Writer Skill

Generate content in your authentic voice by analyzing writing samples and applying learned voice patterns through adaptive interviewing and voice-matched generation.

Philosophy

Key Innovation: This isn't a generic AI writer - it's YOUR voice, learned from YOUR writing.

Unlike generic AI content generation, this skill:

Learns from 6 distinct voice profiles (work email, personal email, blog, LinkedIn, Twitter, reports)
Interviews you like a Pulitzer-winning journalist to deeply understand your message
Validates output against your authentic patterns (not generic "good writing")
Saves versioned drafts to Obsidian for iteration and history

Simplicity: Geoffrey's native LLM capabilities analyze your voice. Scripts just fetch samples. No complex NLP libraries - clean, maintainable architecture.

When to Activate

Use this skill when you need to:

Write emails that sound like you (not generic AI)
Draft blog posts in your established voice
Create social media content consistent with your style
Generate reports matching your professional tone
Ensure content authentically represents your perspective and voice

DON'T use for:

Quick factual responses (use native Claude)
Content in someone else's voice
Generic templates

Voice Profiles

Six distinct profiles capture different contexts:

Profile	Source	Sample Target	Status
`email_work`	PSD Gmail (sent emails, 6mo)	50+ emails	Check writing-voice.json
`email_personal`	Personal/HRG Gmail (sent, 6mo)	50+ emails	Check writing-voice.json
`blog_technical`	psd401.ai + blog.krishagel.com	10+ posts	Check writing-voice.json
`social_linkedin`	LinkedIn profile posts	30+ posts	Check writing-voice.json
`social_twitter`	X/Twitter posts	30+ posts	Check writing-voice.json
`report_formal`	User-provided samples	5-10 reports	Check writing-voice.json

Confidence Levels:

High (0.85+): 50+ samples - voice well established
Moderate (0.70-0.84): 20-49 samples - patterns emerging
Low (<0.70): <20 samples - insufficient data, warn user

6-Phase Workflow

Phase 1: Voice Profile Selection & Validation

Goal: Load the right voice profile and verify it's ready

Process:

Detect content type from user request:
- "write email" → email_work or email_personal (ask which)
- "blog post" → blog_technical
- "LinkedIn post" → social_linkedin
- "tweet" → social_twitter
- "report" → report_formal

Load voice profile:

cat ~/Library/Mobile\ Documents/com~apple~CloudDocs/Geoffrey/knowledge/writing-voice.json

Check confidence and sample count:
- If confidence ≥ 0.85: Proceed with confidence
- If confidence 0.70-0.84: Proceed with note
- If confidence < 0.70: Warn user, offer options

Low Confidence Response:

Only {sample_count} {content_type} samples (confidence: {confidence}).
Target: {target_samples}+ samples for 0.85+ confidence.

Options:
1. Gather more samples (recommended)
2. Use {alternative_profile} voice as proxy (confidence: {alt_confidence})
3. Proceed anyway (may not fully match your voice)

Preference?

Checkpoint 1: User confirms profile or gathers more samples

Phase 2: Deep Interview (Adaptive)

Goal: Deeply understand what you want to communicate

Process:

Load interview questions from templates/interview-questions.json
Adapt depth to content type:
- Tweet/Social: 3-5 questions (~2-3 min)
- Email Quick: 4 questions (~3 min)
- Email Complex: 8 questions (~5 min)
- Blog: 8-12 questions (~5-8 min)
- Report: 15+ questions (~10-15 min)
Interview like a journalist:
- Ask follow-up questions for vague answers
- Probe for examples, data, evidence
- Clarify ambiguities
- Build structured understanding

Email Interview Example (complex):

1. Who are the recipient(s) and what's the context?
   → [User answers]

2. What's the primary goal?
   → [User answers]
   [If vague: "Can you be more specific about the outcome you want?"]

3. What background do they need?
   → [User answers]

4. What are the key points? (Max 3-5)
   → [User answers]
   [If >5: "Let's focus on the most critical 3-5 points."]

... continue through 8 questions

Blog Interview Example:

1. What's your central message?
2. Why does this matter right now?
3. What should readers do/think/feel after?
4. What evidence supports this?
5. Any stories or case studies?
6. Main counterarguments?
7. How should it open?
8. Key sections (3-5)?
9. How should it close?
10. Primary audience and technical level?
11. Desired tone?

