standard with another rigid model. Human beings, organizations, occupations, cultures, and communities will always describe talent in different ways.

A useful framework must preserve that diversity while making translation and shared understanding possible.

STAMP continues to evolve as Gobekli builds products, works with partners, tests real workflows, and learns from the broader LER and skills ecosystem. Its value will ultimately depend on practical implementation, interoperability, transparent governance, and evidence that it helps people and organizations make better decisions.

The long-term ambition is not to map people as if they were fixed collections of assets.

It is to build an adaptable language through which people can better understand, grow, and communicate their capabilities—and through which organizations and AI can learn to see more of the human intelligence they currently miss.

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STAMP status notice: STAMP is a Gobekli-developed conceptual and technical framework that continues to inform the architecture of the Talent Tree and Human Intelligence Layer. It should not currently be interpreted as an independently governed, formally ratified, or broadly adopted public standard. Its specifications and implementation may continue to evolve.

Product availability notice: This article describes Gobekli’s broader technical vision. Some TalentPass and TalentSync capabilities discussed may be in development, expanding release, or available only through Launch Partnerships. Visit the Product Availability page for the current status of TalentPass, TalentSync, Pythia, Profiles, Passport Pages, and the Talent Tree.