An open specification for verified professional knowledge graphs.
WorkProof is a structured, artifact-derived representation of what a professional can actually do — built from verifiable work evidence, not self-reported claims. This repository defines the open schema, evidence model, attestation format, and agent card extension that make up the WorkProof protocol.
Status: Draft v0.1 — open for community feedback
Maintained by: TalentProof
License: MIT
Every professional profile system in existence today is built on the same flawed primitive: the self-reported claim. You say you know React. You say you led a team of 10. You say you shipped at scale. There is no structural way for the person reading your profile to know if any of it is true.
AI is accelerating the problem, not solving it. Recruiters are already deploying AI sourcing tools — Juicebox, Moonhub, SeekOut and others — that autonomously search millions of profiles, score candidates, and send outreach at scale. These tools run faster than any human recruiter, but they still depend on the same flawed input: self-reported text. The signal problem doesn't go away when you automate around it. It compounds.
The next wave goes further. As recruiter agents and candidate agents begin communicating directly — negotiating fit, verifying claims, routing opportunities without human intervention at every step — the absence of structured, machine-readable professional signal becomes a hard blocker. An agent cannot evaluate a paragraph. It needs structured data with explicit confidence scores derived from verifiable evidence. Something has to replace the resume at the schema level.
WorkProof is that replacement.
A WorkProof graph is generated from verifiable work artifacts — code commits, document authorship, assessment results, project records — and structured into named skill clusters with confidence scores derived from evidence weight, not self-report.
It has five components:
| Component | What it captures |
|---|---|
| Skill clusters | Named domains with confidence scores and evidence citations |
| Execution depth | The scale and context in which skills were applied |
| Growth velocity | How fast the graph is expanding into new domains |
| Collaboration graph | Peer signals derived from co-authorship patterns |
| Attestations | Cryptographically signed employer or institution credentials (W3C VC) |
A WorkProof is not a resume. It does not contain job titles, company names (unless attested), or self-written descriptions. It contains structured evidence of capability, derived from artifacts, with explicit confidence scores that reflect the quality of evidence available.
{
"$schema": "https://raw.githubusercontent.com/TalentProof/workproof-schema/main/schema/v0.1.json",
"specVersion": "0.1",
"generatedAt": "2026-04-27T10:00:00Z",
"updatedAt": "2026-04-27T10:00:00Z",
"summary": "string (≤280 chars, AI-generated, describes candidate strengths)",
"growthEdge": "string (≤140 chars, where the candidate is developing toward)",
"growthVelocity": 0.73,
"skillClusters": [],
"collaborationGraph": {},
"sources": [],
"attestations": [],
"agentCard": {}
}A skill cluster is a named professional domain, derived from artifact analysis, with a confidence score that reflects the weight and breadth of available evidence.
{
"id": "frontend-engineering", // stable machine identifier, kebab-case
"name": "frontend-engineering", // human-readable slug (may be same as id in v0.1)
"displayName": "Frontend Engineering", // display label for UIs
"confidence": 0.91,
"executionDepth": {
"scale": "production-scale",
"context": "React at >100k MAU, three shipped products, one open-source library with 400+ stars"
},
"evidence": [
{
"type": "github",
"sourceId": "github:repos:react-dashboard",
"description": "847 commits, primary author, TypeScript + React",
"weight": 0.45,
"verifiedAt": "2026-04-20T00:00:00Z"
},
{
"type": "assessment",
"sourceId": "talentproof:assessment:frontend-q3-2025",
"description": "Frontend fundamentals assessment, score 94/100",
"weight": 0.35,
"verifiedAt": "2025-09-15T00:00:00Z"
},
{
"type": "attestation",
"sourceId": "attestation:acme-corp-2024",
"description": "Employer-attested: shipped three production React apps",
"weight": 0.20,
"verifiedAt": "2024-12-01T00:00:00Z"
}
],
"lastUpdated": "2026-04-27T10:00:00Z"
}| Range | Label | Meaning |
|---|---|---|
| 0.85 – 1.0 | Strong evidence | Multiple high-weight sources, consistent signal |
| 0.60 – 0.84 | Growing expertise | Solid evidence, some breadth still developing |
| 0.35 – 0.59 | Early signals | Emerging evidence, thinner artifact base |
| 0.00 – 0.34 | Exploratory | Initial activity, not enough to make strong claims |
Confidence scores must be derived from evidence weights — they cannot be self-reported or manually overridden by the candidate.
