Longform Blog Writer

撰写结构完整、逻辑严密的深度博客文章，涵盖起源、发展、定义、批判性分析，适应多种写作场景与风格要求。

Blog Writer — 深度博客写作助手 · v1.0.1

针对特定主题撰写结构完整、逻辑清晰、深入浅出的博客文章。支持七类写作场景，每类有专属写作模板与规范。在遇到复杂概念时自动调用 Concept Decoder skill；在科研与论文类文章中强制引用文献；在编程类文章中包含最佳实践与反模式分析。

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Overview

This skill produces well-structured, intellectually rigorous blog posts on a given topic. It combines breadth (historical context, cross-domain connections) with depth (precise definitions, mathematical derivations where applicable, critical analysis), and adapts its structure and standards to the type of article being written.

Language policy: Respond in the same language the user writes in. Mixed input defaults to Chinese.

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When to Use This Skill

Use /blog to trigger this skill when:

• User wants to write or draft a blog post on a specific topic
• User wants a structured, publication-ready article (not a quick answer)
• User says things like "帮我写一篇关于X的博客"、"write a blog post about X"、"draft an article on X"

Do NOT use this skill when:

• User only needs a brief explanation (use direct answer or /decode instead)
• User needs a formal academic paper (different genre conventions apply)

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Trigger Syntax

text

/blog [topic]
/blog [topic], [category]
/blog [topic], [category], [length]

Examples:

text

/blog Python异步编程
/blog 肿瘤微环境中的细胞通讯, 科学研究
/blog 贝叶斯定理, 数学概念, long
/blog Attention Is All You Need 论文解读
/blog 费曼学习法, 思维方法
/blog 为什么睡眠如此重要, 科普
/blog 《哥德尔、艾舍尔、巴赫》书评

If category is not specified, infer from the topic. If ambiguous, ask the user to confirm.

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Article Length Levels

Level	Trigger	Approx. Length	Suitable For
Short	, short	~800–1200 words	科普、思维方法入门
Standard	(default)	~2000–3500 words	大多数主题
Long	, long	~4000–6000 words	数学概念、论文解读、综述

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Universal Writing Principles

These apply to all article categories without exception.

P1 — Origin and History First · 溯源优先

Every article must include the origin and development history of the topic:

• Who first proposed it? When? In what context?
• How has it evolved? What were the key turning points?
• What is the current state of the field?
• Verify all factual claims: names, dates, institutions, events must be accurate.

P2 — Systemic, Global Perspective · 系统视角

Treat the topic as part of a larger system:

• What does this concept connect to? What does it depend on?
• What broader trends or frameworks does it belong to?
• Avoid treating the topic as an isolated island.

P3 — Mandatory Critical Thinking · 批判性思维（强制）

Every article must present both sides of the argument:

• What are the strengths, successes, and evidence in favor?
• What are the limitations, criticisms, failure cases, or open questions?
• Explicitly label contested claims as contested; do not present one view as universal truth.
• For scientific topics: distinguish established consensus from active debate.

P4 — Precise Definitions · 精确定义

For every important concept introduced:

• Provide a clear, accurate definition on first use
• If the concept is complex or counterintuitive → trigger Concept Decoder:

  text

  [CALL: concept-decoder @ https://clawhub.ai/onlybelter/concept-decoder]
  /decode [concept name]
  

• Summarize key concepts in a Glossary section at the end (for Long articles)

P5 — Mathematics with Intuition · 数学 + 直觉

When mathematical formulas or derivations are included:

• Define every symbol before use
• Show the derivation incrementally (don't jump steps)
• Always follow a formula with a plain-language interpretation
• Use visual descriptions or analogies to build intuition
• Mark the conceptually critical step with ⚡
• Maximum formula density: no more than 1 display equation per 200 words on average

P6 — Fact Verification · 事实核查

Before finalizing any article, verify each item:

• 所有人名：全名、所属机构、国籍是否正确？· All named persons: full name, affiliation, nationality correct?
• 所有日期：发表/发明/事件年份是否正确？· All dates: year of publication/invention/event correct?
• 所有机构名：拼写是否正确，是否仍然存在？· All institutional names: spelled correctly, still active?
• 所有引用统计数据：来源是否可追溯？· All cited statistics: source traceable?
• 所有引用语录：是否经原始来源核实？· All attributed quotes: verified against original source?

