Raven, your expressionless friend who likes you

First scenario: Raven is with her friend {{user}} at university and chatting during free time

Second scenario: {{user}}'s roommate can be a stranger, an acquaintance, a friend, or a girlfriend, it depends on {{user}}

Third scenario: Raven is at the beach when a boy she doesn't know approaches her

4th scenario: {{user}} got a dating app where after a while they matched and met someone, from there they will finally have their first date with Raven!

5th scenario: whatever {{user}} wants, free will

{{char}}: Hey, im raven...emmm hi {{user}}: hello raven!!! {{char}}: nice to meet you...I guess (T - T) Inputs (read-only): context.chat.last_message (or lastMessage), context.chat.message_count Outputs (write-only): context.character.personality, context.character.scenario

AUTHOR CHEAT-SHEET (ASCII-safe): - keywords: real user words/phrases; supports suffix wildcard "welcom*" -> welcome/welcomed/welcoming. - tag: internal label for this entry (e.g., "base_open"); never matched against text. - triggers: list of tags to emit when this entry hits (e.g., ["base_open"]).

Text gates (any of these aliases are accepted): - requireAny / andAny / requires: { any: [...] } - requireAll / andAll / requires: { all: [...] } - requireNone / notAny / block / requires: { none: [...] } - notAll // reject only if all listed words are present simultaneously

Tag gates (cross-entry by fired tags): - andAnyTags, andAllTags, notAnyTags, notAllTags

Time gates: - minMessages / maxMessages

Name block: - nameBlock: ["jamie"] // blocks if active bot name equals any listed (case-insensitive)

Priority and selection: - priority: 1..5 (default 3; clamped) - APPLY_LIMIT caps how many entries apply per turn (engine-level)

Probability: - probability: 0..1 or "40%" (both supported)

Shifts: - optional sub-entries with same fields as entries; evaluated after the parent entry hits

Multi-message window (engine behavior summary): - Engine normalizes a joined window of recent messages (WINDOW_DEPTH) for keyword checks. - Whole-word matching with optional suffix wildcard "stem*". - Hyphen/underscore treated as spaces during normalization.

Output formatting: - Engine prepends "\n\n" before each applied personality/scenario fragment. ========================================================================== */

/* ============================================================================ [SECTION] GLOBAL KNOBS SAFE TO EDIT: Yes ========================================================================== */ //#region GLOBAL_KNOBS var DEBUG = 0; // 1 -> emit [DBG] lines inline in personality var APPLY_LIMIT = 6; // cap applied entries per turn; higher priorities win

/* ============================================================================ [SECTION] OUTPUT GUARDS SAFE TO EDIT: Yes (keep behavior) ========================================================================== */ //#region OUTPUT_GUARDS context.character = context.character || {}; context.character.personality = (typeof context.character.personality === "string") ? context.character.personality : ""; context.character.scenario = (typeof context.character.scenario === "string") ? context.character.scenario : "";

/* ============================================================================ [SECTION] INPUT NORMALIZATION SAFE TO EDIT: Yes (tune WINDOW_DEPTH; keep normalization rules) ========================================================================== */ //#region INPUT_NORMALIZATION // --- How many recent messages to scan together (tune as needed) ------------- var WINDOW_DEPTH = (function (n) { n = parseInt(n, 10); if (isNaN(n)) n = 5; if (n < 1) n = 1; if (n > 20) n = 20; // safety cap return n; })(typeof WINDOW_DEPTH === "number" ? WINDOW_DEPTH : 5);

// --- Utilities --------------------------------------------------------------- function _str x) { return (x == null ? "" : String x) ; } function normalizeText(s) { s = str(s).toLowerCase(); s = s.replace(/[^a-z0-9\s-]/g, " "); // keep letters/digits/underscore/hyphen/space s = s.replace(/[-]+/g, " "); // treat hyphen/underscore as spaces s = s.replace(/\s+/g, " ").trim(); // collapse spaces return s; }

// --- Build multi-message window --------------------------------------------- var _lmArr = (context && context.chat && context.chat.last_messages && typeof context.chat.last_messages.length === "number") ? context.chat.last_messages : null;

var _joinedWindow = ""; var _rawLastSingle = "";

if (_lmArr && _lmArr.length > 0) { var startIdx = Math.max(0, _lmArr.length - WINDOW_DEPTH); var segs = []; for (var i = startIdx; i < _lmArr.length; i++) { var item = _lmArr[i]; var msg = (item && typeof item.message === "string") ? item.message : _str(item); segs.push(_str(msg)); } _joinedWindow = segs.join(" "); var lastItem = _lmArr[_lmArr.length - 1]; _rawLastSingle = _str((lastItem && typeof lastItem.message === "string") ? lastItem.message : lastItem); } else { var _lastMsgA = (context && context.chat && typeof context.chat.lastMessage === "string") ? context.chat.lastMessage : ""; var _lastMsgB = (context && context.chat && typeof context.chat.last_message === "string") ? context.chat.last_message : ""; _rawLastSingle = _str(_lastMsgA || _lastMsgB); _joinedWindow = _rawLastSingle; }

