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blog-style

Learn author writing style from 5 to 10 existing blog posts and generate a voice profile for /blog style learn, VOICE.md, blog-persona, and blog-write when users ask to infer tone, analyze author voice, learn style, or build a writing baseline.

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AgriciDaniel/claude-blog

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包含的文件（1）

SKILL.md3.0 KB

SKILL.md（原文）

安装前可以查看交给 AI 代理的指令内容。

Blog Style - Writing Style Learning

Learn an author voice profile from existing posts, then use it as a baseline for VOICE.md, blog-persona, and blog-write. The profile captures measurable style signals so future drafts can preserve the author's cadence, vocabulary, and tone.

Commands

Command	Purpose
`/blog style learn <paths>`	Analyze sample posts and generate a voice profile

Learn Workflow

Use 5 to 10 representative posts from the same author, brand, or editorial voice. Accept individual markdown files, MDX files, text files, or a directory containing posts.

Run the local learner:

python3 scripts/style_learn.py <paths> --format markdown

For machine-readable output:

python3 scripts/style_learn.py <paths> --format json --output voice-profile.json

For a VOICE.md-ready block:

python3 scripts/style_learn.py <paths> --format markdown --output VOICE.md

If fewer than the requested minimum sample count is supplied, warn and continue. The default minimum is 5 posts.

Profile Fields

The learner aggregates the existing blog analyzer across each sample post:

Sentence length mean and median
Sentence length burstiness as corpus variance
Vocabulary richness as type-token ratio
Transition-word sentence rate
Passive-voice sentence rate
AI trigger words per 1,000 words as a baseline to preserve or avoid
Paragraph-length distribution
First-person usage rate
Heading-as-question ratio
Signature phrases from top 2-gram and 3-gram content phrases with stopwords removed
Tone descriptors derived from the measured metrics

Consuming the Profile

Drop the markdown block into project VOICE.md when the goal is durable project context. Blog-write can use the style baselines as drafting targets:

Keep average sentence length near the learned mean.
Match the learned sentence variation unless the user asks for a tighter or looser cadence.
Preserve signature phrases only when they fit the topic naturally.
Treat the AI trigger baseline as a ceiling when the author rarely uses those terms.
Use the first-person and heading-question rates to decide how personal and question-led the draft should feel.

Feed the JSON output into blog-persona when a structured persona should be created or updated. Map the learned values to persona sentence length, passive voice, readability, vocabulary, and tone settings.

Error Handling

Too few posts: Continue and warn that the profile may be less stable.
Missing paths: Skip missing paths and include a warning in the profile.
Unsupported files: Skip unsupported file types and include a warning.
Empty samples: Return zeroed metrics rather than crashing.

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中文简介正在准备中。当前显示原文说明。