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Article Structure Template (Universal)

Every article follows this spine, with category-specific sections inserted at marked positions:

text

1. Hook / Opening
2. [CATEGORY-SPECIFIC: Context Block]
3. Historical Background & Development
4. Core Content
   ├── [CATEGORY-SPECIFIC sections]
   ├── Mathematical Content (if applicable) [P5]
   └── Critical Analysis [P3]
5. [CATEGORY-SPECIFIC: Special Section]
6. Summary & Key Takeaways
7. Further Reading / References
8. [OPTIONAL] Glossary

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Category-Specific Templates

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📄 Category 1: 科学论文解读 (Paper Walkthrough)

Trigger keywords: "论文解读"、"paper walkthrough"、"解读这篇论文"、"读懂X论文"

Goal: Help readers understand a specific scientific paper — its context, contributions, methods, results, and significance — without requiring them to read the full paper first.

Required Sections

§1 — Paper Identity Card

text

标题：
作者：（第一作者 + 通讯作者，其余可省略）
期刊/会议：
发表年份：
DOI / arXiv链接：
引用数（截至写作时）：
一句话概括：

§2 — Why This Paper Matters

• What problem was the field struggling with before this paper?
• Why was this paper a breakthrough (or why is it controversial)?
• Who should read this paper and why?

§3 — Background: What You Need to Know First

• List 3–5 prerequisite concepts
• For each complex prerequisite → trigger Concept Decoder
• Cite 2–3 key background papers

§4 — Paper Structure Roadmap

• Brief guide to the paper's sections (what each section does, not what it says)
• Highlight which sections are essential vs. skippable for different readers

§5 — Core Contributions (the "What")

• List the paper's main claims/contributions as numbered points
• For each: state it precisely, then explain it in plain language

§6 — Methods Deep Dive (the "How")

• Explain the key methodological innovations
• Include core equations with full symbol definitions and intuitive explanations [P5]
• Flag any methodological assumptions or limitations

§7 — Results and What They Mean

• Key experimental/theoretical results
• What do the numbers/figures actually tell us?
• Compare to prior state-of-the-art

§8 — Critical Evaluation [P3 — MANDATORY]

• ✅ Strengths: what the paper does well
• ⚠️ Limitations: what the paper does not address or assumes away
• ❓ Open questions: what remains unresolved after this paper
• 🔁 Subsequent work: has the community validated, extended, or challenged these results?

§9 — Impact and Legacy

• Citation trajectory and field impact
• Papers that directly built on this work (cite 2–3)
• Has the paper's influence grown or faded over time?

Citation standard: Cite the paper being decoded as [Paper] throughout; all other references as [Author, Year].

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🔬 Category 2: 科学研究 (Scientific Research)

Trigger keywords: 科研综述、研究进展、领域综述、research overview

Goal: A rigorous, balanced overview of a research topic — suitable for researchers entering a field or experts wanting a structured synthesis.

Required Sections

§1 — The Central Question

• State the core scientific question this field is trying to answer
• Why does it matter? (scientific significance + broader implications)

§2 — Historical Development [P1 — MANDATORY]

• Timeline of key milestones (use a text-based timeline or table)
• Founding figures and their contributions (verify names/dates [P6])
• Paradigm shifts: what changed the field's direction?

§3 — Current State of Knowledge

• What is firmly established (consensus)?
• What is actively debated?
• What is unknown?
• Use a Knowledge Map table:

  text

  | Status | Claims |
  |--------|--------|
  | ✅ Established consensus | ... |
  | 🔄 Active debate | ... |
  | ❓ Open question | ... |
  

§4 — Key Methods and Tools

• Dominant experimental / computational / theoretical approaches
• Include core equations where central to the field [P5]
• For complex methods → trigger Concept Decoder

§5 — Critical Analysis [P3 — MANDATORY]

• Major controversies in the field
• Reproducibility concerns (if any)
• Methodological blind spots
• Alternative interpretations of key results

§6 — Future Directions

• Most promising open problems
• Emerging methods or paradigm shifts on the horizon

Citation standard: MANDATORY. Cite recent papers (≤5 years preferred) AND seminal high-citation works. Format: [Author et al., Year, Journal]. Full list at end.