{
"scale": "personal | team | org | production-scale",
"context": "string (≤200 chars, specific and honest — not marketing copy)"
}Scale definitions:
personal— side project, solo work, learning contextteam— shipped within a team of 2–10org— cross-team impact, org-wide adoptionproduction-scale— shipped to real users at meaningful scale
| Type | Source | Trust level |
|---|---|---|
assessment |
Standardized skill assessment (e.g. TalentProof) | High — third-party scored |
github |
GitHub contributions, commits, repos, PRs | Medium-high — artifact-derived |
document |
Authored documents, PRDs, specs (Google Drive, Notion) | Medium — authorship-derived |
jira |
Ticket ownership, resolution patterns | Medium — work record |
academic |
University coursework, thesis, capstone projects — institution-verified | Medium — institution-derived |
bootcamp |
Structured training program completion and projects — program-verified | Medium — program-derived |
certification |
Industry or platform certification — proctored, time-limited, provider-verifiable | Medium-high — provider-verified |
course |
Platform-verified course completion (Coursera, Google, Microsoft Learn) | Medium — platform-verified |
attestation |
Employer or institution-signed WorkProof credential | Highest — cryptographically verified |
peer |
Peer vouching via C2C graph (co-authorship based) | Medium — network-derived |
Evidence weight ranges:
attestation: 0.15 – 0.40 per itemassessment: 0.25 – 0.45 per itemcertification: 0.15 – 0.30 per item (scales with provider credibility and scope)github: 0.05 – 0.35 per item (scales with commit depth and consistency)academic: 0.10 – 0.25 per itembootcamp: 0.10 – 0.20 per itemcourse: 0.08 – 0.20 per itemdocument: 0.05 – 0.20 per itempeer: 0.05 – 0.15 per item
Total evidence weights for a cluster should not exceed 1.0. Implementations must cap and normalise when multiple sources overlap.
The collaboration graph captures peer signal derived from co-authorship and verified collaboration — not endorsements or self-reported team size.
{
"peerCount": 12,
"verifiedCollaborations": 4,
"domains": ["frontend-engineering", "developer-tooling"],
"c2cEdges": [
{
"peerHandle": "string (opaque, platform-specific)",
"sharedClusters": ["frontend-engineering"],
"collaborationStrength": 0.78,
"evidenceBasis": "github-co-authorship | attestation-cosignature | peer-vouching"
}
]
}c2cEdges are only included in full WorkProof responses (consent level L3
or higher). Public WorkProof summaries show only peerCount,
verifiedCollaborations, and domains.
Sources record which connectors have contributed to the WorkProof graph and when they were last indexed.
[
{
"type": "github | google-drive | notion | jira | confluence | assessment | slack | microsoft-learn | google-learn | credly | university | bootcamp",
"connectedAt": "2026-03-01T00:00:00Z",
"lastIndexed": "2026-04-27T00:00:00Z",
"status": "active | error | disconnected",
"artifactCount": 847
}
]Attestations are employer-issued WorkProof credentials. They are cryptographically signed, candidate-gated, and based on artifacts — not on subjective human authorship.
The trust model: the employer reviews and signs facts they did not write. The attestation content is generated from the employee's work artifacts. The employer is the notary, not the author.
[
{
"id": "att_01j8abc123",
"issuerName": "Acme Corporation",
"issuerDomain": "acme.com",
"issuerDID": "did:web:acme.com",
"issuerType": "employer | university | bootcamp | certification-body",
"period": {
"start": "2022-06-01T00:00:00Z",
"end": "2024-12-01T00:00:00Z"
},
"attestedClusters": [
{
"clusterId": "frontend-engineering",
"confidence": 0.88,
"note": "Led three production React deployments. Primary author on core UI library."
}
],
"issuedAt": "2024-12-15T00:00:00Z",
"candidateApproved": true,
"revoked": false,
"credential": "eyJ0eXAiOiJKV1Qi...",
"credentialFormat": "jwt_vc | json_ld_vc"
}
]Attestations are based on the W3C Verifiable Credentials Data Model v2.
Implementations should support both jwt_vc (compact, widely supported)
and json_ld_vc (richer context, linked data).
1. Employer deploys WorkProof Attestation Agent
(reads GitHub, Jira, Confluence within company boundary)
2. Agent indexes employee's work artifacts
→ generates draft attestation (clusters + evidence + period)
3. Employee reviews draft
→ approves or requests edits
→ can revoke at any time after issuance
4. Employer reviews and signs
→ signs with company DID or registered signing key
→ issues W3C Verifiable Credential
5. Attestation stored in candidate's WorkProof graph
→ candidate controls which recruiters can see it
→ verifiable without contacting the employer
(signature checked against employer's public DID)
WorkProof extends the
A2A Agent Card
with professional identity fields. A WorkProof-compatible agent card
contains the standard A2A fields plus a workproof extension object.