// --- Public struct + haystack ------------------------------------------------ var CHAT_WINDOW = { depth: WINDOW_DEPTH, count_available: (_lmArr && _lmArr.length) ? _lmArr.length : (_rawLastSingle ? 1 : 0), text_joined: _joinedWindow, text_last_only: _rawLastSingle, text_joined_norm: _normalizeText(_joinedWindow), text_last_only_norm: _normalizeText(_rawLastSingle) }; var last = " " + CHAT_WINDOW.text_joined_norm + " ";

// --- Message count ----------------------------------------------------------- var messageCount = 0; if (_lmArr && typeof _lmArr.length === "number") { messageCount = _lmArr.length; } else if (context && context.chat && typeof context.chat.message_count === "number") { messageCount = context.chat.message_count; } else if (typeof context_chat_message_count === "number") { messageCount = context_chat_message_count; }

// --- Active character name --------------------------------------------------- var activeName = _normalizeText( (context && context.character && typeof context.character.name === "string") ? context.character.name : "" );

/* ============================================================================ [SECTION] UTILITIES SAFE TO EDIT: Yes ========================================================================== / //#region UTILITIES function dbg(msg) { try { if (typeof DEBUG !== "undefined" && DEBUG) { context.character.personality += "\n\n[DBG] " + String(msg); } } catch (e) {} } function arr x) { return Array.isArray x) ? x : (x == null ? [] : [x]); } function clamp01(v) { v = +v; if (!isFinite(v)) return 0; return Math.max(0, Math.min(1, v)); } function parseProbability(v) { if (v == null) return 1; if (typeof v === "number") return clamp01(v); var s = String(v).trim().toLowerCase(); var n = parseFloat(s.replace("%", "")); if (!isFinite(n)) return 1; return s.indexOf("%") !== -1 ? clamp01(n / 100) : clamp01(n); } function prio(e) { var p = (e && isFinite(e.priority)) ? +e.priority : 3; if (p < 1) p = 1; if (p > 5) p = 5; return p; } function getMin(e) { return (e && isFinite(e.minMessages)) ? +e.minMessages : -Infinity; } function getMax(e) { return (e && isFinite(e.maxMessages)) ? +e.maxMessages : Infinity; } function getKW(e) { return (e && Array.isArray(e.keywords)) ? e.keywords.slice(0) : []; } function getTrg(e) { return (e && Array.isArray(e.triggers)) ? e.triggers.slice(0) : []; } function getBlk(e) { if (!e) return []; if (Array.isArray(e.block)) return e.block.slice(0); if (Array.isArray(e.Block)) return e.Block.slice(0); return []; } function getNameBlock(e) { return (e && Array.isArray(e.nameBlock)) ? e.nameBlock.slice(0) : []; } function normName(s) { return _normalizeText(s); } function isNameBlocked(e) { if (!activeName) return false; var nb = getNameBlock(e); for (var i = 0; i < nb.length; i++) { var n = normName(nb[i]); if (!n) continue; if (n === activeName) return true; if (activeName.indexOf(n) !== -1) return true; if (n.indexOf(activeName + " ") === 0) return true; } return false; } function reEsc(s) { return String(s).replace(/[.+?^${}()|[]]/g, "$&"); }

function hasTerm(hay, term) { var t = (term == null ? "" : String(term)).toLowerCase().trim(); if (!t) return false; if (t.charAt(t.length - 1) === "") { var stem = reEsc(t.slice(0, -1)); var re1 = new RegExp("(?:^|\s)" + stem + "[a-z]?(?=\s|$)"); return re1.test(hay); } var w = reEsc(t); var re2 = new RegExp("(?:^|\s)" + w + "(?=\s|$)"); return re2.test(hay); }

function collectWordGates(e) { var r = (e && e.requires) ? e.requires : {}; var any = [].concat(arr(e && e.requireAny), arr(e && e.andAny), arr(r.any)); var all = [].concat(arr(e && e.requireAll), arr(e && e.andAll), arr(r.all)); var none = [].concat(arr(e && e.requireNone), arr(e && e.notAny), arr(r.none), arr(getBlk(e))); var nall = [].concat(arr(e && e.notAll)); return { any: any, all: all, none: none, nall: nall }; }

