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x-mentor-skill-nuwax 导师技能女娲

Agent Skill

x-mentor-skill-nuwa 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

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CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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:x-mentor-skill-nuwa(x 导师技能女娲)
来源仓库:https://github.com/aradotso/trending-skills
仓库路径:skills/x-mentor-skill-nuwa
安装命令:
npx skills add https://github.com/aradotso/trending-skills --skill x-mentor-skill-nuwa
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/aradotso/trending-skills --skill x-mentor-skill-nuwa

简介

x-mentor-skill-nuwa 用于查找、检索和筛选相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词或任务场景快速定位候选结果。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装使用。
  • 需确认权限范围和维护状态,注意是否触发联网、命令执行或文件读写操作。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

X导师.skill (X Mentor Skill)

Skill by ara.so — Daily 2026 Skills collection.

A Claude Code skill that distills methodologies from 6 top X creators (Nicolas Cole, Dickie Bush, Sahil Bloom, Justin Welsh, Dan Koe, Alex Hormozi) plus open-source X algorithm data into a complete content operating system. Covers hook engineering, thread writing, growth mechanics, account diagnostics, and monetization strategy.


Installation

npx skills add alchaincyf/x-mentor-skill

After installation, activate with natural language:

"帮我写条推文"
"X策略"
"推特怎么涨粉"
"帮我审阅这条tweet"
"分析我的X账号 @username"

Five Core Scenarios

Scenario A — Write a Tweet

Trigger: Any request to write/create a tweet or thread.

Workflow:

  1. Clarify format (short tweet vs. thread) and target audience
  2. Generate 3 hook variants with formula labels
  3. Recommend best version with rationale
  4. Flag algorithm risks (external links, timing)

Example interaction:

User: Write a tweet about building an AI coding assistant with Claude Code

X导师: Format — short tweet or thread? Target audience?

User: Short tweet, for AI developers

X导师: 3 Hook versions:

Version A (Curiosity Gap + Credibility Anchor):
"I built an AI coding assistant with Claude Code.
48 hours. Zero frameworks. Pure vibe coding.
The surprising part wasn't that it worked — it was 3x faster than my handwritten version."

Version B (Value Equation: High Result / Low Effort):
"Can't code? Doesn't matter.
I just built an AI coding assistant in 2 days using Claude Code.
Wrote zero lines of code myself. Full walkthrough:"

Version C (Contrarian + Specific Data):
"Most AI coding assistant benchmarks are demo-level.
I built one that's actually in production using Claude Code.
The difference? 30 edge cases and an AI willing to debug until 3am."

→ Recommend Version A: has specific numbers (48h/3x) and a suspense hook.
→ Best publish time: Weekdays 9–11am.
⚠️ No external links in the post body — put them in the first reply.

Scenario B — Review a Hook

Trigger: User pastes existing tweet content asking for feedback.

Scoring rubric:

  • Curiosity gap (does it create a must-read-on feeling?)
  • Credibility anchor (why should I believe you?)
  • Specific benefit (what will I get?)

Example:

User: Review this: "I tested 5 AI coding tools. Here's what I found."

X导师: Hook Score: 3/10

Diagnosis:
❌ No curiosity gap — no suspense, reader doesn't need to click
❌ No credibility anchor — anyone can say "I tested"
❌ No specific benefit — "what I found" is too vague

Rewrite:
"I stress-tested 5 AI coding tools on the same project
(a full-stack app in 48 hours).
One saved me 12 hours. The rest were useless.
A thread:"

Changes: Added specific scenario (full-stack app), time anchor (48h),
suspense (which one?), credibility (real test data).

Scenario C — Topic Selection

Trigger: User asks for content ideas or is stuck on what to post.

4A Topic Matrix: Generate one topic × 4 angles = unlimited content:

AngleDescriptionExample
ActionableHow-to, step-by-step"5 steps to X"
AnalyticalData, research, breakdown"I analyzed 100 posts..."
AspirationalVision, transformation"In 2 years I went from..."
AnthropologicalObservation, patterns"Why most people fail at..."

