soul-extraction

name	soul-extraction
description	Use when extracting conversation patterns and logic jumps from tagged dialog reports — reads /conversation-dump output, clusters trigger→reaction pairs into thinking-pattern.md, detects logic jumps with user confirmation for master-thinking.md

Soul Extraction

Extract conversation patterns and logic jumps from tagged dialog reports, producing a thinking profile and a self-interview document.

Phase 1 — Scan

Ask the user to specify:

• Source: claude or codex
• Topic: a specific topic folder name (e.g., skill-design), or all

Read all .json report files in docs/dialog/<source>/<topic>/ (or all topic folders if "all"). Skip non-report files (topics.md, summary.md).

Each JSON file follows the conversation-dump output schema:

{
  "turns": [
    {
      "index": 1,
      "user": "...",
      "assistant": "...",
      "tags": { "bloom": "...", "depth": "...", "probe": "...", "presup": "...", "discourse": "...", "mechanism": "..." },
      "note": "..."
    }
  ]
}

For each turn, extract:

• User message text (user)
• 6-dimension tags from tags object
• Assistant response (assistant — preceding context)
• Turn position (index — Turn 1 = starting question)
• Classification note (note) — if present, use it to understand non-obvious tag assignments. Notes explain why the classifier chose a tag that might seem wrong at face value (e.g., why a statement was tagged probe:assumption-probe). Factor notes into clustering and jump detection when they clarify intent.

Phase 2 — Extract Patterns

A "pattern" is a pair: trigger signature → reaction pattern.

Trigger signatures include:

• Starting questions — Turn 1 messages (what the user brings unprompted)
• Mid-conversation triggers — assistant output, presented options, errors, or results that provoke the next user message

Reaction pattern is characterized by:

• The 6-dimension tag profile
• The action taken (select, command, evaluate, redirect, etc.)
• A natural-language summary

Clustering: Group turns with similar trigger→reaction profiles across sessions. Two turns are "similar" when at least 3 of 4 discriminating dimensions match: bloom, depth (level only, ignoring sub-category), discourse, mechanism. A pattern must appear in 2+ sessions to be recorded.

Name each pattern with a descriptive verb phrase (e.g., "Fail-safe to human", "Redirect scope", "Challenge naming inconsistency").

Pattern format:

### Pattern: <name>

**Trigger:** <description of what provokes this reaction>
**Reaction:** <what the user does>
**Tag profile:** `bloom:X` `depth:Y/Z` `discourse:X` `mechanism:X`
**Frequency:** N occurrences across M sessions
**Examples:**
- Session <id>, Turn N: "<user message>" → <one-line summary>
- Session <id>, Turn N: "<user message>" → <one-line summary>

Phase 3 — Detect Logic Jumps

A "logic jump" is a user message that is NOT a direct response to what the assistant just said — it introduces a new angle, catches a hidden gap, or connects distant concepts.

Detection heuristics (any suggest a candidate):

• Tags show probe:assumption-probe or probe:evidence-probe when the assistant didn't invite scrutiny
• mechanism is exploration or debugging but the assistant didn't present an error or prompt
• bloom jumps from shallow (confirmations) to analyze/evaluate/create without the assistant asking a question
• User introduces a topic or constraint not mentioned in the prior assistant turn
• A starting question (Turn 1) that connects multiple domains or frames a problem unusually

Curation: Not every candidate is worth presenting. From all candidates, select the 5-12 most valuable logic jumps. Value is judged relative to the topic being analyzed — e.g., if the topic is skill-design, evaluate jumps by how they improved skill writing; if debugging, by how they led to root-cause discovery.

Selection criteria:

• Brought good outcomes for the topic — the jump led to a better result in the domain being studied (e.g., better skill design, better code, better paper)
• Introduced a transferable insight — the reasoning pattern could improve future work in the same topic area
• Revealed non-obvious reasoning — the connection between context and question is genuinely surprising

Discard jumps that are routine (e.g., simple scope corrections, obvious next steps, bloom jumps that are just resuming after confirmations).

User confirmation gate: Present ONE curated candidate at a time with full context. When moving to a new session for the first time, start with a session summary:

Session: — <1-2 sentence summary of what this session was about>

Then present the candidate:

Candidate logic jump (Turn N)

Assistant:

You: "<the user's full message>"

Why this is a jump: <explain what gap you detected — what about the prior context makes this message surprising or non-obvious as a next step>

(A) <causality chain>

(B) <causality chain>

(C) <causality chain>

(D) None of these — I'll explain

(E) Skip

The 3 guesses should be substantively different hypotheses about what the user was actually thinking — not surface restatements. Consider: hidden analogies, pattern recognition from other domains, latent dissatisfaction, architectural instincts, or unstated goals.

Express each guess in causality form using + (logical AND of observations/principles) and => (implies). The + terms can appear anywhere in the chain — beginning, middle, or end. Keep it flexible and natural. Examples:

• "simple is good" principle + complex code => not good => should consider simpler approach
• fixed a bug => why did it exist? + responsibilities are tangled => orthogonalize
• design looks complete => dry-run in head + subagent is stateless => missing dependency

If the user picks A/B/C, record that guess as the chain of thought. If D, the user provides their own explanation. If E, discard the candidate. Process candidates one at a time — do NOT batch.

Phase 4 — Output

Write both files to docs/dialog/<source>/<topic>/ (or docs/dialog/<source>/all/ when topic is "all"):

thinking-pattern.md:

# Thinking Patterns

- **Source:** <source>
- **Topic:** <topic or "all">
- **Sessions analyzed:** N
- **Patterns extracted:** M

## Category: <group name>

### Pattern: <name>
**Trigger:** ...
**Reaction:** ...
**Tag profile:** ...
**Frequency:** N across M sessions
**Examples:** ...

---

## Summary

- Total patterns: N
- Most frequent patterns: ...
- Dominant bloom levels: ...
- Dominant mechanisms: ...

master-thinking.md:

# Master Thinking: Logic Jumps

- **Source:** <source>
- **Topic:** <topic or "all">
- **Logic jumps confirmed:** N

## Jump 1: <short title>

**Context:** <what the assistant just said, truncated>
**Your question:** <the actual user message>
**Chain of thought:** `<principle/observation> + <context> => <inference> => <action>`

> **Interview prompt:** "You were looking at [context]. Then you asked [question]. Walk me through what connected those — what were you actually thinking?"

If the user confirmed a guess (A/B/C) or provided their own explanation (D), fill the Chain of thought: field with it. The interview prompt is always generated — it serves as a self-interview anchor even when the chain is already filled.

Note: Incarnate Integration

When soul-extraction is invoked as part of the incarnate skill's contributor onboarding (Step 2), control returns to incarnate after Phase 4 completes. Incarnate Step 3 then uses the outputs written here to synthesize an advisor profile. Do not invoke incarnate from here — incarnate drives the flow.

Important: Do not delete or move the session JSON files in docs/dialog/<source>/<topic>/ after Phase 4. Incarnate reads them directly for presup-based blind spot analysis.