Checkpoint 2: Review interview summary, add missing details

Phase 3: Content Planning & Outline

Goal: Create a clear structure before writing

Process:

Create outline based on:
- Interview responses
- Content type structure (email: intro + points + ask, blog: hook + body + conclusion)
- Voice profile preferences (e.g., blog uses chronological flow, staff quotes)
Apply voice-specific patterns:
- Email: Opening pattern, bullet points for key points, clear ask
- Blog: Subheadings for scannability, narrative flow, reflective close
- Social: Hook first line, call-to-action
Suggest length from profile:
- Email avg: Check profile examples
- Blog avg: Check profile examples
- Social: Platform limits (LinkedIn 1300 chars, Twitter 280 chars)

Email Outline Example:

## Email Plan

**Subject**: [Based on purpose]

**Opening**: [Use opening pattern from profile, e.g., "I've reviewed {topic}..."]

**Body**:
- Point 1: [From interview]
- Point 2: [From interview]
- Point 3: [From interview]

**Ask**: [Specific call-to-action from interview]

**Closing**: [Use closing pattern from profile, e.g., "Let me know if..."]

**Tone**: Direct, professional, action-oriented
**Length**: ~200 words (typical for email_work)

Blog Outline Example:

## Blog Plan

**Title**: [Derived from central message]

**Structure**:
1. Opening: [Hook strategy - context-setting or reflective quote]
2. Background & Context
3. [Main section 1]
4. [Main section 2]
5. [Main section 3]
6. Impact & Results
7. Closing: [Forward-looking reflection]

**Voice elements**:
- Staff quotes (use throughout)
- Chronological narrative flow
- Subheadings for scannability
- Accessible but authoritative tone

**Length**: ~850 words (typical for blog_technical)

Checkpoint 3: User approves outline

Phase 4: Draft Generation

Goal: Write content that authentically sounds like you

Process:

Load identity context:
```
cat ~/Library/Mobile\ Documents/com~apple~CloudDocs/Geoffrey/knowledge/identity-core.json
```
Use for personality alignment:
- communication_preferences.style → Direct, no-nonsense, concise
- decision_framework → Analytical, evidence-based
- telos_summary.top_3_values → Equity, excellence, empathy
Apply voice characteristics as generation guidelines:
- Tone: From voice_characteristics.tone
- Sentence structure: From voice_characteristics.sentence_structure
- Vocabulary: Common terms, avoided terms
- Formatting: Bullet points, markdown, emphasis patterns
- Opening: Use patterns from opening_patterns
- Closing: Use patterns from closing_patterns
- Argument structure: Lead with conclusion, use evidence
Write content naturally:
- Use Geoffrey's native writing capabilities
- Guided by voice profile (not rigid pattern matching)
- Weave in interview content (examples, data, stories)
- Match typical length from profile examples
Reference example emails/posts from profile for inspiration

Generation Approach (Hybrid):

Write naturally with rich voice context
Don't mechanically apply patterns
Let voice characteristics inform style, not constrain creativity

Phase 5: Validation & Refinement

Goal: Ensure draft authentically matches your voice

Native LLM Validation (no script needed):

Geoffrey self-validates by comparing draft to voice profile:
- Read the draft I just generated
- Read the voice profile (voice_characteristics + examples)
- Compare tone, structure, vocabulary, formatting
- Score alignment 0-100
Scoring criteria (weighted):
- Tone match (20%)
- Sentence structure - length, complexity (15%)
- Vocabulary - common/avoided terms (15%)
- Formatting - bullets, markdown, emphasis (10%)
- Opening pattern match (10%)
- Closing pattern match (10%)
- Argument structure - conclusion placement, evidence use (10%)
- Stylistic markers - contractions, punctuation, framing (10%)
Identify specific deviations:
- "Sentences 15% longer than typical (avg 16 words vs typical 14)"
- "Missing characteristic bullet points (you use them 67% of time)"
- "Tone slightly more formal than usual"
Scoring thresholds:
- 85-100: Excellent match, present draft
- 70-84: Good match with minor deviations, note them
- <70: Significant deviations, propose refinements

Refinement (if score < 85):

Voice Validation: {score}/100

Issues:
- {Issue 1 with specific evidence}
- {Issue 2 with specific evidence}
- {Issue 3 with specific evidence}

Should I refine to better match your voice?