Lean Validation Flywheel:

Tweet (validate) → Thread (expand) → Newsletter (deepen) → Product (monetize)

Never write long-form until a tweet has proven the idea resonates.


Scenario D — Growth Strategy

Trigger: User asks about follower growth, algorithm, or monetization.

X Algorithm Key Weights (from open-source code, April 2026):

Conversation reply (author replies back to you): 150x
Regular reply:                                    27x
Dwell time (>2 minutes):                         20x
Retweet:                                          2x
Like:                                             1x (baseline)

TweepCred System:

Non-Premium user baseline:    -128 points
Distribution threshold:       +17 points
Premium subscription bonus:  +100 points (instant)
Gap without Premium:         -145 points below threshold

Growth phases:

0–1K (Cold Start):
- Post 2–3 short tweets/day to find resonant topics
- Leave 5–10 high-quality replies (200–400 words) on large accounts daily
- DM 3 same-size creators/week for mutual support
- No threads yet — find your high-ER topics first
- Expected: 5–10 followers/day → 1K in 4–8 weeks

1K–10K (Flywheel):
- Weekly thread on proven topics
- Activate "Public Building" — document your process
- Start email list (algorithm changes, newsletters don't)
- Expected: 30–50 followers/day

10K+ (Monetization):
- Cohort courses / 1-on-1 coaching / digital products
- Justin Welsh model: $12M/year, 90% margin, solopreneur

Critical warnings:

⚠️ External links in post body: -30–50% reach
⚠️ Non-Premium links: median engagement = 0
⚠️ "Great post!" replies: algorithm detects and ignores engagement bait

Scenario E — Account Diagnostics

Trigger: User asks to analyze their X account.

Data collection (3-tier fallback):

# Tier 1: Automatic via computer-use / browser tools
# Tier 2: User pastes exported data
# Tier 3: User manually provides metrics

# Data saved to:
user-data/{username}/
├── profile.md              # Account basics
├── tweets_{date}.json      # Raw tweet data
├── tweets_{date}.md        # Human-readable summary
├── report_{date}.html      # Economist-style HTML report
└── strategy.md             # Personalized strategy

Diagnostic report sections:

  1. KPI Dashboard — followers, ER rate, posting frequency
  2. Content ROI — which content types deliver most engagement per hour invested
  3. Distribution Funnel — impressions → likes → replies → follows
  4. Time Analysis — best/worst posting windows
  5. Brand Narrative — positioning clarity score
  6. Action Plan — top 3 highest-ROI changes

Persistent memory: On every activation, the skill checks user-data/{username}/ for historical data:

  • Found + <30 days old → silently load personalized strategy
  • Found + >30 days old → suggest re-diagnosis
  • Not found → offer full diagnosis

6 Core Mental Models

ModelOne-linerSource
Lean Validation FlywheelTweet to validate → expand if data supportsCole/Bush + Sahil + Hormozi + Welsh
Attention EngineeringFirst 2 lines decide everything; hooks can be engineeredCole + Hormozi (Value Equation)
Category CreationDon't fight for a niche — create one only you ownCole (Snow Leopard) + Koe (Niche of One)
Value Front-LoadingGive away the secret for free, sell the executionHormozi + Welsh + Sahil
Build in PublicTurn your process into content; audience becomes stakeholderslevelsio + swyx
Systematic CompoundingTemplates replace inspiration; output becomes predictableWelsh (Content OS) + Koe (2 Hour Writer)

