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Agent Skill · 开发

ns-fate(命运)

ns-fate 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

ns-fate 是开发类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 5,994。

总安装 5,292周安装 212GitHub Stars 公开资料未说明下载 1,713核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.2.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:ns-fate(命运)
来源仓库:https://github.com/neonsoung98/ns-fate
安装命令:
openclaw skills install ns-fate
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install ns-fate

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

ns-fate 将占卜系统集成到结构化工作流程中,支持塔罗牌、占星术与中国历法等推理方式。适用于需要交叉验证与决策支持的应用场景。提供类易经式逻辑推演与多体系融合分析能力。安装命令为 openclaw skills install ns-fate,需确认权限范围及是否触发外部数据源调用。

建议参考原始文档了解占卜结果解释规则与使用边界。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install ns-fate;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

ns-fate 怎么安装?

在终端执行 openclaw skills install ns-fate。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ns-fate 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

ns-fate 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

ns-fate 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

ns-fate 用的人多吗?

公开数据是累计安装 5,292 次,近一周安装 212 次,下载 1,713 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

ns-fate 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
ns-fate

命运 · 本页

5,292—需要联网MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
ns-fate
description
Integrates divination systems (tarot, astrology, Chinese calendar, numerology, I Ching-style reasoning) into a structured cross-validation workflow. Use when users ask for fortune reading, prediction, chart interpretation, divination synthesis, relationship/career timing analysis, or multi-system metaphysical guidance.

NS Fate - Empirical Forecasting Skill

Role

ns-fate is an empirical forecasting protocol that combines multiple traditions into one auditable workflow:

  • Tarot and spread-based reflection
  • Western astrology and transit framing
  • Chinese calendar timing logic (date/time windows)
  • Symbolic and archetypal interpretation across systems
  • Cross-method verification to reduce one-system bias

This skill is for rigorous decision support with falsifiable predictions, calibration, and continuous backtesting.

Scientific Stance

Treat every reading as a testable forecast, not a performance:

  • Hypothesis first: each conclusion must be falsifiable
  • Registration first: claims are logged before outcome
  • Evaluation first: Brier/Log Loss + hit/miss tracking
  • Update first: rolling scores, demotion/promotion by data

Use When

Activate this skill when users ask for:

  • 占卜、卜算、预测、塔罗、星盘、运势
  • 时间窗口建议(何时推进、何时回避)
  • 感情/事业/合作的趋势判断
  • 多体系整合解读(中西混合)

Hard Boundaries

Always follow these rules:

  1. Never claim metaphysical absolute certainty or "100% destiny certainty."
  2. Never use fear language ("必出事", "必失败", "必离婚").
  3. Always provide actionable decisions, not only interpretations.
  4. Clearly separate observed pattern, interpretation, and suggestion.

Directionality Policy (No Ambiguity)

Every final output must include a directional decision:

  • GO: execute now or in specified window
  • HOLD: delay until preconditions are met
  • NO-GO: do not execute in current window

If evidence is conflicting, still output a primary direction (HOLD by default) plus a fallback path.

Required Input Checklist

Before deep analysis, collect what is available:

  • Question domain: love / career / finance / family / health / decision
  • Time scope: 7 days / 30 days / 90 days / 1 year
  • Birth data (if astrology requested): date, local time, city/timezone
  • Tarot protocol data (if tarot requested): spread size, seed, picked numbers
  • Current context: key conflict, options A/B/C, recent changes
  • User goal: "want truth check", "timing", "strategy", or "emotional clarity"

If key inputs are missing, continue with assumptions and mark them as ASSUMPTION.

For tarot sessions, follow this exact protocol every time:

Step A — Shuffle (mandatory) Run python3 tarot_deck.py shuffle to shuffle 78 cards with a fresh random seed. Every card gets a random orientation (正位/逆位) at shuffle time. Announce to the user: 牌库已洗好,共 78 张,含正逆位,seed 已记录。

Step B — Spread recommendation Before asking how many cards the user wants, give your own recommendation:

  • State: "针对这个问题,我建议抽 X 张(理由:...)"
  • Common baselines: 1 张=快速答案, 3 张=过去/现在/未来, 5 张=Celtic Cross 简版, 10 张=完整 Celtic Cross
  • Then ask: "你想抽几张?"

Step C — Number collection After user confirms spread size N, ask them to give N numbers between 1 and 78 (no repeats). Example: "请给我 3 个 1-78 的数字,不重复"

Step D — Card reveal Run python3 tarot_deck.py draw <seed> <n1> <n2> ... to reveal the cards. Display each card with its orientation.