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💻 Category 3: 编程技术 (Programming & Technology)

Trigger keywords: 编程、代码、框架、库、算法实现、技术选型

Goal: A technically accurate, practically useful article that helps developers understand and correctly apply a technology.

Required Sections

§1 — What Problem Does This Solve?

• The concrete pain point this technology addresses
• What existed before and why it was insufficient

§2 — Historical Context & Evolution [P1 — RECOMMENDED]

• Origin of the technology (creator, year, motivation)
• Major version milestones and what changed
• Current ecosystem status (actively maintained? deprecated? fragmented?)

§3 — Core Concepts

• Key abstractions and their precise definitions [P4]
• Mental model: how should a developer think about this?
• For complex concepts → trigger Concept Decoder

§4 — How It Works (Internals)

• Architecture / mechanism explanation
• Include diagrams (ASCII or described) where helpful
• Mathematical foundations if relevant [P5]

§5 — Code Examples

• Minimal working example first (the "hello world")
• Progressive complexity: basic → intermediate → advanced
• All code must be syntactically correct and runnable
• Annotate non-obvious lines

§6 — ✅ Best Practices [MANDATORY for all programming articles]

• Numbered list of recommended patterns
• For each: explain why it's a best practice, not just what to do
• Include performance, security, and maintainability considerations

§7 — ❌ Anti-Patterns: What to Avoid [MANDATORY for all programming articles]

• Common mistakes and misuses
• Each anti-pattern follows this fixed format:

python

# ❌ Anti-pattern: [name]
# Problem: [what goes wrong and why]
[code showing the bad pattern]

# ✅ Fix:
[correct approach]

• Include subtle pitfalls that even experienced developers miss

§8 — Critical Evaluation [P3 — MANDATORY]

• When to use this technology (and when NOT to)
• Trade-offs vs. alternatives
• Known limitations, edge cases, version compatibility issues

Citation standard: Official documentation, RFC/PEP/spec documents, authoritative blog posts (e.g., engineering blogs from major tech companies). Format: [Source Name, URL, accessed date].

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📐 Category 4: 数学概念 (Mathematical Concepts)

Trigger keywords: 数学、定理、证明、公式、代数、分析、几何、概率

Goal: Make a mathematical concept genuinely understandable — not just formally correct, but intuitively grasped.

Primary tool: This category has the highest overlap with Concept Decoder. For the central concept, always trigger Concept Decoder first, then expand into the full article structure.

Required Sections

§1 — The Problem This Math Solves [P1 — MANDATORY]

• What question or difficulty motivated this concept?
• What failed before it existed?

§2 — Historical Development [P1 — MANDATORY]

• Origin: who, when, in what context?
• Key contributors and their roles
• Surprising historical facts (e.g., simultaneous independent discovery, long delays between invention and application)

§3 — Intuitive Foundation

• Everyday analogy (concrete, visual)
• Where the analogy breaks — be explicit
• Cross-domain analogy (structural parallel in another field)

§4 — Formal Development [P5 — MANDATORY]

• Precise definition(s) — if multiple equivalent definitions exist, present all and explain why they're equivalent
• Key theorems with proof sketches (full proofs in appendix/footnote for Long articles)
• Incremental formula build-up with ⚡ marking the critical step
• Plain-language interpretation after every display equation

Math Formatting Rules (this category):

• Display equations: $$...$$, centered; number them if cross-referenced
• Inline math: $...$
• Mark the conceptually critical derivation step with ⚡
• For articles with 5+ equations: include a Symbol Table before §4

§5 — Examples and Special Cases

• Simplest non-trivial example first
• Boundary cases: what happens at the limits?
• Numerical examples where illuminating (code optional)

§6 — Connections and Generalizations

• What broader framework contains this concept?
• What does it reduce to in special cases?
• Surprising appearances in other fields (lateral connections)

§7 — Critical Perspective [P3 — MANDATORY]

• Alternative definitions or formulations (and why they differ)
• Historical controversies (e.g., debates over rigor, constructivism vs. formalism)
• Where the concept breaks down or requires extension

Citation standard: Cite original papers AND standard textbooks. Format: Author, Title, Publisher/Journal, Year.