10 Decision Heuristics

1. Tweet before writing long-form    — tweets are idea refineries
2. Hook gets 50% of creative time    — write 10–15 versions, pick the best
3. Conversation beats everything     — a reply = 150 likes (X open source)
4. 1/3/1 rhythm                      — 1 hook + 3 expansion + 1 transition
5. Super Bowl Response               — new model launch = respond within 1 hour
6. Own your audience                 — algorithms change, newsletters don't
7. 4A Topic Matrix                   — 1 topic × 4 angles = unlimited content
8. Give secrets, sell execution      — 99% of readers won't do it themselves
9. Templates beat inspiration        — Cole uses 7 templates for 200+ threads
10. Replies are gold mines           — one reply can get 6,700 impressions

Hook Templates (Nicolas Cole's 7 Core Formats)

## Template 1: The Curiosity Gap
"[Common belief]. But [surprising exception].
Here's what no one tells you:"

## Template 2: The Numbered List Hook
"[X] things I learned from [credible source/experience]:"

## Template 3: The Contrarian Take
"Unpopular opinion: [mainstream belief] is wrong.
Here's why:"

## Template 4: The Personal Story
"[Time ago], I [relatable struggle].
Today, I [transformation].
What changed:"

## Template 5: The Data Lead
"I analyzed [specific number] [things].
The result surprised me:"

## Template 6: The How-To Promise
"How to [desirable outcome] in [specific time frame]
(without [common obstacle]):"

## Template 7: The Value Equation (Hormozi)
"[High dream outcome] + [High perceived likelihood]
+ [Low time delay] + [Low effort/sacrifice]"

Thread Structure (The 1/3/1 Pattern)

Tweet 1: HOOK
  → One punchy line that creates a curiosity gap
  → Never reveal the answer in the hook

Tweet 2-N: BODY (each tweet follows 1/3/1)
  [1 line setup]
  [3 lines of substance/evidence]
  [1 line transition to next tweet]

Final Tweet: CTA
  Options:
  - "Follow me for more on [topic]"
  - "RT the first tweet if this was useful"
  - "I write about this in my newsletter: [link]"
  ⚠️ Put newsletter/external link ONLY in the last tweet

Content OS Template (Justin Welsh's System)

## Weekly Content Schedule
Monday:    Analytical post (data/research)
Tuesday:   Actionable post (how-to)
Wednesday: Aspirational post (story/transformation)
Thursday:  Engagement/reply day (no original post)
Friday:    Thread (on topic validated by Mon-Wed posts)
Weekend:   Community building, DMs, newsletter

## Topic Pillars (pick 2-3)
Pillar 1: [Your professional expertise]
Pillar 2: [Your contrarian perspective]
Pillar 3: [Your personal story/journey]

## Weekly Review Metrics
- Top post by impressions: [__]
- Top post by engagement rate: [__]
- New followers this week: [__]
- Email subscribers added: [__]
- What to double down on: [__]

AI/Tech Niche Specific Tactics

## Timing Windows for AI Content
- New model releases: Respond within 0–60 minutes
- Major AI news: Within 2–4 hours (before saturation)
- Weekend builds: "Ship something Sunday" posts perform well
- Best posting windows: 9–11am weekdays (your audience's timezone)

## High-ER Content Types for AI Niche
1. Build-in-public updates with specific metrics
2. Contrarian takes on hyped tools (with evidence)
3. Before/after comparisons (workflow transformation)
4. "I gave AI a hard problem" with honest results
5. Tool teardowns (not just "here's a cool tool")

## Avoid in AI Niche
❌ "AI is going to change everything" (too vague)
❌ Resharing press releases without original take
❌ Engagement bait ("Drop a 🔥 if you agree")
❌ Posting the same benchmark every tool already shares

Anti-Patterns Reference

## The 6 Common Failure Modes

1. TOPIC SCATTER — Posting about 10 different topics, never building authority
   Fix: Pick 2–3 pillars, stick for 90 days minimum

2. LINK ADDICTION — Putting URLs in every post
   Fix: All links go in replies or last thread tweet only

3. VANITY POSTING — Writing for yourself, not your reader
   Fix: Every post answers "what does my reader get from this?"

