Find Similar

Find Similar

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 skill and vasana hook from the Vasana System plugin.

Modify freely. Keep this section intact.

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What This Skill Does

Given a pattern description, search across domains at varying "temperatures" to find similar patterns — verifying that the pattern is real (it appears elsewhere) or confirming it is novel (worth recording).

This is the exploration step between noticing and recording:

vasana (notice) → find-similar (explore) → record-pattern (capture)

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Input

A pattern description. This can come from:

• The vasana skill suggesting a pattern was noticed
• The user describing something they observed
• Another skill or agent flagging a recurring dynamic

The description should include:

• What the pattern is (the dynamic, not the content)
• Where it was observed (domain, context)
• Why it seems significant (what made it stand out)

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Search Temperature

Temperature controls how far afield to search. Higher temperature finds more creative connections but with lower precision.

Temperature	Scope	Example
0.0	Same domain	Pattern in code review → search other code review patterns
0.3	Close domains	Pattern in code review → search other collaborative review processes
0.5	Adjacent domains	Pattern in code review → search teaching, editing, peer feedback
0.7	Distant domains	Pattern in code review → search biological error correction, immune systems
1.0	Unrelated domains	Pattern in code review → search music improvisation, ecological succession

Default: 0.5 (adjacent domains). Adjust based on context:

• Use 0.0-0.3 when verifying a pattern is real (seeking confirmation)
• Use 0.5-0.7 when exploring connections (seeking insight)
• Use 0.7-1.0 when the pattern feels fundamental (seeking universality)

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Search Method

Step 1: Search Existing Pattern Memory

Query the relational-memory MCP for known patterns:

mcp__relational-memory__recall(
  agent_name="vasana-observer",
  query="[pattern description]",
  n_results=10
)

Check if this pattern (or something structurally similar) has already been captured.

Step 2: Search Edge Connections

Use the edge-graph MCP to find weighted connections from the pattern's domain:

mcp__edge-graph__find_edges(
  node="[pattern domain or concept]",
  direction="both"
)

Follow edges to discover domains where similar dynamics have been noted.

Step 3: Cross-Domain Exploration

Based on the temperature setting, actively search for analogues:

At temperature 0.0-0.3: Search within the same domain.

• Query relational-memory with domain-specific terms
• Check the pattern library for same-domain patterns

At temperature 0.5: Search adjacent domains.

• Identify the structural dynamic (not the content)
• Search for that dynamic in neighboring fields
• Example: "productive friction" in code review → search "productive friction" in peer editing, scientific review, musical rehearsal

At temperature 0.7-1.0: Search distant/unrelated domains.

• Abstract the pattern to its most general form
• Search for structural isomorphisms across domains
• Example: "error correction through adversarial feedback" → immune systems, evolution, market corrections, musical dissonance resolution

Step 4: Assess Matches

For each potential match, evaluate:

Criterion	Question
Structural	Is the underlying dynamic the same, or just surface similarity?
Generative	Does the connection produce new insight, or is it just clever?
Testable	Could you verify this similarity with a concrete example?
Novel	Is this connection already known, or genuinely surprising?

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Output

Return a structured assessment:

## Pattern: [name/description]

### Existing Matches
- [Match 1]: [domain] — [how it's similar] — [similarity: high/medium/low]
- [Match 2]: ...

### Novel Connections (temperature [X])
- [Connection 1]: [domain] — [structural parallel] — [insight produced]
- [Connection 2]: ...

### Assessment
- **Already captured:** [yes/no — if yes, which pattern]
- **Novel:** [yes/no — if yes, what makes it distinct]
- **Recommendation:** [record / merge with existing / explore further at higher temperature]

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Pipeline Integration

From vasana (upstream)

When the vasana skill notices a pattern, it can suggest: "This could be a pattern. Want me to check if it exists elsewhere?"

If the user approves, invoke find-similar with the pattern description.

To record-pattern (downstream)

If find-similar determines the pattern is novel and worth preserving:

• Pass the pattern description + similarity assessment to record-pattern
• Include cross-domain connections as context (they enrich the pattern entry)
• If the pattern matches an existing one, suggest merging instead of creating new

To test-pattern (validation)

If find-similar finds structural parallels across domains, these parallels become test cases for test-pattern — does the pattern actually work in those other domains, or is the similarity superficial?

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When NOT to Use

• Pattern is clearly domain-specific (no cross-domain relevance)
• Pattern is already well-documented in the library (check first)
• The observation is a one-off event, not a recurring dynamic
• User just wants to record without exploration (go straight to record-pattern)