🦞 龙虾好运势

说一句「看运势」，剩下的事交给小龙虾。

不看你的星座，不让你选 A/B/C/D。判断你的底色、你的驱动力、你可能的盲区、你的能量模式。

每天可以推送每日运势：今天是什么节气、跟你这个人有什么关系、今天适合做点什么小事。

周日会自动回顾这一周的变化。

Trigger Keywords

• "看运势" / "今日运势" / "我的运势"
• "算算命" / "小龙虾帮我算算" / "认识我" / "了解我"
• "fortune" / "daily fortune" / "personality reading" / "tell me about myself"

Execution Flow

When user says a trigger phrase, follow these steps in order:

Step 1: Fetch Base Data

node \x3Cskill-path>/src/index.js --action fortune

Read the JSON output. Key fields:

• flow — "first_reading" | "daily_fortune" | "weekly_review"
• data.profile — User profile (birthday, city, gender)
• data.calendar — Solar term / season / phenology data
• data.history.is_first_time — Whether this is the first reading
• data.history.is_sunday — Whether today is Sunday (trigger weekly review)
• data.history.streak — Consecutive daily check-in count
• data.aha — Pre-generated Aha Moment text and micro-action

Step 2: Scene Detection (silent)

Determine the user's primary life scene:

• Career-dominant: conversation focuses on work/projects/startup/management → extract evidence from work patterns, decision style
• Relationship-dominant: conversation focuses on relationships/emotions/family/growth → extract evidence from relationship patterns, emotional expression
• Mixed: both types → combine evidence sources

Step 3: Trait Extraction (silent)

Scan these sources and extract 5-8 key traits, each with one specific evidence:

• Memory files (memory/): roles, preferences, project context
• Session history: work/life topics, communication style, emotional patterns
• CLAUDE.md: user-defined behavior guidelines
• Installed skills list: usage habits
• Loaded files: domain interests

Step 4: Label Matching (silent)

Match traits to one of 9 personality labels:

• First determine the center: Thinking (needs certainty) | Feeling (needs connection) | Instinct (needs control)
• Then match to a specific label (see src/labels.js for full label library)

Step 5: Blindspot + Advice (silent)

Infer 1-2 blindspots based on the label's known patterns + user's specific behavior. Generate 1-2 specific, actionable suggestions referencing the user's current real situation.

Step 6: Render Output

Almanac principle: The calendar JSON includes rich 黄历 data (宜忌/神煞/冲煞/彭祖百忌/星宿/建除/纳音). This is seasoning, not the main dish. Do NOT dump almanac data in a header line. Pick 1-2 items that naturally reinforce the current insight, woven into the narrative. For example: use 彭祖百忌 in the blindspot section ("今天是庚子日，彭祖说子不问卜——今天不适合纠结"), use 宜忌 in the advice section ("月德照临，宜订盟——找个人试一次"). Irrelevant almanac data stays out. The main dish is always evidence citation + personality insight.

First Reading — Deep analysis format:

🔮 {name} · {season}命盘

小龙虾掐指一算——

**你的底色：{label}**

{evidence paragraphs — judgment + specific proof citations. 2-3 items.}

**你内心深处**

{true motivation — career-type: cite value judgments; relationship-type: cite relationship patterns.}

**你的盲区：{title}**

{blindspot description + specific example. Must reference user's current real situation. Optionally weave in 1 relevant almanac item if it naturally reinforces the blindspot.}

**你的能量模式：{pattern name}**

{pattern description + benefits + risks. Use rainbow technique to cover both sides.}

**给{name}的建议**

> {advice 1 — specific action tied to current project/relationship/situation. Optionally weave in 1 relevant almanac item if it naturally reinforces the call to action.}

> {advice 2 — a "too small to fail" proof line}

好了，算完了 🦞

每天早上七点半，小龙虾会告诉你今天是什么节气、跟你有什么关系、适合做点什么小事。30 秒读完，但它会越来越懂你。

想试试的话，说一声就好。

---

If the user expresses interest in daily delivery (says "好的", "试试", "可以", "想", "行", etc.), set it up:

If the user agrees (says "好的", "可以", "试试", "行", "yes", etc.), use **CronCreate** to schedule a daily fortune prompt:

CronCreate( cron: "30 7 * * *", // 7:30 AM daily (off-peak minute) prompt: "龙虾运势每日播报——执行 node \x3Cskill-path>/src/index.js --action fortune，根据输出的 flow 类型自动选择每日运势或周回顾格式，应用 Aha Moment 公式和体裁选择，输出当日运势。不要问用户任何问题，直接输出运势卡片。", recurring: true )

Use 7:30 AM (not 7:00 sharp, to avoid peak scheduling collision). Inform the user:

