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name: blog-style description: 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. argument-hint: "learn <paths>" user-invokable: true license: MIT

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.