{
"name": "Candidate Agent — Priya Sharma",
"description": "Professional agent representing Priya Sharma on TalentProof",
"url": "https://talentproof.ai/agents/priyasharma",
"version": "1.0",
"capabilities": {
"streaming": false,
"pushNotifications": false
},
"extensions": {
"workproof": {
"specVersion": "0.1",
"profileUrl": "https://talentproof.ai/p/priyasharma",
"availability": "passive",
"consentLevel": 1,
"topClusters": [
{ "name": "frontend-engineering", "confidence": 0.91 },
{ "name": "system-design", "confidence": 0.74 },
{ "name": "developer-tooling", "confidence": 0.61 }
],
"hasAttestations": true,
"sourceCount": 3,
"trustScore": 0.82
}
}
}The workproof extension exposes only public-facing fields (consent level
L1). Deeper access requires negotiation through the WorkProof consent gate.
The WorkProof consent gate controls what a querying agent can access. Consent levels are scoped to a specific requester identity, a specific purpose (job spec context), and a time window.
| Level | Label | Accessible data | Who can request |
|---|---|---|---|
| L0 | Exists | Agent endpoint URL only | Anyone |
| L1 | Summary | Top clusters (names + confidence), availability, source count, attestation flag | Any registered agent |
| L2 | Domain query | Answer structured questions about specific skill domains | Verified recruiter agents |
| L3 | Cited evidence | Specific proof points for claimed clusters | Verified recruiter agents with trust score ≥ 0.6 |
| L4 | Full graph | Complete WorkProof including collaboration edges and attestation detail | Explicit candidate approval per request |
Consent levels are managed by the candidate through their WorkProof agent settings. Implementations must enforce consent levels server-side — the consent level declared in the agent card is the maximum; individual requests may be granted lower access.
Present consent to candidates as a simple availability dial:
- Closed → L0 only
- Passively open → L1 default, L2 on request
- Actively looking → L1 default, L2 on request, L3 available to trusted recruiters
Do not expose L0–L4 terminology in candidate-facing UIs.
Growth velocity is a 0–1 signal representing how actively a candidate's WorkProof graph is expanding into new domains over a rolling 12-month window.
growthVelocity = (newClusterCount × 0.4)
+ (risingClusterDelta × 0.35)
+ (newSourceConnections × 0.25)
Normalise to 0–1 based on platform-wide distribution.
A velocity of 0.7+ indicates a candidate actively expanding their professional surface area. A velocity of 0.2 or below indicates a stable but not actively growing practitioner. Neither is inherently better — recruiters should filter by velocity only when growth trajectory is explicitly required for a role.
{
"$schema": "https://raw.githubusercontent.com/TalentProof/workproof-schema/main/schema/v0.1.json",
"specVersion": "0.1",
"generatedAt": "2026-04-27T10:00:00Z",
"updatedAt": "2026-04-27T10:00:00Z",
"summary": "Frontend engineer with production-scale React experience and a secondary track record in developer tooling. Strong artifact base across three shipped products and one OSS library.",
"growthEdge": "Moving into system design and distributed architecture, active GitHub signal in this area over the last 8 months.",
"growthVelocity": 0.73,
"skillClusters": [
{
"id": "frontend-engineering",
"name": "frontend-engineering",
"displayName": "Frontend Engineering",
"confidence": 0.91,
"executionDepth": {
"scale": "production-scale",
"context": "React at >100k MAU across three shipped products, one OSS library with 400+ stars"
},
"evidence": [
{
"type": "github",
"sourceId": "github:repos:react-dashboard",
"description": "847 commits, primary author, TypeScript + React",
"weight": 0.45,
"verifiedAt": "2026-04-20T00:00:00Z"
},
{
"type": "assessment",
"sourceId": "talentproof:assessment:frontend-q3-2025",
"description": "Frontend assessment, score 94/100",