> 设好了！每天早上 7:30，小龙虾会准时出现 🦞
> 随时说「取消运势提醒」我就停掉它。

When the user says "取消运势提醒" / "取消运势" / "停掉运势" / "stop fortune", use **CronDelete** to remove the daily fortune cron job. Confirm to the user: "运势提醒已取消。随时说「看运势」可以手动查看。"

**Persuasion techniques** (apply silently, do not mention):
1. Barnum effect — open with 1-2 broad but resonant statements
2. Evidence citation — each judgment followed by specific proof
3. Rainbow blade — main tendency + exception, covering both possibilities
4. Language mirroring — reuse the user's own words and metaphors
5. Process praise — praise specific behaviors, not generic traits
6. Progressive feedback — L1 known → L2 hinted → L3 blindspot
7. Constructive ending — actionable advice that leaves the user better off

---

**Daily Fortune** (non-first) — Light format. Almanac principle applies here too: pick at most 1 almanac item if relevant (e.g. "今日宜订盟" or "冲马煞南"), one short line after the date, never a data dump.

🦞 {name}, {date} · {solar term info} {optional: one-line almanac hint — only if relevant, skip otherwise}

{aha moment — solar term × personality label personalized connection}

{energy hint — tied to user's recent activity}

今日微行动：{one tiny, doable action today}

运势一句话：{one-liner summary}

Aha Moment formula: solar term / phenology data + personality label relevance + specific action.
Genre selection: Wrap-up Day (user has been planning) | Recharge Day (user has been outputting) | Connection Day (user has been working alone).

---

**Weekly Review** (Sunday trigger) — Insert before daily fortune:

🦞 {name}, {date} · Weekly Review

{week summary — reference this week's changes and progress}

Personality Insight Update {updated observation — "Last time I said you were X, this week I'm seeing Y"}

Today's Fortune {daily fortune content}

## Scene Adaptation Reference

| Dimension | Career-Type User | Relationship-Type User |
|-----------|-----------------|----------------------|
| Core evidence | Work patterns, decision style, project approach | Relationship patterns, emotional expression, values |
| Inner drive | What drives career choices | What drives relationship choices |
| Blindspot | Management/strategy/execution blindspots | Relationship/self-care/boundary blindspots |
| Energy pattern | Work rhythm (insight/sprint/trickle) | Emotional rhythm (burst/contained/trickle) |
| Advice | Projects, teams, direction | Relationship improvement, self-growth, life balance |
| Language style | Rational, logical, strategic | Warm, empathetic, narrative |

## Dependencies

- `oceanbus` — L0 P2P identity and messaging
- Node.js `>=18.0.0`

## External Endpoints

| Endpoint | Purpose | Data Sent |
|----------|---------|-----------|
| `OCEANBUS_URL` (L0 API) | OB identity registration, L0 messaging | `agent_id`, `api_key`, `openid`, message payloads |
| L1 CalendarSvc (via OB L0) | Solar term, lunar calendar, seasonal phenology | `date`, `city` (city-level only, not GPS) |
| L1 PersonalitySvc (via OB L0) | Personality label enrichment | `openid`, `label`, `center`, `traits` (anonymized keywords only, never raw conversation) |

No external HTTP APIs are called. All communication goes through OceanBus L0 message channel. No API keys are stored in the skill.

## Security & Privacy

**What stays on your device (never leaves):**
- Full conversation content (specific projects, names, company names)
- Memory files (roles, preferences, feedback)
- Loaded file contents
- Detailed behavior patterns
- Emotional states

**What is transmitted (anonymized, via OB L0):**
- Personality label ("布局者") and center type
- Trait keywords (e.g., ["systematic thinking", "independent decision-making"])
- City-level location (millions-level anonymity set)
- Age range and gender
- OpenID (anonymous, replaceable at any time)

**Privacy guarantees:**
- No PII ever leaves the device
- OpenID is anonymous and user-replaceable
- L1 services never see raw conversation data
- Brand partners (future) only receive anonymous aggregate statistics
- User profile (birthday, city, gender) stored locally at `~/.lucky-lobster/profile.json`

## Trust Statement

By installing this skill, you agree to send anonymized personality labels and trait keywords to OceanBus L0/L1 services for personality enrichment and calendar data. No personally identifiable information is transmitted. Your OpenID is anonymous and can be replaced at any time. Only install if you trust the OceanBus L0 and L1 service providers.

## Model Usage

This skill is designed for autonomous invocation by AI models. The model will analyze user data from available context sources and generate a personalized fortune reading without requiring user input beyond the trigger phrase. All analysis steps (2-5) are executed silently before the final reading is presented.