Does the skill replace my coding model or call Jev itself?

No. It helps an agent build applications that use Jev, but it does not replace the coding model and its helpers never call the API or require a key. Vendor skill and coding-agent guides stay authoritative.

Which responsibilities must never move into the model?

Keep them in deterministic code: validation, permissions, persistence, workflow state, side effects, and final policy. Jev supplies narrow normalized evidence over supplied facts for policy to consume.

How do I evaluate a judgment before it changes behavior?

Yes. Start in shadow or advisory mode, keep receipts with probabilities and versions, compare against deterministic or human labels, and use the offline Python and TypeScript scaffolding first.

Does installing the repo plugin publish it publicly?

No. The repo package is repository marketplace distribution. A public Plugins Directory listing needs a separate submission, validation, review, and publisher approval.

What six steps does the implementation skill cover?

Find the seam, prepare state, ask typed questions, normalize evidence, handle failure, and evaluate and promote. The last step starts in shadow or advisory mode and compares against deterministic or human labels before changing behavior.

Do the skill's helpers need a TypeSafe account?

No. The offline Python and TypeScript helpers do not call TypeSafe or require an API key; only the guidance references the vendor API.

Does the repo plugin need an MCP server?

No. It is a skills-only plugin, which OpenAI accepts, so the public submission ZIP contains the plugin root without a repository marketplace or MCP server.

CommunityJev Agents & MCP

TypeSafe AI Implementation Skill

Engineering playbook and skill for placing Jev typed judgments at bounded seams while keeping validation, permissions, persistence, and policy in deterministic code.

See highlights

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Stars: 1
Language: Python
License: APACHE-2.0
Published by: Community
Author: ngallodev-software
Last verified: Oct 2026

ngallodev-software/typesafe-ai-implementation-skill

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a work in progress, built from the typesafe-ai-skill for building and analyzing jev opportunities

Forks: 0
Last pushed: Updated Oct 2026

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About this project

A practical engineering playbook for using Jev/System One at bounded semantic decision seams while keeping application authority in code. Portfolio · TypeSafe/Jev case study · JevHunt listing. Start here: SKILL.md · Design references · Provenance · Official TypeSafe documentation. This Codex companion helps integrate TypeSafe AI's Jev model into applications; it does not replace the LLM that powers Codex. For current API and primitive instructions, use TypeSafe's official agent skill and coding-agent guide.

Quoted from the project's README. Read the full README.

Tagscommunity github

Works forJev for Classification

Used inJev for Software & DevTools

Languages: Python

Highlights

Core rule restricts semantic models to bounded judgment seams and keeps authority-bearing decisions in application code.
Workflow covers seam finding, bounded redacted state, typed Choice Noul Score questions, receipts, and fallback paths.
Helpers and offline scripts never call TypeSafe and need no key, with design references and provenance tags included.
Ships a skills-based ChatGPT and Codex plugin package with marketplace definition and prepared submission materials.

Watch out

Independent guide with no TypeSafe affiliation or endorsement; vendor docs and SDKs remain the authority on product behavior. Copy reviewed against the repository README on 2026-10-04, not from running it.

Questions about this entry

Short answers drawn from the linked project or page.

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