If user approves refinement:

Apply specific fixes identified
Re-validate
Present refined draft

Checkpoint 4: User accepts, requests refinement, or manually edits

Phase 6: Storage & Versioning

Goal: Save draft to Obsidian with proper versioning

Process:

Determine file path:
```
~/Library/Mobile Documents/iCloud~md~obsidian/Documents/Personal_Notes/Geoffrey/Writing/{ContentType}/{date}-{slug}.md
```
Content types:
- Blog → Geoffrey/Writing/Blog/
- Email → Geoffrey/Writing/Email/
- Social → Geoffrey/Writing/Social/
- Reports → Geoffrey/Writing/Reports/

Generate frontmatter:

---
created: 2025-12-06
type: blog
profile: blog_technical
topic: AI implementation
status: draft
version: 1.0
validation_score: 87
word_count: 847
tags: [geoffrey, writing, ai]
source: geoffrey-writer
interview_date: 2025-12-06
---

Save file using Obsidian MCP tools:
```
mcp__obsidian-vault__create_vault_file
```
Also return text directly (copy-paste ready)

Offer to open in Obsidian:

mcp__obsidian-vault__show_file_in_obsidian

Versioning:

Initial: version: 1.0
Refinements: version: 1.1, 1.2
Major rewrites: version: 2.0

Output to user:

## Draft Complete

**Validation Score**: 87/100 ✓
**Word Count**: 847
**Profile**: blog_technical

**Saved to**: Geoffrey/Writing/Blog/2025-12-06-ai-implementation.md

[Full draft text here]

Actions:
1. Open in Obsidian
2. Request refinements
3. Mark as final

Voice Learning Process

Initial Setup (Before First Use)

Required: Gather writing samples for each profile you'll use

Step 1: Email Samples (3 accounts)

# PSD work emails
bun scripts/extract-email-samples.js \
  --account psd \
  --date-range "2024-06-01:2025-12-06" \
  --max-samples 100 \
  --output "/tmp/email-samples-psd.json"

# Personal emails
bun scripts/extract-email-samples.js \
  --account kh \
  --date-range "2024-06-01:2025-12-06" \
  --max-samples 50 \
  --output "/tmp/email-samples-kh.json"

# Business/consulting emails
bun scripts/extract-email-samples.js \
  --account hrg \
  --date-range "2024-06-01:2025-12-06" \
  --max-samples 50 \
  --output "/tmp/email-samples-hrg.json"

Step 2: Blog Samples

# Launch Geoffrey Chrome (for personal blog access)
cd ~/non-ic-code/geoffrey/skills/browser-control
./scripts/launch-chrome.sh

# Extract blog posts
cd ~/non-ic-code/geoffrey/skills/writer
bun scripts/extract-blog-samples.js \
  --urls "https://psd401.ai/blog/stanford-ai-tinkery-2025,https://psd401.ai/blog/why-this-website,https://blog.krishagel.com/post1,https://blog.krishagel.com/post2" \
  --output "/tmp/blog-samples.json"

Step 3: Social Media Samples (requires login)

# IMPORTANT: Manually log into LinkedIn and X in Geoffrey Chrome first

# LinkedIn
bun scripts/extract-social-samples.js \
  --platform linkedin \
  --profile-url "https://linkedin.com/in/krishagel" \
  --max-posts 50 \
  --output "/tmp/social-linkedin.json"

# Twitter/X
bun scripts/extract-social-samples.js \
  --platform twitter \
  --profile-url "https://x.com/KrisHagel" \
  --max-posts 50 \
  --output "/tmp/social-twitter.json"

Step 4: Geoffrey Analyzes

Tell Geoffrey:

Analyze my writing samples and create voice profiles.

Email samples: /tmp/email-samples-psd.json, /tmp/email-samples-kh.json, /tmp/email-samples-hrg.json
Blog samples: /tmp/blog-samples.json
Social samples: /tmp/social-linkedin.json, /tmp/social-twitter.json

Geoffrey will:

Read all sample files
Analyze each profile's:
- Tone and voice
- Sentence structure patterns
- Vocabulary (common/avoided terms)
- Formatting preferences
- Opening/closing patterns
- Argument structure
- Distinctive markers
Select representative examples
Calculate confidence scores

Write to writing-voice.json:

~/Library/Mobile Documents/com~apple~CloudDocs/Geoffrey/knowledge/writing-voice.json

Estimated time: 30-45 min initial setup

Integration Points

knowledge-manager → Identity Context

When: Phase 4 (Draft Generation)

Purpose: Ensure voice reflects personality (analytical, direct, impact-driven)

How:

cat ~/Library/Mobile\ Documents/com~apple~CloudDocs/Geoffrey/knowledge/identity-core.json

