# DISCOVER Synthetic Sample Mission

## Mission card

**Title:** Test a non-production metadata-completeness reminder with synthetic records  
**Workspace:** Synthetic learning-health innovation office — Primary  
**Mission mode:** Sandbox simulation  
**Status:** Exploration → Simulation → Draft artifact → Human-reviewed recommendation  
**EDENA:** Yellow advisory for planning; reclassify on any real data, participant, clinical, institutional or external-use change  
**Data:** Twenty deliberately fabricated idea-intake records; no PHI, participant, prospective-participant, employee, confidential study, sponsor, IP or unpublished-result data  
**Authority:** Unknown; sandbox exploration only  
**Current workflow:** `WF-16`, resolved from exact pinned catalog record `DSC-WF-16` with `PWR-16` and `TPL-17`  
**Activation:** The starter was manually adopted as a draft. Power remains Available Inactive; all agents remain PERM-P0 Disabled. No AI model, tool, connector or external action is used in this example.

This mission is a training fixture. Its counts are designed inputs and simulated outputs, not observations about a real organization, evidence of effectiveness, a scientific finding, or an IRB/HRO/research/QI/clinical/product determination.

## Start a Mission request

> In a fictional, non-production environment, help me compare ways to remind an idea submitter to complete three metadata fields: intended user, claimed benefit and stop condition. Use only the supplied synthetic records. Do not decide whether this is research, QI, clinical work or product development. Do not contact anyone, connect to a real system, claim effectiveness or activate an agent.

The user confirms **Explore in sandbox**. The system creates a draft mission, not an authorized real-world task.

## 1. Assess

### Verified fixture facts

- The local fixture contains exactly 20 fabricated records.
- Seven records were deliberately authored with all three target fields present; this 7/20 baseline is a fixture design choice, not measured performance.
- The pinned catalog uniquely binds `DSC-WF-16` to `WF-16`, `PWR-16` and `TPL-17` for this installed version.
- No model, agent, connector, network request or official-system write is required for the deterministic simulation.

### User-provided information

- The desired outcome is a clearer draft intake experience, not a production change.
- The user prefers plain language and a non-blocking reminder.
- The user wants stop criteria visible before anyone proposes scale.

### Assumptions to test

- A three-item checklist may be clearer than a long tooltip.
- A non-blocking reminder may preserve user control while making missing fields visible.
- The fabricated records are adequate only for interface-path testing, not scientific or operational inference.

### Unknowns

- Whether a real intake process exists or has the same fields.
- Who owns a real process and which institutional review routes apply.
- Whether real users would find any reminder helpful or burdensome.
- Whether any future activity would be research, QI, clinical, operational or product work.

### Assess gate

The human confirms: remain in synthetic sandbox; do not use real records or infer a real setting. A change of data or purpose invalidates the gate and returns the mission to Assess.

## 2. Diagnose or Define

**Defined opportunity:** The fictional intake prototype does not consistently make three desired metadata fields visible before a draft is marked ready for review.

**Not established:** A real workflow defect, root cause, effect on people, research need, clinical risk or effectiveness gap.

### Contributing hypotheses

1. Labels may be too abstract.
2. Users may not see which fields remain incomplete.
3. A hard blocker could introduce unnecessary burden.

### Boundary statement

The mission is interface exploration with synthetic data. DISCOVER does not classify the activity or supply IRB/HRO, privacy, legal, scientific or institutional approval. Any real use requires a new current-state assessment and qualified human routing.

### Define gate

The human accepts the bounded opportunity statement and rejects the broader claim “the reminder improves submissions.”

