Claude-skill-registry author-profile

Create author profiles via questionnaire or transcript analysis for consistent article voice

install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/author-profile" ~/.claude/skills/majiayu000-claude-skill-registry-author-profile && rm -rf "$T"
manifest: skills/data/author-profile/SKILL.md
source content

Author Profile

Create and maintain consistent author voice across all articles.

Profile Location

Stored in:

.article_writer/authors.json

Schema:

.article_writer/schemas/authors.schema.json

Two Ways to Create an Author

Option 1: Manual Questionnaire

Ask questions in conversational groups (2-3 at a time):

Identity

  1. What name/identifier for this author? (e.g., "mwguerra")
  2. Display name? (e.g., "MW Guerra")
  3. Professional role(s)?
  4. Years/areas of experience?
  5. Expertise areas?

Languages

  1. Primary writing language? (e.g., pt_BR, en_US)
  2. Translation target languages?

Tone (1-10)

  1. Casual (1) vs Formal (10)?
  2. Neutral (1) vs Opinionated (10)?

Vocabulary

  1. Terms readers know (use freely)?
  2. Terms to always explain?

Style

  1. Signature phrases?
  2. Phrases to avoid?

Positions

  1. Strong technology opinions?
  2. Topics to stay neutral on?

Example

  1. Write 2-3 sentences in your voice as example.

Option 2: Extract from Transcripts

Use Skill(voice-extractor) for transcript analysis.

If the author has recordings (podcasts, interviews, videos, meetings):

  1. Prepare transcripts - Get transcription files with speaker labels
  2. Run analysis:
    bun run "${CLAUDE_PLUGIN_ROOT}"/scripts/voice-extractor.ts --speaker "Name" transcripts/*.txt
    
  3. Review extracted data - Communication style, phrases, vocabulary
  4. Add identity info - Name, role, expertise, languages (manual)
  5. Merge - Combine extracted + manual data

Option 3: Combined Approach (Recommended)

Best results come from combining both:

  1. Extract voice patterns from transcripts
  2. Add identity/expertise info manually
  3. Review and refine the merged profile

Author JSON Structure

{
  "id": "author-slug",
  "name": "Display Name",
  "languages": ["pt_BR", "en_US"],
  "role": "Senior Developer",
  "experience": "10+ years",
  "expertise": ["Laravel", "PHP", "Architecture"],
  "tone": {
    "formality": 4,
    "opinionated": 7
  },
  "vocabulary": {
    "use_freely": ["Controllers", "Middleware", "API"],
    "always_explain": ["DDD", "CQRS", "Event Sourcing"]
  },
  "phrases": {
    "signature": ["Na prática...", "Vamos direto ao ponto:"],
    "avoid": ["Simplesmente", "É só fazer..."]
  },
  "opinions": {
    "strong_positions": ["Tests are essential", "Fat models are bad"],
    "stay_neutral": ["Tabs vs spaces", "IDE preferences"]
  },
  "example_voice": "Sample paragraph in author's voice...",
  "voice_analysis": {
    "extracted_from": ["podcast_ep1.txt", "interview.txt"],
    "sample_count": 156,
    "total_words": 12450,
    "sentence_structure": {
      "avg_length": 14.5,
      "variety": "moderate length, conversational",
      "question_ratio": 12.3
    },
    "communication_style": [
      { "trait": "enthusiasm", "percentage": 28.5 },
      { "trait": "analytical", "percentage": 24.1 }
    ],
    "characteristic_expressions": ["you know", "the thing is"],
    "sentence_starters": ["I think", "So the"],
    "signature_vocabulary": ["approach", "strategy", "implementation"],
    "analyzed_at": "2025-01-15T10:00:00Z"
  },
  "notes": "Additional style notes..."
}

Voice Analysis Fields

When transcripts are analyzed, these fields are populated:

FieldDescription
extracted_from
Transcript files analyzed
sample_count
Speaking turns analyzed
total_words
Total words in analysis
sentence_structure
Length, variety, question frequency
communication_style
Traits: enthusiasm, hedging, directness, etc.
characteristic_expressions
Frequently used phrases/fillers
sentence_starters
Common ways to start sentences
signature_vocabulary
Words that characterize the speaker

Using Voice Analysis When Writing

When writing articles, use voice_analysis data:

  1. Sentence structure: Match
    avg_length
    and
    variety
  2. Tone: Follow
    communication_style
    traits
  3. Natural speech: Sprinkle
    characteristic_expressions
    naturally
  4. Vocabulary: Prefer words from
    signature_vocabulary
  5. Sentence starters: Use patterns from
    sentence_starters

Multi-Language Workflow

  1. Article written in author's primary language (first in array)
  2. After completion, translated to other languages
  3. Each file named:
    {slug}.{language}.md

Example for author with

["pt_BR", "en_US"]
:

content/articles/2025_01_15_rate-limiting/
├── rate-limiting.pt_BR.md    # Primary (written first)
└── rate-limiting.en_US.md    # Translation

Default Author

If article task doesn't specify author:

  • First author in
    authors.json
    is used
  • Their language settings apply
  • Their voice/tone is followed

Updating Authors

Add More Transcript Data

# Analyze new transcripts for existing author
bun run "${CLAUDE_PLUGIN_ROOT}"/scripts/voice-extractor.ts \
  --speaker "Name" \
  --author-json \
  new_podcast.txt > new_analysis.json

# Merge into existing profile (manually or via command)

When to Update

  • New transcript data available
  • Writing style evolves
  • Feedback indicates tone mismatch
  • New expertise develops