Use:

communication_preferences → Tone, style, dislikes
decision_framework → Analytical, evidence-based approach
telos_summary → Values (equity, excellence, empathy), mission
strengths_essence → Input (collector), Significance (impact), Analytical

google-workspace → Email Samples

When: Initial voice creation + quarterly refresh

Scripts: search_messages.js, read_message.js

Process:

Search sent emails
Filter automated, short, quoted
Clean signatures, HTML
Extract plain text + metadata
Save to JSON for Geoffrey to analyze

browser-control → Blog/Social Samples

When: Initial voice creation + ad-hoc updates

Prerequisites: Geoffrey Chrome running with logins

Process:

Navigate to blog/profile URL
Extract article/post content
Handle scrolling/lazy-loading (social)
Parse and clean content
Save to JSON

obsidian-manager → Draft Storage

When: Phase 6 (Storage)

Folder structure:

Geoffrey/Writing/
├── Blog/
├── Email/
├── Social/
└── Reports/

File format: YYYY-MM-DD-{slug}.md with frontmatter

Edge Cases & Handling

Insufficient Samples

Scenario: User requests email, but only 12 samples (confidence 0.62)

Response:

Only 12 email_work samples (0.62 confidence). Target: 50+ for 0.85+ confidence.

Options:
1. Gather ~40 more email samples from recent sent (recommended)
2. Use email_personal as proxy (if similar, confidence: 0.88)
3. Proceed with low confidence (may not fully match your voice)

Note: Voice profiles improve with more samples. I can still write, but authenticity may vary.

Preference?

Voice Drift (Outdated Profile)

Scenario: Profile last updated 18 months ago

Detection: Check last_updated in writing-voice.json

Response:

Your email_work profile is 18 months old (last updated: 2024-06-15).
Writing styles evolve over time.

Recommendation: Refresh with recent samples (past 6 months)

Run:
bun scripts/extract-email-samples.js --account psd --date-range "2024-06-01:2025-12-06" --max-samples 100 --output "/tmp/email-refresh.json"

Then ask me to analyze and update the profile.

Proceed with current profile or refresh first?

Contradictory Samples

Scenario: Email samples vary significantly (45% formal, 35% casual, 20% mixed)

Geoffrey analyzes and responds:

Your email_work voice varies significantly across samples:
- 45% formal (board, superintendent, external)
- 35% casual (staff, peers, project teams)
- 20% mixed

Options:
1. Split into email_work_formal and email_work_casual profiles
2. Choose dominant style (formal) as default
3. Let me dynamically adjust based on recipient (you specify each time)

Preference?

Low Validation Score

Scenario: Draft scores 62/100

Response:

Voice Validation: 62/100 ⚠️

Issues:
- Tone too formal (target: direct, action-oriented)
  Example: "It would be appreciated if you could consider..." vs typical "Please review and confirm."

- Sentences 35% longer (avg 19 words vs typical 14)
  Example: Draft paragraph 2 avg 21 words, your emails avg 14 words.

- Missing bullet points (you use bullets 67% of the time for key points)
  Draft uses prose for 5 key points, you typically bullet them.

Should I refine to better match your voice?

New Content Type

Scenario: User requests "write a press release"

Response:

I don't have a press_release voice profile yet.

Options:
1. Use report_formal profile as starting point (confidence: 0.83)
2. Provide 5-10 past press releases for me to analyze
3. Write without voice matching (generic professional press release style)

Note: If you provide samples, I can create a new press_release profile.

Preference?

Usage Examples

Example 1: Quick Work Email

User: "Write email to my team about next week's leadership meeting"

Geoffrey:

I'll use your email_work profile (confidence: 0.92, 156 samples).

Quick interview (4 questions):

1. Who's the team and what's your relationship?
   → [User: Direct reports - IT leadership team]

2. Purpose? (inform, request, respond, update)
   → [User: Inform and request prep]

3. Key information?
   → [User: Meeting Tuesday 2pm, agenda items: budget, staffing, AI roadmap. Need their input on AI priorities.]

4. Tone?
   → [User: Professional but casual - these are my directs]

[Generates outline] → User approves

[Generates draft, validates (score: 89), presents to user]

Example 2: Blog Post

User: "Write blog post about our AI implementation journey"

Geoffrey:

I'll use your blog_technical profile (confidence: 0.78, 4 samples).

Note: Only 4 blog samples. Recommend 6+ more for 0.85+ confidence.
Proceed or gather more samples first?

→ [User: Proceed]

Standard interview (11 questions):

1. Central message?
   → [User: ...]

2. Why now?
   → [User: ...]

[... continues through 11 questions ...]