"weight": 0.35,
"verifiedAt": "2025-09-15T00:00:00Z"
},
{
"type": "attestation",
"sourceId": "att_01j8abc123",
"description": "Employer-attested: led three production React deployments",
"weight": 0.20,
"verifiedAt": "2024-12-15T00:00:00Z"
}
],
"lastUpdated": "2026-04-27T10:00:00Z"
},
{
"id": "developer-tooling",
"name": "developer-tooling",
"displayName": "Developer Tooling",
"confidence": 0.61,
"executionDepth": {
"scale": "team",
"context": "Built internal CLI tools adopted by 15-person engineering team"
},
"evidence": [
{
"type": "github",
"sourceId": "github:repos:dev-cli",
"description": "Primary author of internal CLI, 3 contributors",
"weight": 0.55,
"verifiedAt": "2026-02-10T00:00:00Z"
},
{
"type": "peer",
"sourceId": "peer:handle:anish-mehta",
"description": "Co-authored tooling project, verified collaboration",
"weight": 0.10,
"verifiedAt": "2026-01-15T00:00:00Z"
}
],
"lastUpdated": "2026-04-27T10:00:00Z"
}
],
"collaborationGraph": {
"peerCount": 8,
"verifiedCollaborations": 3,
"domains": ["frontend-engineering", "developer-tooling"]
},
"sources": [
{
"type": "github",
"connectedAt": "2026-03-01T00:00:00Z",
"lastIndexed": "2026-04-27T00:00:00Z",
"status": "active",
"artifactCount": 1204
},
{
"type": "assessment",
"connectedAt": "2025-09-10T00:00:00Z",
"lastIndexed": "2025-09-15T00:00:00Z",
"status": "active",
"artifactCount": 2
}
],
"attestations": [
{
"id": "att_01j8abc123",
"issuerName": "Acme Corporation",
"issuerDomain": "acme.com",
"issuerDID": "did:web:acme.com",
"period": {
"start": "2022-06-01T00:00:00Z",
"end": "2024-12-01T00:00:00Z"
},
"attestedClusters": [
{
"clusterId": "frontend-engineering",
"confidence": 0.88,
"note": "Led three production React deployments."
}
],
"issuedAt": "2024-12-15T00:00:00Z",
"candidateApproved": true,
"revoked": false,
"credentialFormat": "jwt_vc"
}
],
"agentCard": {
"availability": "passive",
"consentLevel": 1,
"protocol": "workproof/v0.1"
}
}A machine-readable JSON Schema (schema/v0.1.json) is included in this
repository for validation. Import it in your implementation:
import workproofSchema from 'workproof-schema/schema/v0.1.json'
import Ajv from 'ajv'
const ajv = new Ajv()
const validate = ajv.compile(workproofSchema)
const valid = validate(workproofDocument)import type { WorkProof, SkillCluster, Attestation, AgentCard }
from 'workproof-schema/types'Types are published to npm as workproof-schema (coming soon).
A conforming WorkProof implementation must:
- Generate
skillClustersfrom at least one non-self-reported source (assessment, GitHub, document, or attestation) - Derive
confidencescores from evidence weights — not user input - Enforce consent gate levels server-side
- Store the full WorkProof graph locally or in an encrypted candidate-controlled store — not exposed by default
- Expose only L1 data in the public agent card extension
| Component | Recommended |
|---|---|
| Graph storage | KuzuDB (embedded, local), Firestore (cloud) |
| Embedding model | text-embedding-004 (Google), or any 768-dim model |
| LLM for cluster generation | Gemini 1.5 Pro, GPT-4o, Claude Sonnet |
| Attestation signing | W3C VC JWT, did:web |
| Agent protocol | MCP (tools), A2A (agent-to-agent) |
Professionals who actively learn through online platforms — Coursera, Microsoft Learn, Google Career Certificates, Udemy, LinkedIn Learning — are well-served by WorkProof, but the schema draws a deliberate distinction between verifiable completions and self-reported ones.
certification — industry or platform certifications that are
externally proctored, time-limited, and verifiable against a provider
registry. These carry higher weight because they cannot be inflated and
require renewal.
course — structured learning program completions that are
platform-verified (not self-reported). Lower weight than certifications
because completion rigor varies, but valid evidence when the issuing
platform provides external verification.