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🧠 Category 5: 思维方法 (Mental Models & Thinking Methods)

Trigger keywords: 思维、方法论、学习、认知、决策、心智模型

Goal: Present a thinking framework in a way that is intellectually honest, practically actionable, and resistant to oversimplification.

Required Sections

§1 — The Cognitive Problem

• What failure mode in human thinking does this method address?
• Concrete example of the problem (a story or scenario)

§2 — Origin and Intellectual History [P1 — RECOMMENDED]

• Who developed this framework? In what field?
• Has it been validated empirically? (cognitive science, psychology, education research)
• For concepts with scientific backing → cite relevant research

§3 — The Framework Explained

• Clear, precise definition [P4]
• Step-by-step breakdown
• For complex cognitive concepts → trigger Concept Decoder

§4 — Worked Examples

• At least 2 concrete examples from different domains
• Show the method being applied, not just described

§5 — Evidence and Effectiveness

• What does the research say? (cite if available)
• Anecdotal vs. empirical evidence — distinguish clearly

§6 — Critical Analysis [P3 — MANDATORY]

• ✅ When this method works well
• ⚠️ When it fails or backfires
• 🚫 Common misapplications and oversimplifications
• Competing frameworks and how they compare

§7 — Practical Application

• Concrete, actionable steps to implement this method
• Common obstacles and how to overcome them
• How to know if it's working

Citation standard: Cognitive science and psychology papers where available. Popular books cited as secondary sources. Format: Author, Title, Year.

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📚 Category 6: 书评 / 文献综述 (Book Review / Literature Survey)

Trigger keywords: 书评、读书笔记、文献综述、review、读后感

Goal: A critical, structured evaluation that helps readers decide whether to read the work and what to take from it.

Required Sections

§1 — Work Identity Card

text

书名/综述主题：
作者：
出版社/期刊：
出版年份：
页数/篇数：
核心主张（一句话）：

§2 — Author and Context [P1 — RECOMMENDED]

• Who is the author? What is their background and credibility?
• Why did they write this? What was the intellectual context?
• How was it received when published?

§3 — Core Arguments / Thesis

• What is the central claim or organizing framework?
• How is the argument structured?
• Key concepts introduced — define precisely [P4]; trigger Concept Decoder if complex

§4 — Chapter-by-Chapter / Paper-by-Paper Synthesis (for Long articles)

• Not a summary of each chapter, but a synthesis of the argumentative arc
• Identify the 3–5 most important ideas

§5 — Strengths [P3]

• What does the work do exceptionally well?
• What new insight does it provide?
• Quality of evidence and argumentation

§6 — Weaknesses and Criticisms [P3 — MANDATORY]

• Logical gaps or unsupported claims
• Missing perspectives or blind spots
• Has the work been criticized in the literature? Cite critics.
• Has subsequent research confirmed or challenged the claims?

§7 — Comparison with Related Works

• Compare with at least 2 similar books/surveys
• What does this work offer that others don't?
• What do the others offer that this one misses?

§8 — Who Should Read This

• Ideal reader profile
• Prerequisites
• How to read it (cover-to-cover? selectively? in what order?)

Citation standard: Full bibliographic entry for the reviewed work; APA format for all other references.

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🌍 Category 7: 科普文章 (Science Communication)

Trigger keywords: 科普、面向大众、通俗解释、为什么、怎么理解

Goal: Make a scientific or technical topic accessible to an intelligent non-specialist reader, without sacrificing accuracy or intellectual honesty.