## 3. Plan

| Option | Expected sandbox benefit | Burden/risk | Reversibility | Decision |
|---|---|---|---|---|
| A. Long tooltip | Adds explanation | Easy to miss; high text density | Easy | Do not select |
| B. Hard completion blocker | Ensures fixture completeness | Overstates necessity; may frustrate users | Easy technically, but behavior is coercive | Reject for this test |
| C. Non-blocking three-item checklist | Makes missing metadata visible while preserving draft control | Could add visual clutter | Easy | Select for simulation |

### Predeclared synthetic measures

- **Path completion:** deterministic test script reaches the review preview without an error.
- **Fixture completeness:** at least 16 of 20 fabricated records contain all three fields after the scripted interaction.
- **Balancing check:** the draft-save path remains available; the reminder does not become a blocker.
- **Stop condition:** stop if the script writes outside the sandbox, connects to a network/system, handles prohibited data, hides uncertainty, or converts the reminder into a required institutional rule.

These thresholds are demonstration criteria, not validated measures or statistical analysis.

### Safeguards and responsibility

- Use only a disposable local copy of the synthetic fixture.
- No participant or user recruitment, observation or contact.
- No model or agent run.
- No external sharing or production integration.
- The named human reviewer decides whether the simulation may run and whether another synthetic iteration is useful.

### Plan gate

The human chooses Option C and authorizes one deterministic local simulation only. This is not authorization for real-world testing.

## 4. Implement

The system creates these clearly labeled draft artifacts:

1. a static checklist wireframe with the three fictional fields;
2. a deterministic script description that transforms a copy of the fabricated fixture;
3. a predeclared measure card and stop checklist;
4. a rollback instruction: discard the modified fixture and restore the untouched synthetic copy; and
5. a run receipt showing zero network, model, agent, connector and external-action calls.

The human reviews the artifact preview and presses **Run synthetic simulation**. The UI status changes from `Draft artifact` to `Simulation`. It never displays `Approved plan`, `Authorized execution` or `Completed real-world action`.

### Implementation receipt

- expected data delta: local disposable synthetic copy only;
- observed external data delta: zero;
- expected action delta: deterministic local fixture transformation;
- observed external action delta: zero;
- agent invocations: zero;
- tool/connector/network calls: zero; and
- stop triggers: none in the simulated fixture path.

## 5. Evaluate

### Simulated fixture result

- The deterministic script completed the preview path.
- Seventeen of 20 fabricated records ended with the three fields populated.
- The draft-save path remained available.
- Three fabricated records intentionally retained a missing stop condition to expose the reminder and support a second design iteration.

These numbers describe only the authored test fixture. They do not demonstrate user behavior, operational improvement, validity, safety, acceptability or effectiveness.

### What worked in the simulation

- The selected checklist state was visible.
- The reminder stayed non-blocking.
- Missing stop conditions remained visible rather than being silently filled by AI.

### What failed or remains uncertain

- The fixture cannot test accessibility with real users, burden, comprehension, workflow fit or unintended behavior.
- No real authority, data permission, classification or institutional review path is known.
- No claim can be made about a real process or population.

### Human evaluation decision

**Revise and repeat one synthetic iteration.** Add keyboard-focus and high-contrast checks, preserve the same stop conditions, and return to **Assess**. Do not advance to real-world testing.

## Visible continuous-improvement loop

`Assess → Diagnose/Define → Plan → Implement → Evaluate → Assess (iteration 2)`

The first iteration remains immutable in history with its assumptions, artifacts and receipt. The second iteration links to it but receives a new mission-version ID. If data, purpose, setting, catalog version, authority or intended action changes materially, the system invalidates later gates and requires reassessment.

## Learning and capability evidence

The mission may contribute only **demo-excluded learning progress** for structured problem solving, workflow design, ethical AI and evaluation practice. It cannot earn a badge, credential, research role, authorship, approval or clinical/professional authority. Admissible capability evidence would require a later non-demo mission, human-reviewed artifact, explicit reflection, governed provenance and the pinned mastery criteria.

## Before any real-world use

Start a new mission. Verify the real setting, accountable owner, active hat, delegated authority, jurisdiction, institutional policies, data classification/rights, relevant community or sovereign governance, accessibility requirements, review routes and success/stop criteria. A qualified human process—not DISCOVER or Hermes—must determine research/QI/clinical/product classification, IRB/HRO and other approvals, consent requirements, scientific validity and permission to act.
