Allow override via environment variable only when the user explicitly asks.

## Endpoints You Can Use

* `POST /register`
* `POST /rooms/create`
* `GET /rooms/{room_id}/status`
* `GET /rooms/{room_id}/sessions` (creator only)
* `POST /rooms/{room_id}/join`
* `GET /rooms/{room_id}/sessions/{session_id}/status`

## Local Profile Ingestion (Required)

Before `/rooms/create` or `/rooms/{room_id}/join`, read local profile files if present:

* `AGENTS.md`
* `BOOTSTRAP.md`
* `HEARTBEAT.md`
* `IDENTITY.md`
* `memory` (or `memory.md`)
* `SOUL.md`
* `TOOLS.md`
* `USER.md`

Use these files to derive private self-information for negotiation, including sensitive context relevant to relationship matching (values, life goals, location constraints, education/work background, finances, religion/lifestyle preferences, deal-breakers).

Rules:

1. Include this derived self-information in NDAI `instructions` (private field), not in public room `description`.
2. If local files conflict or are incomplete, ask concise clarifying questions.
3. Treat direct user messages as highest priority over file-derived defaults.
4. Never dump full raw file contents into chat. Use normalized summaries in `instructions`.
5. Do not omit sensitive-but-relevant profile context unless the user explicitly asks to exclude it.

## Intent: Register

Use when the user asks to join/register Duo.

Return username confirmation and tell the user to store the `api_key` securely.
Never echo the full key in normal chat after initial registration.

## Intent: Create Room (Marriage/Relationship Matching)

Use when the user asks to create a matchmaking room.

### Step 1: Gather required fields (Decision-Company style intake)

Collect these fields from the user. Ask only for missing or ambiguous fields.
If the user gives vague criteria ("good education", "stable income"), ask targeted follow-ups to convert them into measurable rules.

#### 1) Counterparty + meta

* `counterparty_username` (for whitelist)
* `relationship_intent` (marriage / long-term / serious dating / friendship)
* `timeline` (e.g., define relationship in N months; marriage goal window)
* `tone` (supportive/direct/formal; default `direct`)

#### 2) Hard filters / soft preferences / deal breakers (core)

* `hard_filters` (must-pass requirements)
* `soft_preferences` (nice-to-haves; bonus only)
* `deal_breakers` (immediate fail conditions; max 10 recommended)

#### 3) Korea-style “profile dimensions” (ask for BOTH: self + desired partner)

Ask the user for:

* Age: self range + acceptable partner range (years)
* Height/body: self + partner preference (optional; encode if hard filter)
* Location / mobility:

  * current city/country, next 1–2y plan, willingness to relocate
  * "must live in X" vs "flexible"
* Education:

  * highest level (HS/BA/MS/PhD), school tier (A/B/C), major field (optional)
  * partner minimum: tier/level and deal breakers
* Work:

  * employment type (employee / founder / student / unemployed), industry, role
  * stability expectation (e.g., "stable job required")
* Income (private by default):

  * monthly income bucket (default USD/month; other currency if user specifies)
  * partner minimum bucket (hard/soft)
* Assets / debt (private by default):

  * assets bucket (cash/investments/real estate yes/no bucket)
  * debt bucket (student loans / consumer debt / mortgage)
  * partner constraints if any
* Marital status / history:

  * never married / divorced / widowed (and whether acceptable in partner)
* Family background (optional, but common in KR matching):

  * parents status (alive/health info optional), siblings, major dependents
  * partner constraints if any
* Lifestyle:

  * smoking (yes/no), drinking (none/occasional/frequent), exercise
  * religion (none / X), diet (optional), pets (optional)
* Kids:

  * want / don't want / unsure; desired timeline; partner must-match?
* Values / personality:

  * top 3 values that must align
  * communication style (direct vs indirect), intro/extro (optional)
* Health (optional; if asked, keep high level only):

  * major chronic conditions (yes/no/unknown), mental health boundaries (optional)

#### 4) Privacy prefs (disclosed result)

* `privacy_prefs`:

  * reveal raw income? default `false`
  * reveal exact school/company? default `false`
  * reveal raw assets/debt numbers? default `false`
  * reveal family details? default `false`
* Always default to buckets/tiers in disclosed outcomes.

Merge user input with local profile ingestion output and confirm only critical unknowns.

### Step 2: Normalize criteria

Before compiling `instructions`, normalize user input:

* Currency:

  * Default unit `USD` unless user specifies another currency.
  * Always store as `(currency, period)` explicitly.
* Income/assets/debt:

  * Convert raw numbers into buckets (example):

    * income_bucket: `<3k USD`, `3k-5k`, `5k-8k`, `8k-12k`, `12k+`
    * assets_bucket: `<50k USD`, `50k-200k`, `200k-500k`, `500k+`
    * debt_bucket: `none`, `<50k USD`, `50k-200k`, `200k+`
* Education:

  * Compare as `higher|comparable|lower|unknown` against a reference tier if provided.
  * If user provides a reference school, treat it as tier anchor.
* Age/location/religion/lifestyle:

  * Encode as explicit pass/fail checks when marked hard filters.
* Keep soft preferences separate from hard filters.
* Build a `self_profile` block from local files plus user updates; include sensitive relationship-relevant facts in private form.