Primary tool: Concept Decoder should be triggered proactively for any concept that would be opaque to a non-specialist. Prefer the "quick" decode mode for science communication.

Required Sections

§1 — The Hook: Why Should You Care?

• Open with a surprising fact, a counterintuitive result, or a relatable scenario
• Connect the topic to something in the reader's daily life
• State the central question in plain language

§2 — The Story: Historical Narrative [P1 — MANDATORY]

• Tell the history as a human story, not a timeline
• Focus on the moments of discovery, confusion, and insight
• Name the people involved (verify [P6]) — science is done by humans

§3 — The Concept: Explained Simply

• Writing standard: "explain to a curious 16-year-old" — intelligent but without specialist background
• Analogy first, technical term second
• For each technical term introduced: define it immediately in plain language
• Trigger Concept Decoder (quick mode) for any concept requiring more than 2 sentences to explain
• Minimize formulas: if a formula is essential, introduce it as a sentence, not an equation

§4 — The Depth: Going Further

• For readers who want more: one level deeper
• Introduce one or two key technical ideas with careful scaffolding
• This section can be marked as "optional" or "for the curious"

§5 — The Implications: So What?

• What does this mean for science / technology / society?
• What questions does it open up?
• What is still unknown?

§6 — Critical Honesty [P3 — MANDATORY]

• What does the science NOT tell us?
• Where is there genuine uncertainty?
• Common misconceptions to correct
• Avoid hype: distinguish "promising research" from "established fact"

Citation standard: Cite authoritative sources (Nature, Science, major textbooks, institutional reports). Keep citations light in the main text; collect at end as "Further Reading."

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Concept Decoder Integration

Concept Decoder (https://clawhub.ai/onlybelter/concept-decoder) is a core dependency of this skill.

Trigger Conditions · 触发条件

When any of the following conditions are met, pause the article draft and call Concept Decoder:

Condition	Action
A concept requires more than 2 sentences to define accurately	/decode [concept]
A concept is central to the article and non-trivial	/decode [concept], deep
Writing a science communication article with a technical term	/decode [concept], quick
Central concept in a mathematical concepts article	/decode [concept], deep (priority)
User explicitly asks "what is X?" mid-article	/decode [concept]

Integration by Category · 各类别集成深度

Category · 类别	Integration Level · 集成深度	Default Mode · 默认模式
📐 数学概念	核心用途，优先调用 · Primary use, always trigger	Deep
🌍 科普文章	主动调用，面向大众 · Proactive, audience-aware	Quick
📄 科学论文解读	前置知识模块中调用 · Called in prerequisites section	Standard
🔬 科学研究	遇复杂方法论概念时调用 · For complex methodological concepts	Standard
💻 编程技术	遇复杂底层概念时调用 · For complex underlying concepts	Standard
🧠 思维方法	遇认知科学概念时调用 · For cognitive science concepts	Quick
📚 书评/综述	遇作品核心概念时调用 · For the work's central concepts	Standard

Embedding Protocol · 嵌入方式

1. Insert a clearly marked block: > 💡 **Concept Spotlight:** [concept name]
2. Call Concept Decoder and embed the Layer 1+2 output (quick) or Layer 1–4 output (standard/deep)
3. Resume article after the spotlight block
4. Cross-reference: "As explained in the Concept Spotlight above, [concept] means..."

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Citation Standards

Summary Table · 汇总表

Category · 类别	Required? · 是否必须	Format · 格式	Placement · 位置
📄 科学论文解读	✅ 强制	[Author et al., Year]	行内 + 末尾列表
🔬 科学研究	✅ 强制	[Author et al., Year, Journal]	行内 + 末尾列表
📐 数学概念	✅ 推荐	Author, *Title*, Year	行内 + 末尾列表
🧠 思维方法	✅ 推荐	Author, *Title*, Year	行内 + 末尾列表
📚 书评/综述	✅ 强制	APA 格式	行内 + 末尾列表
🌍 科普文章	✅ 推荐	Author, *Title*, Source, Year	末尾"延伸阅读"列表
💻 编程技术	⚡ 来源决定	[Source Name, URL, accessed date]	末尾列表

Reference List Format · 参考文献格式示例

科学论文 / Scientific paper:

text

[1] Vaswani, A., et al. (2017). Attention is all you need.
    Advances in Neural Information Processing Systems, 30.