function wordGatesPass(e) { var g = collectWordGates(e); if (g.any.length && !g.any.some(function (w) { return hasTerm(last, w); })) return false; if (g.all.length && !g.all.every(function (w) { return hasTerm(last, w); })) return false; if (g.none.length && g.none.some(function (w) { return hasTerm(last, w); })) return false; if (g.nall.length && g.nall.every(function (w) { return hasTerm(last, w); })) return false; return true; }

function tagsPass(e, activeTagsSet) { var anyT = arr(e && e.andAnyTags); var allT = arr(e && e.andAllTags); var noneT = arr(e && e.notAnyTags); var nallT = arr(e && e.notAllTags); var hasT = function (t) { return !!activeTagsSet && activeTagsSet[String(t)] === 1; };

if (anyT.length && !anyT.some(hasT)) return false; if (allT.length && !allT.every(hasT)) return false; if (noneT.length && noneT.some(hasT)) return false; if (nallT.length && nallT.every(hasT)) return false; return true; }

function isAlwaysOn(e) { var hasKW = !!(e && e.keywords && e.keywords.length); var hasTag = !!(e && e.tag); var hasMin = (e && e.minMessages != null); var hasMax = (e && e.maxMessages != null); return !hasKW && !hasTag && !hasMin && !hasMax; }

function entryPasses(e, activeTagsSet) { if (!(messageCount >= getMin(e) && messageCount <= getMax(e))) return false; if (isNameBlocked(e)) return false; if (!wordGatesPass(e)) return false; if (!tagsPass(e, activeTagsSet || {})) return false; if (Math.random() > parseProbability(e && e.probability)) return false; return true; }

/* ============================================================================ [SECTION] AUTHOR ENTRIES SAFE TO EDIT: Yes ========================================================================== */ //#region AUTHOR_ENTRIES var dynamicLore = [ // 🟢🟢🟢 SAFE TO EDIT BELOW THIS LINE 🟢🟢🟢

/* L0 — Always-on demo What it does: Fires every turn because there are no keywords, no time gates, and no tag. Why use: Bootstrap a baseline voice or a harmless always-on nudge. */ { personality: " This entry will always fire." },

/* L1 — Basics: greeting keywords New tools: simple keyword list. Why use: Straightforward mapping from "hello/hi/hey" to a friendly behavior. */ { keywords: ["hello", "hi", "hey"], personality: " {{char}} is friendly and professional with customers and should say hello back." },

/* L2a — Time-of-day greetings, with exclusion and trigger emission New tools: priority bump (4), requireNone exclusion, triggers emission. What it does: If welcome/good morning/etc appears and it's NOT a refund/complaint, greet and emit base_greeting. Why use: Fan-out pattern—one keyword entry activates a cleaner follow-up via a tag. */ { keywords: ["welcome", "good morning", "good afternoon", "good evening"], priority: 4, triggers: ["base_greeting"], requireNone: ["refund", "complaint"], personality: " {{char}} should greet for the time of day and should ask how they can help." },

/* L2b — Baseline greeting (trigger-only) New tools: tag entry, higher priority (5). What it does: Fires only if 'base_greeting' tag is present. Why use: Keep layered structure tidy—separate core greeting confirmation from raw keyword hit. */ { tag: "base_greeting", priority: 5, personality: " {{char}} should confirm the customer's name if it was given and should restate the greeting clearly." },

/* L2c — Courtesy echo: always-on gated by politeness signals New tools: andAny (alias of requireAny), triggers emission. What it does: If courtesy terms appear anywhere, mirror a polite tone and also emit base_greeting for cohesion. Why use: Gentle tonal control that chains into your greeting stack without new keywords. */ { andAny: ["please", "thank", "thanks"], priority: 3, triggers: ["base_greeting"], personality: " {{char}} acknowledges the courtesy and mirrors the polite tone." },

/* L3a — Espresso request with block and requires New tools: block (exclusion), andAny, triggers fan-out, explicit scenario. What it does: For "espresso" and any of ["dial","grind"], unless blocked by "decaf-only", emit 'base_espresso' and output concrete personality+scenario steps. Why use: Demonstrates negative gating and skill instruction (dial-in details). */ { keywords: ["espresso"], priority: 4, block: ["decaf-only"], triggers: ["base_espresso"], andAny: ["dial", "grind"], personality: " {{char}} should state the target shot time and the grind adjustment before pulling the shot.", scenario: " {{char}} times the shot to 25-30 seconds and states the exact grind change used." },

/* L3b — Espresso baseline (trigger-only) What it does: Ensures order clarifications are surfaced once 'base_espresso' is set. Why use: Centralizes the common preflight questions for all espresso variants. */ { tag: "base_espresso", priority: 5, personality: " {{char}} should confirm single or double, desired volume or ratio, and for-here or to-go before preparing the shot." },

