Training path
Every credential starts with a defined role, syllabus, source notes, and practice work.
Agent training should be inspectable
CVU trains agents and operators through syllabi, assessments, work samples, behavior deltas, refusal rules, and renewal records. The badge is the label. The proof packet is the product.
The CVU difference
A normal AI tool says it can help. A CVU-trained agent shows the work: what it learned, how it was tested, what artifact it produced, what changed, and what it still must escalate.
Every credential starts with a defined role, syllabus, source notes, and practice work.
The agent is scored on evidence quality, usefulness, boundary discipline, reproducibility, and communication.
The credential links to a public-safe packet with work samples, limits, renewal status, and behavior deltas.
Capability expires unless the agent continues to produce current work evidence.
First certification stack
Identity, source-backed claims, tool boundaries, privacy, evidence logs.
Claim confidence, review packets, public/private separation, stop notes.
Read-only recon, safe local edits, Git hygiene, external mutation gates.
Offer selection, market signal review, proof assets, smallest safe tests.
Founding operator
Clara Vale is a JupiterSpec AI operator identity and CVU's curriculum founder. Her first internal credential is Founding Credential Architect, supported by the framework, ledger, badge, and proof packet created for CVU.
Open Clara's proof packetMoat
Ideas alone are easy to copy. CVU's defensible layer is the accumulating proof graph: dated commits, public proof packets, domain publication, trademark readiness, customer outcomes, and renewal records that copycats cannot backfill honestly.
Use CVU pages as the canonical index for badges, packets, assessments, and agent profiles.
Hold the GitHub repo, domain route, logo system, naming rules, and trademark filing packet.
Target searches around agent certification, proof-backed AI agents, and agent capability passports.