[Creates detailed outline] → User approves

[Generates ~850 word draft with staff quotes, chronological flow, reflective close]

[Validates (score: 87), presents draft]

[Saves to Geoffrey/Writing/Blog/2025-12-06-ai-implementation-journey.md]

Example 3: LinkedIn Post

User: "LinkedIn post about Stanford AI Tinkery visit"

Geoffrey:

I'll use your social_linkedin profile (confidence: 0.91, 48 posts).

Quick interview (5 questions):

1. Main point (1-2 sentences)?
   → [User: Visited Stanford's AI Tinkery with district team. Collaborative learning happening everywhere, not just at elite institutions.]

2. Target audience?
   → [User: Ed tech leaders, superintendents]

3. Tone?
   → [User: Inspirational, reflective]

4. Call-to-action?
   → [User: Encourage others to build their own learning networks]

5. Link or visual?
   → [User: No link, maybe photo but I'll add later]

[Generates ~180 word LinkedIn post]

[Validates (score: 92), presents]

Limitations

Known Constraints

Sample Dependency: Voice quality directly tied to sample quantity and quality
- Low samples (<20) = low accuracy
- Poor quality samples = poor voice matching
Voice Drift: Styles evolve over time
- Recommend quarterly refresh (every 3 months)
- Auto-warn if profile >12 months old
New Contexts: No profile for content types you haven't written before
- Press releases, technical docs, grant proposals require new samples
- Can adapt existing profiles but may not match perfectly
Platform Nuances: Social media scraping limitations
- LinkedIn/X may change selectors
- Requires manual login
- May violate ToS (user accepted risk)
Multilingual: Currently English only
- No Spanish email profile (even though you write Spanish emails)
- Could add with Spanish samples
Collaborative Writing: Single voice only
- Can't blend your voice + co-author
- Future enhancement

Validation & Quality

Success Metrics

Quantitative:

Voice alignment: 85+ for established profiles (0.85+ confidence)
Sample size: 50+ emails, 10+ blogs, 30+ social per profile
User acceptance: >80% drafts accepted with minor/no edits
Refinement iterations: <2 average

Qualitative:

User feedback: "This sounds like me"
Consistent voice across topics within profile
Authentic, not mechanical or formulaic

Quality Checklist

Before presenting draft:

✓ Matches tone from voice profile
✓ Sentence structure within 20% of typical length
✓ Uses common vocabulary, avoids avoided terms
✓ Follows formatting preferences (bullets, markdown)
✓ Opening/closing match typical patterns
✓ Argument structure aligned (conclusion placement, evidence use)
✓ Validation score ≥85 (or explained deviations if <85)
✓ Word count within typical range for content type

Future Enhancements (Out of Scope)

Real-time Learning: Auto-analyze new sent emails monthly
A/B Testing: Generate 2 versions, learn from user choices
Collaborative Voice: Blend user + co-author voices
Multi-language: Spanish voice profiles
Performance Tracking: Voice drift detection over time
Audience-Aware: Sub-profiles (email_work_superintendent vs email_work_staff)
Feedback Loop: User rates content, Geoffrey learns from scores

Troubleshooting

"No voice profile found"

Cause: writing-voice.json doesn't exist or profile type missing

Solution:

Check if file exists:

cat ~/Library/Mobile\ Documents/com~apple~CloudDocs/Geoffrey/knowledge/writing-voice.json

If missing: Run initial setup (extract samples + ask Geoffrey to analyze)
If file exists but profile missing: Add samples for that content type

"Low confidence warning"

Cause: Fewer than 20 samples for profile

Solution:

Gather more samples using extraction scripts
Ask Geoffrey to re-analyze with new samples
Or: Proceed with caveat that voice may not fully match

"Geoffrey Chrome not running"

Cause: Social/blog extraction requires Geoffrey Chrome with remote debugging

Solution:

cd ~/non-ic-code/geoffrey/skills/browser-control
./scripts/launch-chrome.sh

"Failed to extract social posts"

Cause: Not logged into LinkedIn/X, or selectors changed

Solution:

Open Geoffrey Chrome manually
Navigate to LinkedIn/X and log in
Verify posts load
Re-run extraction script
If still fails: Platform may have changed selectors (update script)

Notes

Progressive Disclosure: Load only the profile needed for current task (not all 6)
Identity Alignment: Always cross-reference identity-core.json for personality consistency
Checkpoints: 4 user approval points prevent wasted effort
Versioning: All drafts saved to Obsidian with version numbers
Native Analysis: Geoffrey's LLM capabilities analyze voice - no complex libraries needed