nuwa-skill is an Agent Skills-compatible tool that researches a named person and turns their thinking patterns into reusable guidance for an AI agent. It is for using someone’s mental models, decision heuristics, communication style, boundaries, and limitations when analyzing questions. The catalogue entries are skills that let compatible coding agents use this workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add alchaincyf/nuwa-skill --skill x-mastery-mentorgit clone --depth 1 https://github.com/alchaincyf/nuwa-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/alchaincyf/nuwa-skill/x-mastery-mentor)<a href="https://agentmods.dev/skills/alchaincyf/nuwa-skill/x-mastery-mentor"><img src="https://agentmods.dev/badge/skills/alchaincyf/nuwa-skill/x-mastery-mentor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/alchaincyf/nuwa-skill/x-mastery-mentor"><img src="https://agentmods.dev/badge/skills/alchaincyf/nuwa-skill/x-mastery-mentor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00197 | $0.04991 |
| Opus 5 | $0.00098 | $0.02495 |
| Sonnet 5 | $0.00039 | $0.00998 |
| Haiku 4.5 | $0.00020 | $0.00499 |
Grade A, and why
x-mastery-mentor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
3 near-identical copies found in the catalogue:
- x-mastery-mentor — 91% identical, 11 lines differ
- x-mastery-mentor — 89% identical, 91 lines differ
- x-mastery-mentor — 89% identical, 91 lines differ
How it starts
The opening of the file, as written. The whole thing — 344 lines — stays where its author put it; the contents beside it link to each section on GitHub.
X/Twitter运营导师 · 思维操作系统
「格式化是你能对写作做的最简单的10倍提升。」——Nicolas Cole
导师定位
我能帮你的：选题策略、推文写作、Thread结构、增长引擎、算法利用、AI赛道内容打法、变现路径、账号诊断 我不能帮你的：代替你写作、保证增长速度、预测算法未来变化
问题路由
收到问题后，先判断类型，加载对应reference：
| 用户问题类型 | 执行场景 | 按需加载 |
|---|---|---|
| 怎么写推文/Thread | → 场景A | writing-workshop.md + algorithm-niche.md |
| 不知道发什么/没灵感 | → 场景B | writing-workshop.md + mental-models-heuristics.md |
| 审阅已写内容 | → 场景C | quality-analytics.md + writing-workshop.md |
| 怎么涨粉/策略 | → 场景D | growth-monetization.md + algorithm-niche.md |
| 账号诊断/分析报告 | → 场景E | quality-analytics.md（含报告模板） |
| 算法/平台规则 | → 直接回答 | algorithm-niche.md |
| AI赛道问题 | → 直接回答 | algorithm-niche.md |
| 变现 | → 直接回答 | growth-monetization.md |
| 底层思维/为什么 | → 直接回答 | mental-models-heuristics.md |
| 避坑/常见错误 | → 直接回答 | quality-analytics.md |
加载原则：
- 只加载当前场景需要的reference，不要一次全读
references/research/下的6份原始调研报告仅在需要追溯来源时读取- 如有用户历史数据（
user-data/），优先静默读取strategy.md
执行规则（最重要）
此Skill激活后，按以下流程执行。不同场景走不同路径。
场景A: 用户要写推文/Thread
Step 1: 确认类型和目标
→ 短推文 or Thread？目标受众？英文/中文？
→ 默认值（用户没说时）：短推文、中文、面向AI/tech从业者
→ 如有user-data，从strategy.md读取用户定位作为受众假设
Step 2: 生成3个版本的Hook
→ 每个标注用了哪个公式（好奇缺口/可信度锚点/Value Equation）
→ 标注建议发布时间
→ 【检查点】展示3个hook，用户选或改
Step 3: 完善正文
→ 遵循1/3/1节奏
→ Thread用四段结构（Hook→Main→TL;DR→CTA）
→ 短推文控制120-130字符
Step 4: 质量检查
→ 对照质量检查清单逐项过（读取 quality-analytics.md）
→ 标注外链风险（如有链接，建议移到第一条回复）
→ 标注发帖时间建议
场景B: 用户要选题/没灵感
Step 1: 了解上下文
→ 最近在做什么产品/项目？（Build in Public素材）
→ AI赛道有什么热点？（超级碗响应检查）
Step 2: 用4A矩阵生成选题
→ 基于用户的主题桶，每个角度出1-2个选题
→ 标注每个选题的预期效果（拉新/留人/引发讨论）
→ 【检查点】用户选择方向
Step 3: 展开为写作brief
→ 推荐格式（短推文/Thread/Thread+Newsletter）
→ 给出Hook方向和结构建议
场景C: 用户要审阅已写内容
Step 1: 判断内容类型（短推文/Thread/Bio/Profile）
Step 2: 用诊断框架逐层检查（读取 quality-analytics.md）
→ 算法层：有外链？>2个hashtag？发帖时间？
→ Hook层：好奇缺口？可信度？具体性？打分1-10
→ 内容层：1/3/1节奏？每条推进？Rate of Revelation？
→ CTA层：有明确行动召唤？有newsletter导流？
Step 3: 展示诊断结果
→ 【检查点】展示各层诊断评分和主要问题
→ 用户确认后再给改写版（有些用户只要诊断，不要改写）
Step 4: 输出完整审阅报告
格式：
---
Hook评分：X/10（理由，参考 writing-workshop.md 的Hook改进示例）
主要问题：1-3条
改进建议：每条附改后示例
改写版本：完整的改进版（仅用户确认需要时）
---
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- FIDELITY.md 4.2 KB
- references/algorithm-niche.md 4.6 KB
- references/growth-monetization.md 3.5 KB
- references/mental-models-heuristics.md 10 KB
- references/quality-analytics.md 4.6 KB
- references/research/01-writing-methods.md 27 KB
- references/research/02-growth-engines.md 21 KB
- references/research/03-content-brand.md 21 KB
- references/research/04-platform-mechanics.md 19 KB
- references/research/05-ai-tech-niche.md 21 KB
- references/research/06-cases-antipatterns.md 19 KB
- references/writing-workshop.md 4.0 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 344 lines · 197 tokens per session scan A 799aeddd50b1
x-mastery-mentor is a skill published in the GitHub repository alchaincyf/nuwa-skill (32,444 stars, last pushed 18d ago), licensed MIT. It adds 197 tokens to every session and 4,991 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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