Safety Plan Crosscheck
A production manager pastes a venue safety plan and planned stage actions, and Jev checks each action against cited controls, classifies the gap, and sends uncertain or high-risk items to a human safety lead.
Verify & Escalate· check then hand off
Section · Jev
full primer →The kernel.
Jev checks each claim about Production safety against its evidence; anything unclear goes to a person, so directors never act on a guess.
Why this primitiveTyped evidence flags missing controls without giving automated clearance for hazardous activity.
Kernel
a Jev `noul` checks each claim/field against its evidence; low or mid probabilities are escalated to a human review queue instead of auto-acting
Drives the UI as
claim cards marked supported / unsupported / needs review, with a review queue column
Required key.
TYPESAFE_API_KEY
Your TypeSafe key. The mega-prompt asks for it with a secure form and keeps it on the server. Jev returns typed decisions: choice, noul (yes/no probability) and score.
open ↗Add this in your Lovable project under Settings → Secrets before pasting the prompt below.
Appendix · Mega-prompt
The build prompt.
budget · 1 message
Paste into a fresh Lovable project. Make sure the key above is set first. read the build strategy →
Build "Safety Plan Crosscheck" as a ONE-SHOT Lovable build (~5 credits, no follow-ups).
Single-page TanStack Start app, one route, cut scope ruthlessly.
THIS PROMPT IS SELF-CONTAINED
Do not assume the participant knows Jev or has read any other page. TypeSafe Jev is a decision model,
not a text generator. It receives `state` plus a map of typed `questions` and returns JSON that code can
use directly. Use Choice to select exactly one defined option, Noul for a yes/no probability from 0 to 1,
and Score for a probability-weighted position across ordered levels. Keep rules, thresholds, arithmetic,
and actions in code. Use a generative model only if the product separately needs prose.
CONCEPT
A production manager pastes a venue safety plan and planned stage actions, and Jev checks each action against cited controls, classifies the gap, and sends uncertain or high-risk items to a human safety lead.
Discipline: Theater & Live Performance (Production safety).
Jev pattern: Verify & Escalate (check then hand off) — a Jev `noul` checks each claim/field against its evidence; low or mid probabilities are escalated to a human review queue instead of auto-acting.
Why typed decisions: Typed evidence flags missing controls without giving automated clearance for hazardous activity.
ENGINE: TypeSafe Jev (typed decisions, not text)
- Before writing code, ask the participant for `TYPESAFE_API_KEY` with Lovable's secure secret form. Never
ask them to paste it into chat, never prefix it with `VITE_`, and never expose it to browser code.
- Put the callable server function in `src/lib/jev.functions.ts`; do not import a server-only module from UI code.
- Validate the user's input before the handler and read `process.env['TYPESAFE_API_KEY']` inside the handler.
- From the server, call:
POST https://api.typesafe.ai/v1/systemone
Authorization: Bearer <TYPESAFE_API_KEY>, Content-Type: application/json
body: { "model": "jev-latest", "state": <Planned actions and cited safety-plan controls>, "questions": QUESTIONS }
- Ask all questions in ONE request (they run in parallel and cannot see each other).
- Read Choice from `answers[id].choice`, `.probabilities`, and `.confidence`; Score from `.score`,
`.probabilities`, `.legend`, and `.confidence`; Noul from `.noul` only (it has no separate confidence).