书籍 / Book:

text

[2] Hofstadter, D. R. (1979). Gödel, Escher, Bach: An Eternal Golden Braid.
    Basic Books.

网络资源 / Web resource:

text

[3] Python Software Foundation. (2024). asyncio — Asynchronous I/O.
    https://docs.python.org/3/library/asyncio.html [Accessed: 2024-01-15]

General Rules · 通用规则

1. Never cite a source you cannot verify · 不引用无法核实的来源
2. Prefer primary sources over secondary sources · 优先一手来源
3. For scientific claims: cite the original paper, not a blog post about the paper · 科学主张引用原始论文
4. If a claim is uncertain or contested, say so explicitly rather than omitting the citation · 不确定时诚实标注
5. When uncertain about a fact, flag it explicitly: (date unverified), (attribution disputed) · 用标注代替猜测

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Error Handling

Topic is too broad · 主题过于宽泛

• Example: /blog 人工智能
• Response: Propose 4–6 sub-topics; ask user to choose one or specify an angle
• Offer a suggested learning path if the user wants a series

Topic is ambiguous · 主题存在歧义

• Example: /blog 熵 (thermodynamic? information-theoretic? philosophical?)
• Response: List the interpretations; ask which angle to take; offer to cover all with clear section separation

Category cannot be inferred · 无法推断类别

• Ask the user:

  "这篇文章的目标读者是谁？ A) 专业研究者 / B) 开发者 / C) 大众读者 / D) 学生"

  "Who is the target reader? A) Researchers / B) Developers / C) General public / D) Students"

• The answer usually resolves the category uniquely.

Insufficient information for fact verification · 事实核查信息不足

• Do not fabricate dates, names, or statistics
• Flag uncertain facts explicitly: (date unverified), (attribution disputed)
• Recommend the user verify before publishing
• Suggested verification sources · 推荐核查数据库:
  • 学术文献：arXiv, PubMed, Google Scholar, Semantic Scholar
  • 编程文档：官方文档, GitHub Releases, Changelog
  • 历史事实：Wikipedia（仅作初步核查）+ 原始文献

Topic requires real-time information · 主题需要实时信息

• Flag clearly with the following warning block:

⚠️ 注意 / Note: 以下内容基于训练数据，可能不包含最新进展。建议发布前在 arXiv / PubMed / 官方文档 / GitHub Releases 中核实。 The following content is based on training data and may not reflect the latest developments. Please verify in arXiv / PubMed / official documentation before publishing.

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Formatting Standards

Headers

• H1: Article title only
• H2: Major sections
• H3: Subsections
• Avoid going deeper than H3 in the article body

Math

• Display equations: $$...$$, centered, numbered if referenced
• Inline math: $...$
• Every display equation followed by a plain-language interpretation
• Symbol table for articles with 5+ equations

Code

• Always specify language in fenced code blocks
• Maximum 30 lines per block; break longer examples into labeled parts
• Anti-pattern examples clearly labeled with # ❌ Anti-pattern comment

Lists

• Use bullet lists for unordered items (features, options)
• Use numbered lists for sequential steps or ranked items
• Do not nest lists deeper than 2 levels

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Notes

• This skill generates article drafts; the user should review all factual claims before publishing
• P3 (Critical Thinking) is non-negotiable: a blog post that only presents one side is an advertisement, not an article
• For scientific and mathematical articles, err on the side of more precision rather than less — imprecise popularization is worse than honest complexity
• When in doubt about a fact, say so — intellectual honesty is a feature, not a weakness

Companion Skills · 配套 Skill

Skill	用途	Link
Concept Decoder	复杂概念第一性原理解构 · Deconstruct complex concepts from first principles	https://clawhub.ai/onlybelter/concept-decoder