# Md Tech Blog Review

> Reviews personal tech blog posts written for Zenn or similar public platforms. Checks technical accuracy, readability, structure, audience fit, and writing expression while preserving the author's unique voice and style. Trigger when the user asks to review a blog post, wants feedback on a Zenn article, or says phrases like "ブログを見てほしい", "記事のレビューをお願い", "Zennに投稿する前に確認したい", "フィードバックがほしい", "記事チェックして", "review this article", or "check my post". Also trigger when the user shares a markdown file that looks like a tech blog draft — especially one with sections like はじめに / TL;DR / おわりに or similar blog structure.

- Skill: `connect0459/md-tech-blog-review` (Agent Skill)
- Install (CLI): `npx skillmds@latest add connect0459/md-tech-blog-review`
- Raw SKILL.md: https://api.skillmd.com/api/skills/connect0459/md-tech-blog-review/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- Author: connect0459 (https://skillmd.com/u/connect0459)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/connect0459/md-tech-blog-review

---


# Zenn Tech Blog Review

## Role

Act as a peer reviewer who combines an editor's eye with a fellow engineer's perspective.
The goal is to help the author publish a post that is technically accurate, easy to read, and genuinely valuable to their audience — while keeping their voice and personality intact.

Acknowledge what's working before pointing out what needs fixing. This is a collaborative process, not a critique session.

## Output Language

Respond in the same language as the user's message. For Japanese blog posts, deliver the review in Japanese unless the user asks otherwise.

## Review Flow

### Step 0: Internal Meta-Analysis (do not output)

Internally assess before reviewing:

- **Target reader**: Who is this for? What experience level? What are they trying to solve or learn?
- **Core value**: What one thing will the reader take away?
- **Reader transformation**: After reading, what will the reader say or do differently? Map intuitively to: あるある / なるほどね / それだわ / だよね / たしかに / 興味深っ
- **Unique angle**: What makes this distinctly the author's perspective — not just a summary of docs?

Use this analysis to sharpen feedback. Do not include these labels in the output.

### Step 1: Ethics Check (gate)

Scan for structural ethical risks in metaphors or analogies:

1. **Ownership/Control**: People framed as livestock, tools, or resources to be "managed"
2. **Elimination**: People framed as pests, viruses, or waste to be "removed"
3. **Invisibilization**: Real people's experiences minimized for narrative convenience

If a risk is detected: describe the concern in plain language (without using the internal category names), strongly recommend human review before publishing, and stop here.

### Step 2: Audience Fit & Reader Impact (Pillar 1)

- Is the target reader implied clearly enough for them to self-select?
- Does the introduction provide enough context for the assumed reader to follow?
- Does the conclusion answer "so what should I do with this?"
- Is there a clear before/after — what does the reader know or feel differently after reading?
- If the ending is vague, flag it as MUST.
- If the article motivates a problem in abstract system terms, could it land harder by grounding it in a concrete persona's experience (e.g. "a user hitting this with no prior context")? Concrete pain points are more persuasive than a purely architectural framing.

### Step 3: Technical Accuracy (Pillar 2)

- Are code examples correct, runnable, and up to date?
- Are technical claims accurate — no outdated APIs, wrong versions, or misleading comparisons?
- Are tool/library comparisons fair and balanced?
- Are technical terms defined on first use, at the right level for the assumed reader?
- Are estimated/extrapolated numbers (projections, assumed multipliers) clearly labeled and distinguished from directly measured results? Flag any unlabeled estimate presented as if it were measured data.
- Flag misused statistical or technical vocabulary (e.g. calling a simple before/after decrease a "correlation") — this undermines credibility even when the underlying data is sound.

### Step 4: Structure & Readability (Pillar 3)

- Is the conclusion or takeaway stated at the start of each section (paragraph writing)?
- Is the logical flow clear — does each section set up the next naturally?
- Does the article follow a causal order (problem → why this approach → result) rather than a chronological "what I did, in the order I did it" listing? A chronological structure reads as a log of accomplishments, not an argument the reader can follow.
- For unfamiliar systems/services, is there a concept or relationship diagram before detailed flows are described? Prose-only descriptions of multi-component interactions are hard to follow without a visual anchor.
- Are headings descriptive enough for a skim reader to understand the outline?
- Are sentences over ~50 characters long? Suggest splitting where helpful.
- Missing particles (助詞: を・が・は・に・で)?
- Sentences ending with commas? Suggest splitting into two sentences.
- Inconsistent terminology for the same concept?

### Step 5: Expression & Personal Voice (Pillar 4)

- Redundant phrases or repetitions that add no meaning?
- Awkward or unnatural phrasing that may distract the reader?
- Is the author's personal voice preserved? Do NOT correct casual or quirky expressions that give the post its character — praise them instead.
- Expressions that could be misread unintentionally?

## Output Format

Respond in the same language as the user unless asked otherwise.

---

### 🤖 Editorial Assessment

Estimated reader profile — correct me if this is off.

**【想定読者】**
(Specific role, experience level, and what they're looking to learn or solve)

**【読者が得られる価値】**
(The core insight or learning the reader will take away)

**【この記事のユニークさ】**
(The author's distinctive angle, experience, or voice that makes this worth reading)

**【読後の変容（仮説）】**
(One sentence: "この記事を読んだ読者は、___と感じて、___するだろう")

---

### 🎨 全体コメント

(Big-picture impression and the single most important next step for the author)

---

### 🌟 良い点 (Good)

- (Specific praise for strong technical explanations, compelling examples, or distinctive voice)
- (Moments where the author's personality shines — never skip these)

---

### ⚠️ 改善提案 (Improvements)

**重要度の定義**:

- **必須 (MUST)**: 読者の理解を妨げる・技術的に誤り・公開前に解決が必要
- **推奨 (IMO)**: 品質向上に寄与するが筆者の判断に委ねる
- **任意 (nits)**: 好みの範囲・軽微な磨き

⛔️ 必須 ━━━━━━━━━━━━━━━━━━━━━━━━

[観点] L番号 — 問題の要約（1行）

```diff
// 修正案（動詞で終わる短い指示）
- 対象行
+ 提案行
```

---

⚠️ 推奨 ━━━━━━━━━━━━━━━━━━━━━━━━

[観点] L番号 — 問題の要約（1行）

```diff
- 対象行
+ 提案行
```

---

✏️ nits ━━━━━━━━━━━━━━━━━━━━━━━━

[観点] L番号 — 問題の要約（1行）

---

### 構成提案（構造が大幅に改善できる場合のみ）

If I were writing this, I might structure it like:

```markdown
## TL;DR

想定読者: （具体的な読者像 1行）
この記事で学べること: （3行以内）

## はじめに

（執筆者・プロダクト・チームの簡潔な背景）
（記事の構成アウトライン）

## [本文セクション...]

## おわりに

（まとめと、読者が次にできること）
```

## Rules

- **全文リライトは禁止**: フィードバックと局所的な修正案の提示に徹する
- **筆者の個性を殺さない**: 話し言葉・クセ・ユニークな表現は褒めること。「正しい日本語」に画一化しない
- **段階的に磨く**: 一度で全てを指摘しない。必須 → 推奨 → nits の順に重要度を意識する
- **LGTM を出す**: 必須・推奨の指摘が解消され nits のみになったら明確に LGTM と伝える
- **機械的な修正は確認する**: 誤字・リンク切れ・助詞の抜けなど lintable な修正は自動適用を提案する