SERVER IMPLEMENTATION (adapt the input name and result mapping to this concept)
```ts
import { createServerFn } from "@tanstack/react-start";
import { z } from "zod";
const Input = z.object({ text: z.string().trim().min(1).max(20000) });
export const decide = createServerFn({ method: "POST" })
.inputValidator((input: unknown) => Input.parse(input))
.handler(async ({ data }) => {
const apiKey = process.env['TYPESAFE_API_KEY'];
if (!apiKey) return { ok: false as const, status: 500, message: "TYPESAFE_API_KEY is not configured." };
const requestBody = {
model: "jev-latest",
state: { text: data.text },
questions: QUESTIONS,
};
let response: Response | undefined;
for (let attempt = 0; attempt < 3; attempt += 1) {
response = await fetch("https://api.typesafe.ai/v1/systemone", {
method: "POST",
headers: { Authorization: `Bearer ${apiKey}`, "Content-Type": "application/json" },
body: JSON.stringify(requestBody),
});
if (response.ok || (response.status !== 429 && response.status < 500)) break;
if (attempt < 2) await new Promise((resolve) => setTimeout(resolve, 750 * 2 ** attempt));
}
if (!response) return { ok: false as const, status: 503, message: "Jev did not respond." };
if (!response.ok) {
const message = (await response.text()).slice(0, 500) || `Jev request failed (${response.status}).`;
return { ok: false as const, status: response.status, message };
}
const result = await response.json();
return { ok: true as const, answers: result.answers };
});
```
In the page component, call `decide` with `useServerFn`, preserve the participant's input while loading or
on error, disable duplicate submissions, and render the returned typed values rather than parsing prose.
QUESTIONS (start here, refine wording if needed)
{
"control_covers_action": {
"type": "noul",
"instructions": "Does the cited control explicitly address the planned action?",
"criteria": {
"true": "A relevant control is clearly stated",
"false": "Control is absent, ambiguous, or in conflict"
}
},
"safety_gap": {
"type": "choice",
"instructions": "What is the best evidence-based status?",
"criteria": {
"covered": "Relevant control is documented",
"missing": "No relevant control is documented",
"conflict": "Plan contains conflicting controls",
"unclear": "Action or control is insufficiently specified"
}
},
"hazard_severity": {
"type": "score",
"instructions": "What is the potential severity if the gap is real?",
"criteria": [
"1: Minimal operational concern",
"2: Minor injury or delay potential",
"3: Significant injury potential",
"4: Severe injury or life-safety potential"
]
}
}
DECISION RULES (in code, not in the model)
- choice/score: if confidence < 0.6 show a "needs review" badge instead of acting.
- noul: >= 0.75 = yes, <= 0.25 = no, otherwise "review".
- score: normalise with score / (levels - 1) before combining with weights.
- Missing or malformed answers go to review. Show safe HTTP status and message in the UI. Retry only 429/5xx,
at most twice with increasing delay; never retry 400/401/402/403/404 and never invent results.
- If the product needs prose (a summary, a message), use Lovable AI for that text only; Jev makes the decisions.
UI
claim cards marked supported / unsupported / needs review, with a review queue column. Include 2 sample inputs as one-click chips so the demo works instantly.
Tailwind + shadcn, editorial look, footer: "Built during the Creative AI & Quantum Hackathon organised by StreetKode Fam during Indian Krump Festival 14".
NO database, NO auth, NO extra pages, NO tests.
BEFORE YOU FINISH
- Run the app and test both sample inputs through the real server function.
- Confirm the browser receives no API key and no fabricated fallback result.
- Confirm Choice/Score confidence and Noul probability drive the stated review rules.
- Confirm loading, success, review, empty-input, and service-error states are readable on mobile.
Reference only if needed: https://docs.typesafe.ai/api and https://docs.typesafe.ai/primitives
Market sizing.
TAM
$8B workplace safety software
SAM
$500M live-event safety planning
SOM
$3M reachable performing-arts venues
Indicative figures for hackathon pitches — refine with your own research before raising.
Adjacent entries.
Production safety
Stage Risk Radar
Paste a scene's movement plan, set hazards, and rehearsal conditions, and Jev scores each staging option for hazard exposure, controllability, and repeatability for weighted review.
Production safetySafety Note Matcher
A production manager pastes a rehearsal or scene note and approved safety procedures, and Jev selects the procedure that applies or none.
CastingAudition Evidence Desk
A casting director pastes role requirements and audition packets, and Jev checks evidence for each requirement, classifies fit, and scores confidence for human review when confidence is low or mid.
Stage managementCue Sheet Sentinel
A stage manager pastes a cue sheet and rehearsal notes, and Jev verifies each cue trigger against its cited note, labels the mismatch type, and routes uncertain checks to human review.