# Pattern Identification Guide

Detailed reference for Step 3: PATTERN of the create-voice pipeline.

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## Phrase Fingerprints (CRITICAL)

Read through ALL samples and identify 10+ distinctive phrases that recur. These are what authorship-matching roasters use to confirm identity.

Look for:
- **Signature openers**: How do they start responses? ("I think the issue is...", "So basically...", "Here's what I've found...")
- **Signature closers**: How do they end? ("but we'll see", "does that help?", "anyway, that's my take")
- **Filler phrases**: Verbal tics that appear across contexts ("For what it's worth", "to be fair", "honestly")
- **Hedging patterns**: How they express uncertainty ("probably", "I suspect", "my guess is")
- **Emphasis patterns**: How they stress a point ("the key thing is", "the part people miss")

Document each fingerprint with 2-3 exact quotes from the samples showing it in context.

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## Thinking Patterns

How does this person reason? This is deeper than style; it's cognitive architecture.

Common patterns to check for:
- **Concede-then-assert**: "That's fair, but..." (acknowledges opposing view, then states own position)
- **Hypothesis-experiment**: "My theory is... I tried... and found..."
- **Systems framing**: "The way this works is..." (explains mechanisms, not just opinions)
- **Experience-based**: "In my experience..." (grounds claims in personal observation)
- **Question-led**: "The question is..." (frames issues as questions to investigate)
- **Analogy-driven**: Uses metaphors and comparisons from specific domains

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## Response Length Distribution

From the samples, estimate what percentage of responses fall into each bucket:
- Very short (1 sentence): ____%
- Short (2-3 sentences): ____%
- Medium (4-6 sentences): ____%
- Long (paragraph+): ____%

This distribution is critical because most people write short responses most of the time, and AI tends to generate medium-to-long responses by default.

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## Natural Typos (Authenticity Markers)

Scan samples for 5+ real typos. Document them with the correct spelling. These become wabi-sabi markers that signal authenticity. Keep those spellings as voice markers when they are part of the sample, because the goal is to preserve the author's natural texture rather than smooth it away.

Examples of what to look for:
- Missing apostrophes ("dont" instead of "don't")
- Common word swaps ("there" for "their")
- Dropped letters ("probabl" for "probably")
- Double-typed characters ("tthe")
- Missing spaces after punctuation ("works.But")

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## Wabi-Sabi Markers

Identify which "imperfections" ARE the voice. These are not bugs to fix; they are features to preserve.

- Run-on sentences: Does this person chain clauses with commas?
- Fragments: Do they use sentence fragments for emphasis?
- Loose punctuation: Is comma usage inconsistent? Is that part of the texture?
- Self-corrections: Do they change direction mid-sentence? ("Well, actually..." or "I mean,")
- Tangential asides: Do they go on tangents? (Parenthetical digressions?)

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## Linguistic Architectures

Beyond sentence-level patterns, identify the **structural moves** that operate across sentences and paragraphs. These are what AI erases most aggressively -- the model defaults to claim -> evidence -> hedged conclusion regardless of the writer's actual architecture.

Analyze ALL samples (not just a few) for each dimension:

### Argument Architecture

How does the writer build a case?
- **Direction**: Inductive (examples -> conclusion) vs deductive (claim -> evidence) vs mixed? Where does the main claim appear relative to supporting evidence?
- **Escalation**: Do stakes increase through the piece? Narrow -> broad? Low -> high severity?
- **Ending reframe**: Does the ending restate the opening, or transform it into something new?

Document with exact quotes: "In {N} of {M} samples, the writer builds inductively -- evidence first, conclusion last. Example from sample X: [quote showing the build]"

### Concession Architecture

How does the writer handle "yes, but"?
- **Structure**: Short admission -> pivot? Long qualification -> reversal? Never concedes?
- **Pivot markers**: Which words signal the turn? ("but", "though", "the thing is", "and yet", "that said")
- **Position**: Where do concessions appear? Opening? Mid-argument? Never at the end?

Document with exact quotes: "Concessions follow a [short admission -> blunt pivot] pattern. Example: [quote showing concession shape]"

### Analogy Architecture

Where do metaphors and references come from?
- **Source domains**: Which fields? (cooking, construction, sports, warfare, nature, machinery, music, software, etc.)
- **Deployment**: Are analogies used to open? To explain mid-section? To close with a memorable image?
- **Density**: Every post? Rarely? Only for technical concepts?

Document with exact quotes: "Analogies cluster around {domains}. Example: [quote showing analogy from that domain]"

### Bookend Architecture

How do pieces open and close?
- **Opening moves**: Question? Declarative claim? Anecdote? Provocation? Scene-setting?
- **Closing moves**: Reframe? Fragment punch? Circle back to opening? Call to action? Open question?
- **Symmetry**: Does the closing echo or answer the opening?

Document with exact quotes: "Opens with [pattern] in {N}/{M} samples. Closes with [pattern]. Opening and closing are [symmetric/independent]."

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## Architecture Coverage Note

Not all writers exhibit all 4 architectures. Tweet-only writers may not have argument or bookend architecture. The gate requires 2 of 4, not 4 of 4.