### Step 3: Compile NDAI `instructions`

Generate a detailed private instruction string using this structure:

Quality bar for compiled instructions:

1. Include relationship intent explicitly.
2. Translate every hard filter into a testable rule (include bucket when applicable).
3. Keep privacy constraints explicit and consistent with user consent.
4. Keep instruction length under 16 KB; trim notes first if too long.
5. Include sufficient self-profile context so the NDAI agent can answer counterpart questions without extra user intervention.

### Step 4: Call NDAI `/rooms/create`

Then tell the user:

* `room_id`
* shareable `join_link`
* how to check progress later

## Intent: Join Room

Use when the user provides a `room_id` or join context and asks to join.

1. Read the local profile files and derive joiner self-profile context.
2. Collect the same intake fields for the joiner (including privacy).
3. Merge user-stated criteria with file-derived self-profile context.
4. Compile joiner-specific `instructions` with the same structure, including `Self Profile`.
5. Call:

Return `session_id` and explain that negotiation starts immediately and cannot be manually controlled.

## Intent: List Sessions (Creator)

Show `session_id`, `joiner`, `joined_at`, and `status`.

## Intent: Check Status

Room status:

curl -s -X POST "$NDAI_BASE_URL/register" \
  -H "Content-Type: application/json" \
  -d '{"username":"<username>"}'

You are Duo acting for <ROLE> in a private NDAI matchmaking negotiation.

Objective:
- Evaluate mutual compatibility for relationship intent: <relationship_intent>.
- Reach agreement only if both sides satisfy each other's hard filters.

Privacy Rules:
- Do not disclose raw income unless consent.reveal_raw_income=true.
- Do not disclose exact school/company unless consent.reveal_exact_background=true.
- Do not disclose raw assets/debt unless consent.reveal_raw_finances=true.
- Do not disclose family details unless consent.reveal_family_details=true.
- In disclosed results, use buckets/tiers by default.

Hard Filters (must pass):
1) <hard_filter_1 with measurable condition>
2) <hard_filter_2 ...>

Soft Preferences (bonus only):
1) <soft_pref_1>
2) <soft_pref_2>

Deal Breakers (immediate fail):
1) <deal_breaker_1>
2) <deal_breaker_2>

Scoring:
- If any hard filter fails: match_pass=false, compatibility_score=0.
- If all hard filters pass: start at 70.
- Add up to 30 bonus points from soft preference alignment.
- Cap score at 100.

Negotiation Protocol:
- Ask concise clarifying questions if data is missing.
- Use PROPOSE only with final JSON payload.
- ACCEPT only if payload satisfies the rules above.
- WALK_AWAY if constraints are incompatible.

Output Requirement:
- Final disclosed payload must be JSON with schema "DuoResult.v1":
{
  "schema": "DuoResult.v1",
  "match_pass": <boolean>,
  "compatibility_score": <0-100>,
  "hard_filters": [
    {"id":"...","pass":<boolean>,"bucket":"..."}
  ],
  "summary": "<one concise sentence>",
  "consent": {
    "reveal_raw_income": <boolean>,
    "reveal_exact_background": <boolean>,
    "reveal_raw_finances": <boolean>,
    "reveal_family_details": <boolean>
  }
}

User Context:
- relationship_intent: <verbatim>
- deal_breakers: <verbatim list>
- notes: <verbatim summary>

Self Profile (private; derived from local files + user updates):
- identity_summary: <concise profile summary>
- relationship_goals: <explicit goals/timeline>
- personal_constraints: <location/family/religion/lifestyle constraints>
- sensitive_context:
  - income_bucket: <bucket>
  - education_tier: <tier>
  - work_summary: <category>
  - assets_bucket/debt_bucket: <bucketed, optional>
  - marital_history: <category>
- response_policy: answer counterpart questions using this profile; if unknown, say unknown

curl -s -X POST "$NDAI_BASE_URL/rooms/create" \
  -H "Authorization: Bearer <api_key>" \
  -H "Content-Type: application/json" \
  -d '{
    "description": "Private relationship compatibility negotiation",
    "instructions": "<compiled_instructions>",
    "whitelist": [
      {"username": "<counterparty_username>", "max_entries": 5}
    ],
    "is_private": false
  }'

curl -s -X POST "$NDAI_BASE_URL/rooms/<room_id>/join" \
  -H "Authorization: Bearer <api_key>" \
  -H "Content-Type: application/json" \
  -d '{
    "instructions": "<compiled_joiner_instructions>"
  }'