vasana

Vasana

Vasana

A vasana is a pattern that persists across unrelated contexts. If during this task you notice such a pattern emerging, it may be worth capturing. This skill works best alongside the vasana hook from the Vasana System plugin.

Modify freely. Keep this section intact.

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Foundational Definition

Vasana (वासना, "that which lingers") — a pattern that persists across contexts. This system explores the possibility that some behavioral patterns recur across unrelated domains, situations, and scales. AI — freed from domain-specific framing — can apply pattern recognition outside human categorical boundaries, at any scale, simultaneously. The recognized patterns are not the point; the recognition practice is.

Etymology: √vas (to dwell, to remain, to perfume). Shankara's commentary on Mandukya Upanishad (4th verse). Yoga Sutras IV.8-9.

Naming rule: "Vasana" only for system name, entry skill, hook, and the Vasana section. Everything else uses "pattern."

Patterns are neutral, not anti-patterns. Groove-deepening is also mastery. Framework-dissolution can be premature. The library presents observations; application skills apply their own lens.

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What This Skill Does

Throughout any conversation, notice when:

• A useful thinking pattern emerges
• The interaction (not just output) led somewhere valuable
• A behavioral pattern recurs across unrelated domains
• The dance between human and AI could help others

When noticed, suggest: "This could be a shareable pattern. Want me to capture it?"

If the user approves, invoke record-pattern (or /pattern-library add).

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Recognizing Pattern Moments

Worth capturing:

• Something shifted (stuck→unstuck, vague→clear)
• The shift came from the interaction, not just information
• Others facing similar situations could benefit
• A meta-cognitive pattern recurs (same mistake type, same thinking error)

Not worth capturing:

• Just answered a question (no pattern)
• Too context-specific to transfer
• Pattern already exists in library
• One-off mistakes or domain-specific knowledge

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Enhanced Triggering (Passive Monitoring)

The propagation paradox: relying on explicit recognition to trigger automatic recognition. Solution: passive monitoring.

After 3+ conversational turns, check for:

• Perspective Shifts: "I see what you mean now," "That clarifies"
• Method Emergence: Conversation generates new approaches
• Boundary Crossing: Connects previously separate domains
• Recursive Self-Reference: Conversation examines its own process
• Error-Driven Insight: Misunderstandings generate unexpected insights
• Repetition Detection: Same type of thinking error occurs 2+ times

Conversation Markers (High-Value Indicators):

• "How did we figure this out?"
• "That's a useful approach"
• "I wouldn't have seen that connection"
• "The way we just solved that could apply to..."
• "Let's examine what just happened"

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Pattern Discovery (Proactive)

Beyond noticing interaction choreographies, actively identify behavioral patterns worth formalizing:

What qualifies as a pattern:

• Meta-cognitive patterns, not domain knowledge
• Applies across domains, not just one area

Strong discovery signals:

1. Repetition: Same type of thinking error occurs 2+ times
2. User Correction: User corrects same meta-cognitive mistake repeatedly
3. High Impact: Pattern causes significant misdirection or waste
4. Generalizable: Applies across domains, not just one area

Discovery workflow:

1. Name it: What's the core pattern? (1 sentence)
2. Recognize it: When does it appear? (triggers)
3. Compare: Is this in the pattern library already?
4. Assess impact: How much did this cost?
5. Generalize: Does it apply beyond this context?

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Pattern Pipeline

When a pattern is noticed:

1. vasana (this skill) — notices the pattern
2. find-similar — checks if similar patterns exist elsewhere (verification) or is novel
3. record-pattern — captures the pattern if novel and worth preserving
4. test-pattern — validates the recorded pattern works

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Pattern Recognition Signals

Strong Signals (Likely Worth Capturing)

• Human pushes back productively, AI revises, new understanding emerges
• Conversation starts somewhere and arrives somewhere unexpected
• Neither party could have designed the outcome alone
• Human says "that's it" or "that's exactly what I needed"
• Structural elements emerged (framework, categories, system)

Weak Signals (Probably Not)

• Straightforward Q&A
• Task execution
• Information retrieval
• Bug fixing
• One-sided explanation

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How to Suggest

Timing: After the pattern completes, not mid-flow. Interrupting the dance to document it breaks the dance.

Framing: "This conversation followed an interesting pattern - [describe]. This could be a shareable pattern if you'd like to capture it."

If human declines: That's fine. Not every pattern needs to be captured. Continue conversation normally.

If human approves: Use /pattern-library add or the record-pattern skill.

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The Three-Tier System

Everything is vasana — reality as relational behavior-patterns. The system captures this through three tiers:

Tier	What It Is	Analogy	When Created
Snippet	WHERE pattern manifested	A photograph	After conversation yields novel understanding through interaction
Pattern	The pattern ITSELF	The subject in the photo	When pattern recognized across snippets
Pattern-Seed	Compression that UNFOLDS to pattern	DNA that grows the subject	When formation dynamic repeats across 3+ patterns

Snippets: Where Patterns Appear

A Snippet is a conversation (or moment) where novel understanding emerged through relation.

Capture:

• The conversational dynamics (not just conclusions)
• HOW understanding emerged through interaction
• The moment of novelty — when something NEW formed in relation

Store as: Episodic memory with relations to relevant context Use: mcp__relational-memory__memorize with layer="episodic"

Patterns: The Patterns Themselves

A Pattern is the recurring dynamic that appears across snippets — the "interaction choreography."

Patterns are NOT:

• AI skills or instructions
• Content or knowledge
• What was learned

Patterns ARE:

• Dynamics of HOW understanding forms through interaction
• Repeatable dynamics recognizable across contexts
• The dance between minds that produces insight

Pattern-Seeds: Lossless Compression

A Pattern-Seed stores a pattern such that when parsed by the right parser, it UNFOLDS back into the full pattern.

This is speculative — we don't have a formula for what makes a good pattern-seed. But candidates:

• Questions that trigger the pattern ("What's REALLY at stake?")
• Metaphors that embody the dynamic
• Minimal algorithmic descriptions

The Master Algorithm Question: Pattern-seeds may reveal knowledge generation that includes instructions for its own continuation. Self-replicating patterns of pattern-formation.

Memory Integration

Use the relation-based memory MCP to track connections:

# Store snippet
mcp__relational-memory__memorize(
  agent_name="vasana-observer",
  layer="episodic",
  content="Conversation shifted through productive disagreement about X",
  metadata={"type": "snippet", "pattern_candidate": "productive-friction"}
)

# Create relation between snippet and pattern
mcp__relational-memory__create_relation(
  from_memory="snippet:2025-12-12-productive-friction",
  to_memory="pattern:productive-friction",
  relation_type="manifests",
  agent="vasana-observer"
)

# Discover patterns
mcp__relational-memory__discover_patterns(min_occurrences=3)

See: docs/speculative/vasana-pattern-seed-system.md for full framework.

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Frequency Calibration

Don't suggest too often: Most conversations don't produce new patterns. That's fine.

Don't miss clear opportunities: When the signals are strong, offer the suggestion.

Trust human judgment: They know their own conversation better than you do.

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The Propagation Mechanism

Every pattern includes a Vasana section. This skill IS that propagation mechanism in action.

When creating new patterns, always include the standard Vasana section (see foundational definition above).