WorkProof does not accept self-reported course completions as evidence. A certificate that anyone can generate by clicking through a course with no external verification is equivalent to typing "Python" on a resume — it adds nothing structurally. Every platform LinkedIn lets you list on your profile falls into this category. WorkProof draws the line differently.
| Platform / credential | Verifiable? | Evidence type | Weight range |
|---|---|---|---|
| Microsoft certifications (AZ-900, MS-900, etc.) | Yes — Microsoft Transcript | certification |
0.15 – 0.30 |
| Google Career Certificates | Yes — Credly badge API | course |
0.10 – 0.20 |
| AWS / Azure / GCP certifications | Yes — provider registry | certification |
0.20 – 0.35 |
| GitHub Certifications (Actions, Copilot, Security) | Yes — GitHub API | certification |
0.15 – 0.25 |
| Coursera (with accredited university partner) | Yes — Credly / Coursera URL | course |
0.08 – 0.15 |
| Cisco, CompTIA, PMP, CKAD and similar | Yes — provider registry | certification |
0.15 – 0.35 |
| Udemy | No — self-generated certificate | Not standalone evidence | |
| LinkedIn Learning | No — self-reported completion | Not standalone evidence | |
| Coursera (no accredited partner) | No — self-generated | Not standalone evidence |
A Udemy certificate alone has no evidential weight in WorkProof. But a GitHub project built during or after a Udemy course does — because the project is a verifiable artifact. The course becomes context; the project is the evidence.
This is the correct model for all unverified learning:
Udemy ML course + no project = 0 WorkProof evidence
Udemy ML course + GitHub project = GitHub evidence (course is context, not evidence)
Google ML cert + no project = course evidence (0.10 weight)
Google ML cert + GitHub project = course evidence + github evidence (higher confidence)
Implementations should surface this to candidates clearly: “Connect your GitHub to show what you built with what you learned.” The combination of a verified certificate and an artifact project is meaningfully stronger than either alone.
{
"type": "certification",
"sourceId": "microsoft:transcript:AZ-900-2026",
"description": "Microsoft Azure Fundamentals (AZ-900), passed March 2026",
"weight": 0.22,
"verifiedAt": "2026-03-15T00:00:00Z",
"metadata": {
"provider": "Microsoft",
"certificationId": "AZ-900",
"expiresAt": null,
"verificationUrl": "https://learn.microsoft.com/api/credentials/..."
}
}The following connectors are planned for WorkProof implementations:
| Connector | What it pulls | Status |
|---|---|---|
microsoft-learn |
Certifications via Microsoft Transcript API | Planned |
google-learn |
Google Career Certificates via Credly | Planned |
credly |
Any Credly-hosted badge (Coursera, IBM, etc.) | Planned |
github-certifications |
GitHub Actions, Copilot, Security certifications | Planned |
aws |
AWS certification registry | Planned |
Self-reported platforms (Udemy, LinkedIn Learning) are intentionally not connector targets. They can appear as contextual notes on a candidate’s profile but do not contribute to WorkProof evidence weights.
WorkProof is designed to be accurate for professionals at every stage — including college graduates and career-changers with no employer history. The schema handles this honestly rather than forcing a mismatch.
A college graduate’s WorkProof graph will show lower confidence scores than a senior engineer’s. That is correct, not a flaw. A graduate with strong assessment results and active GitHub projects might generate confidence scores in the 0.45–0.65 range. A senior engineer with employer attestation and years of production artifacts might reach 0.85–0.95. The gap is real and the schema reflects it accurately.
This is more honest than a resume. A graduate’s resume inflates coursework into professional claims. A WorkProof graph shows what the evidence actually supports — with growth velocity often running higher than experienced practitioners who have stabilised in a domain.
Graduates have more WorkProof evidence than they realise:
| Source | What it captures |
|---|---|
| Skill assessments | Standardised scores independent of employment history |
| GitHub | Personal projects, coursework repos, open source contributions, hackathon code |
Academic evidence (academic type) |
University coursework, thesis, capstone project authorship |
Bootcamp evidence (bootcamp type) |
Structured program completion, projects built during training |
| Hackathons | GitHub repos + peer collaboration edges from team events |
| Freelance work | Client project artifacts, GitHub activity, doc authorship |
No employer history is required. Assessments and GitHub activity alone are sufficient to generate a valid WorkProof graph.
Universities and bootcamps can act as attestation issuers — not just
employers. An institutional attestation follows the same W3C VC format as
an employer attestation, with issuerType set to indicate the institution
kind:
{
"id": "att_grad_01",
"issuerName": "University of California, Berkeley",
"issuerDomain": "berkeley.edu",
"issuerDID": "did:web:berkeley.edu",
"issuerType": "university",
"period": {
"start": "2022-09-01T00:00:00Z",
"end": "2026-05-15T00:00:00Z"
},
"attestedClusters": [
{
"clusterId": "frontend-engineering",
"confidence": 0.62,
"note": "Completed CS upper division coursework. Primary author on senior capstone: distributed task scheduler, deployed to production."