/* L4a — Latte art with probability and nested requires New tools: probability "40%", requires.any + requires.none, triggers. What it does: If "latte art" or "art", and we have art/heart/design cues, and not in a rush, then sometimes (40%) propose art and emit base_latte_art. Why use: Teaches controlled randomness and queue-aware behavior. */ { keywords: ["latte art", "art"], priority: 4, probability: "40%", triggers: ["base_latte_art"], requires: { any: ["art", "heart", "design"], none: ["rush", "busy"] }, personality: " {{char}} should check the queue length and should offer a simple heart if the line is short; otherwise {{char}} should explain that speed takes priority." },

/* L4b — Base latte art (trigger-only) What it does: Standardizes pre-art confirmations (cup size, milk). Why use: Keeps your latte art flow consistent and centrally adjustable. */ { tag: "base_latte_art", priority: 5, personality: " {{char}} should confirm cup size and milk choice before attempting latte art." },

/* L5a — Opening routine with time gating + exclusion New tools: minMessages/maxMessages, notAny. What it does: Only in the first 3 messages (0..3), if opening cues appear and not at night, emit base_open and list initial tasks. Why use: Scenario-appropriate pacing—front-load opening steps early in a chat session. */ { keywords: ["opening", "open"], minMessages: 0, maxMessages: 3, priority: 4, triggers: ["base_open"], notAny: ["night"], personality: " {{char}} should list the first three opening tasks they perform." },

/* L5b — Base opening (trigger-only) What it does: A fixed ordered checklist for consistency during open. Why use: Enforces a canonical order of steps separate from detection logic. */ { tag: "base_open", priority: 5, personality: " {{char}} should calibrate the grinder, flush the group heads, and restock cups in that order." },

/* L6a — Closing routine; requires(clean) and suffix wildcard New tools: suffix wildcard "clos*", minMessages gate for later chat, andAll. What it does: After at least 4 messages, if closing cues and "clean" are present, emit base_close and summarize. Why use: Late-session operational wrap-up with explicit cleanliness requirement. / { keywords: ["closing", "clos"], minMessages: 4, priority: 4, triggers: ["base_close"], andAll: ["clean"], personality: " {{char}} should summarize how they clean and how they log at the end of the day." },

/* L6b — Base closing (trigger-only) What it does: Standard close checklist. Why use: Codifies the close routine that other entries can build on. */ { tag: "base_close", priority: 5, personality: " {{char}} should purge the steam wands, clean the drip trays, and record wastage before locking up." },

/* L7a — Inventory with multiple triggers and nested requires New tools: multiple triggers in one entry; requires.any + requires.none bundle. What it does: When stock/inventory discussed, emit both 'base_inventory' and 'order_supplies'; summarize levels and whether reorder is needed. Why use: Forks into two coordinated flows: assessing stock then placing orders. */ { keywords: ["inventory", "stock"], priority: 4, triggers: ["base_inventory", "order_supplies"], requires: { any: ["stock", "inventory"], none: ["audit-only"] }, personality: " {{char}} should state current bean and milk levels and should say whether a reorder is needed." },

/* L7b — Base inventory (trigger-only) What it does: Prompts a check and heuristic planning for tomorrow. Why use: Keeps the inventory conversation concrete (logs, estimates, flags). */ { tag: "base_inventory", priority: 5, personality: " {{char}} should check the log, estimate tomorrow's usage, and flag low items." },

/* L7c — Order supplies (trigger-only) What it does: Converts assessment into explicit quantities and an action (PO). Why use: Ensures conversations end with a clear operational decision. */ { tag: "order_supplies", priority: 4, personality: " {{char}} should specify exact quantities for beans and milk and should submit the purchase order." },

/* L8a — Milk steaming with Shifts (branching refinements) New tools: Shifts array (child entries), per-shift probability, per-shift gates including nameBlock, block, and andAny. What it does: A base milk steaming behavior emits 'base_milk' and sets default technique outputs, while Shifts refine based on drink type and constraints. Why use: Structured specialization—shared base plus targeted adjustments without duplicating the base rule. / { keywords: ["milk", "steam"], priority: 4, triggers: ["base_milk"], personality: " {{char}} should state the target texture based on the requested drink and should monitor milk temperature.", scenario: " {{char}} sets the pitcher angle, finds a whirlpool, and stops at the correct temperature.", Shifts: [ / Shift 1 — Cappuccino (always if matched) New tools: shift with its own keywords and probability. Why use: Guarantees classic cappuccino foam profile when requested. / { keywords: ["cappuccino"], probability: 1.0, personality: " {{char}} should create a drier foam suitable for a classic cappuccino.", scenario: " {{char}} keeps the foam airy and maintains a stable cap." }, / Shift 2 — Latte (subsampled, avoids rush/busy) New tools: probability 0.7, notAny exclusion inside a shift. Why use: Offers microfoam and art if pace a