4. ENGAGEMENT BAIT — "Like if you agree!" "RT for more!"
   Fix: Algorithm detects this; earn engagement through value

5. PREMATURE MONETIZATION — Selling before building trust
   Fix: Welsh rule: 1,000 true fans before any paid offer

6. INCONSISTENCY — Posting 10x one week, zero the next
   Fix: Reduce quality bar temporarily to maintain consistency
      ("minimum viable post" > no post)

Troubleshooting

Issue: Posts getting zero impressions

Diagnosis: TweepCred likely below distribution threshold (-128 baseline)
Fix sequence:
1. Subscribe to Premium (+100 TweepCred instantly)
2. Remove all external links from post bodies
3. Increase reply activity on large accounts (150x weight)
4. Check if account has any policy flags (check X settings)

Issue: Good impressions but no follower growth

Diagnosis: Content-to-profile mismatch or weak profile
Fix sequence:
1. Audit profile: bio must state WHO you help + HOW
2. Pin your best-performing thread to profile
3. Every viral post should funnel to a clear follow reason
4. Add "I write about [X] every [cadence]" to bio

Issue: Followers not converting to email subscribers

Diagnosis: No consistent CTA or newsletter value prop unclear
Fix sequence:
1. Add newsletter link to bio (not just Linktree)
2. End every thread with a specific newsletter CTA
3. Give away a "lead magnet" (free guide, template, checklist)
4. Post one "newsletter exclusive content preview" per week

Issue: Account diagnostics tool can't auto-collect data

# Fallback to manual data provision:
Provide any of the following:
- Screenshot of your X Analytics dashboard
- CSV export from X Data (Settings → Your Account → Download archive)
- Manual paste of your last 20 tweets with engagement numbers

Minimum viable data for diagnosis:
- Last 30 days impressions
- Top 5 posts by engagement
- Follower count + growth rate
- Most common posting times

File Structure (Post-Installation)

your-project/
├── SKILL.md                          # Main routing file (249 lines)
├── references/
│   ├── writing-workshop.md           # Short tweets/hooks/threads/topics
│   ├── algorithm-niche.md            # X algorithm + AI niche tactics
│   ├── growth-monetization.md        # Growth engines + monetization
│   ├── quality-analytics.md          # Quality checklist + diagnostics
│   └── mental-models-heuristics.md   # 6 models + 10 heuristics
├── research/
│   ├── 01-writing-methods.md         # Nicolas Cole / Dickie Bush methodology
│   ├── 02-growth-engines.md          # Sahil Bloom / Justin Welsh systems
│   ├── 03-content-brand.md           # Dan Koe / Alex Hormozi frameworks
│   ├── 04-platform-mechanics.md      # X algorithm / TweepCred analysis
│   ├── 05-ai-tech-niche.md           # AI niche / Build in Public / China devs
│   └── 06-cases-antipatterns.md      # Case studies + failure patterns
└── user-data/
    └── {username}/
        ├── profile.md
        ├── tweets_{date}.json
        ├── tweets_{date}.md
        ├── report_{date}.html
        └── strategy.md

Quick Reference Card

WRITE TWEET    → 3 hooks + formula labels + publish time + link warning
REVIEW HOOK    → score/10 + 3-point diagnosis + rewrite
TOPIC IDEAS    → 4A matrix + lean validation flywheel
GROWTH STUCK   → TweepCred diagnosis + weekly action plan
ACCOUNT AUDIT  → auto-collect → HTML report → personalized strategy

ALGORITHM WEIGHTS:  Reply conversation=150x, Reply=27x, RT=2x, Like=1x
LINK PENALTY:       -30–50% reach (put in replies only)
PREMIUM VALUE:      +100 TweepCred (bridges most of the -145 deficit)
BEST POST TIME:     Weekdays 9–11am
HOOK TIME BUDGET:   50% of total writing time

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.54%
按下载量换算885

Claude

28.63%
按下载量换算734

Cursor

19.95%
按下载量换算511

Gemini CLI

10.1%
按下载量换算259

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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