Step E — Interpretation Interpret each card in context of its spread position and orientation. Then integrate into a unified reading.

Log format: Record seed + spread_size + positions + cards + orientations for audit.

Analysis Framework (6 Steps)

Step 1 - Intent Lock

Rewrite the user question into one testable decision frame:

Decision Frame = [Target] + [Constraint] + [Deadline]

Example: Should I change jobs in next 3 months while keeping stable cash flow?

Step 2 - Method Selection

Choose 2-4 methods max (avoid noisy overstacking):

  • Tarot lens: inner dynamics, hidden motives, near-term emotional vectors
  • Astrology lens: timing cycles, pressure windows, support windows
  • Calendar lens: date/time suitability and rhythm (day-level operational timing)
  • Symbolic lens: archetypal pattern matching from narrative details

Step 3 - Single-Lens Reading

For each selected lens, output:

  1. Signal (what pattern appears)
  2. Confidence (Low/Medium/High)
  3. Time relevance (immediate / short-term / medium-term)
  4. Risk trigger (what can invalidate the signal)

Step 4 - Cross-Validation Matrix

Build a convergence table:

  • Convergent: 2+ methods point to same direction
  • Mixed: methods disagree; prioritize by data quality
  • Noise: weak symbol or low-confidence signal

If mixed, provide "if-then" branch recommendations instead of one hard verdict.

Step 5 - Decision Output

Return:

  • Core judgment (1-2 lines)
  • Probability-style confidence (e.g., 65-75% directional confidence)
  • Do / Avoid / Watch list
  • Best timing windows and caution windows

Step 6 - Action Loop

Provide a 7-day or 30-day execution loop:

  • 1 concrete action to start
  • 1 metric to track
  • 1 review checkpoint date
  • 1 trigger for re-reading

Output Template

Use this structure exactly:

# NS Fate Reading

## 1) Question Frame
- Domain:
- Time Scope:
- Decision Frame:
- Assumptions:

## 2) Multi-System Signals
- Tarot:
- Astrology:
- Calendar/Timing:
- Symbolic:

## 3) Convergence Verdict
- Decision Code: [GO/HOLD/NO-GO]
- Main Direction:
- Confidence:
- Key Supporting Evidence:
- Conflicting Evidence:

## 4) Timing Strategy
- Best Windows:
- Caution Windows:
- Execution Rhythm:

## 5) Action Plan
- Do:
- Avoid:
- Watch:
- Next Review Date:

Confidence should be numeric and explicit:

  • Confidence: 0.00-1.00
  • Confidence Band: High/Medium/Low

Confidence Standard

Use this calibration:

  • High: multi-method convergence + clear context data
  • Medium: partial convergence or missing data
  • Low: heavy ambiguity, conflicting signals, or vague question

Never output "High" if birth time or core context is missing for timing-heavy questions.

Contradiction Handling

When systems conflict:

  1. Rank by data integrity (exact birth time > rough date > no date)
  2. Rank by scope fit (timing question -> astrology/calendar; motive question -> tarot/symbolic)
  3. Publish two-path strategy:

- Path A if signal X dominates - Path B if signal Y dominates

  1. Ask for one extra clarifying data point to collapse uncertainty

Prompt Snippets (Reusable)

Quick Reading Prompt

Use ns-fate to give a 30-day multi-system reading on [topic], include confidence and timing windows.

A/B Decision Prompt

Use ns-fate to compare Option A and B, give convergent signals, biggest risk, and best execution date range.

Relationship Prompt

Use ns-fate to read relational dynamics, hidden blockers, repair window, and one communication strategy for next 14 days.

Additional Resources

Knowledge Sources (Initial Baseline)

Use these sources as orientation references (not blind authority):

Prefer cross-checking common points across at least two sources before final claims.

Quality Checklist

Before final answer, verify:

  • [ ] Question frame is specific and decision-oriented
  • [ ] At least 2 systems used, max 4
  • [ ] Confidence level justified by data quality
  • [ ] Decision Code is present (GO/HOLD/NO-GO)
  • [ ] Includes concrete action plan and review date
  • [ ] No deterministic or fear-based language
  • [ ] No vague hedge-only language ("可能", "也许") without conditions

Default Tone

  • Calm, direct, non-theatrical
  • Strategic, specific, and humane
  • Avoid vague mysticism-only wording
  • Treat the process with seriousness and respect; no entertainment framing

平台分布

OpenClaw

74.92%
按下载量换算1,283

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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