}
],
"issuedAt": "2026-05-20T00:00:00Z",
"candidateApproved": true,
"revoked": false,
"credentialFormat": "jwt_vc"
}A bootcamp attestation uses "issuerType": "bootcamp" with the same
structure. Bootcamp attestations carry slightly lower weight than university
or employer attestations in the evidence model, reflecting the shorter and
less independently-verified context.
Growth velocity is often the strongest signal for a recent graduate. A candidate actively shipping projects, completing assessments, and contributing to open source may have a growth velocity of 0.80+ even with lower absolute confidence scores. Recruiters hiring for trainable potential — rather than specific depth — should weight velocity heavily.
Implementations targeting graduate hiring should surface growthVelocity
prominently in candidate-facing UIs and recruiter query results, not treat
it as a secondary field.
{
"specVersion": "0.1",
"summary": "Recent CS graduate with strong frontend assessment results and active GitHub history across four personal projects. Fast learner with high growth velocity — two new technical domains added in the last six months.",
"growthEdge": "Moving from personal projects into collaborative open-source contribution. First OSS PR merged last month.",
"growthVelocity": 0.81,
"skillClusters": [
{
"id": "frontend-engineering",
"name": "frontend-engineering",
"displayName": "Frontend Engineering",
"confidence": 0.58,
"executionDepth": {
"scale": "personal",
"context": "Four personal React projects, one deployed. Senior capstone: interactive data visualisation tool."
},
"evidence": [
{
"type": "assessment",
"sourceId": "talentproof:assessment:frontend-2026",
"description": "Frontend assessment, score 88/100",
"weight": 0.45,
"verifiedAt": "2026-03-10T00:00:00Z"
},
{
"type": "github",
"sourceId": "github:repos:portfolio-site",
"description": "React portfolio, 120 commits, deployed on Vercel",
"weight": 0.25,
"verifiedAt": "2026-04-01T00:00:00Z"
},
{
"type": "academic",
"sourceId": "berkeley:capstone:2026",
"description": "Senior capstone: data visualisation tool, primary author",
"weight": 0.20,
"verifiedAt": "2026-05-01T00:00:00Z"
}
]
}
],
"sources": [
{ "type": "assessment", "status": "active" },
{ "type": "github", "status": "active" },
{ "type": "university", "status": "active" }
],
"attestations": [
{
"id": "att_grad_01",
"issuerName": "UC Berkeley",
"issuerType": "university",
"issuerDomain": "berkeley.edu",
"period": { "start": "2022-09-01T00:00:00Z", "end": "2026-05-15T00:00:00Z" },
"attestedClusters": [
{ "clusterId": "frontend-engineering", "confidence": 0.55 }
],
"candidateApproved": true,
"revoked": false
}
]
}The lower confidence scores are accurate. The high growth velocity is the differentiating signal. A recruiter looking for trainable early-career talent gets an honest picture of both.
| Version | Status | What's included |
|---|---|---|
| v0.1 | Draft | Core schema, skill clusters, evidence model, attestation format, consent gate, agent card extension |
| v0.2 | Planned | JSON Schema file, TypeScript types npm package, attestation verification library |
| v0.3 | Planned | C2C collaboration graph protocol, peer vouching spec |
| v1.0 | Planned | Stable spec, reference implementation, registry protocol |
WorkProof is an open specification. Community feedback is welcome.
- Discuss: Open a GitHub Discussion for questions and proposals
- Issues: File an issue for spec ambiguities or errors
- PRs: Pull requests welcome for typos, examples, and clarifications — schema changes require Discussion first
See CONTRIBUTING.md for guidelines.
| Protocol | Relationship |
|---|---|
| MCP (Anthropic) | WorkProof graph is exposed via MCP tools. MCP handles tool/data connections; WorkProof defines what the data means. |
| A2A (Google / Linux Foundation) | WorkProof extends the A2A Agent Card with professional identity fields. A2A handles agent communication; WorkProof provides the professional signal. |
| W3C Verifiable Credentials | WorkProof attestations use the W3C VC data model for employer-signed credentials. |
| OpenAPI | WorkProof registry APIs are described in OpenAPI 3.1. |
MIT License. Copyright (c) 2026 TalentProof.
See LICENSE for full text.
If you implement or reference the WorkProof schema in academic or commercial work:
WorkProof Schema v0.1 (2026). TalentProof.
https://github.com/TalentProof/workproof-schema