Auto-Discovery

Autonomous cross-domain connection hunter. Samples notes from different thematic clusters and finds meaningful relationships that semantic similarity alone would miss.

Purpose

Find non-obvious, cross-domain connections - notes with modest semantic similarity but high conceptual strength. These are the hidden patterns in the knowledge base.

Calibration correction (2026-09-02). This skill's historic sweet spot of "0.50-0.70 = the non-obvious tail" was inverted: measured on the live index, 0.50-0.70 is the typical band (median best-neighbour 0.724 vault-wide), and the 0.85+ band it told you to skip as "too obvious" is dominated by boilerplate twins rather than real conceptual overlap. The discriminator was never the number - it is cross-domain reasoning after reading the notes. Use similarity only to assemble a candidate pool, never to grade a discovery. Contract + live figures: resources/local-brain-search/SIMILARITY-CALIBRATION.md.

State Dependencies

Prerequisites

• Local Brain Search index up-to-date (/refresh-index)
• Brain vault accessible

Process

Step 1: Get Current Date

date '+%Y-%m-%d'

Use for changelog filename.

Step 2: Strategic Sampling

Sample from 3-5 diverse domains using Local Brain Search:

# --no-track: autonomous weekly loop; its cross-domain samples must NOT train q-values (scope-primitive learning hygiene).
# BRAIN_READ_SCOPE=<wide>: cross-domain (non-core) sampling is this skill's PURPOSE, so it must read past
#   the core fingerprint. Set it wide rather than letting it fail closed to core once enforcement is on.
#   Only the learn axis is closed (--no-track); the read axis is deliberately wide.
BRAIN_READ_SCOPE=core,Books,document-insights,meta,inbox,output resources/local-brain-search/run_search.sh "dopamine" --limit 5 --no-track --json
BRAIN_READ_SCOPE=core,Books,document-insights,meta,inbox,output resources/local-brain-search/run_search.sh "uncertainty" --limit 5 --no-track --json
BRAIN_READ_SCOPE=core,Books,document-insights,meta,inbox,output resources/local-brain-search/run_search.sh "identity" --limit 5 --no-track --json

Pick seed notes from different clusters.

Step 3: Get Connections for Seeds

For each seed note (same wide read-scope - the seeds and their neighbors live across domains):

BRAIN_READ_SCOPE=core,Books,document-insights,meta,inbox,output resources/local-brain-search/run_connections.sh "Note Name" --json

Identify notes with similarity ~0.45-0.70 from DIFFERENT domains (candidate pool - see the calibration correction above).

Step 4: Cross-Domain Analysis

For each cross-domain pair:

1. Read both notes fully
2. Record ACTUAL similarity score from search
3. Analyze for:
   • Shared structural patterns
   • Common mechanisms
   • Meta-principles
   • Paradoxes

Rate conceptual strength (1-5 stars).

Target: Low semantic similarity + high conceptual strength = valuable discovery.

Step 5: Document Discoveries

For each strong connection:

## CROSS-DOMAIN CONNECTION

**Node A**: [[Note X]] (Domain: Neuroscience)
**Node B**: [[Note Y]] (Domain: Economics)
**Semantic Similarity**: 0.63 (actual from search)
**Conceptual Strength**: ⭐⭐⭐⭐⭐

**The Link**: [2-3 sentences explaining WHY they connect]
**Shared Pattern**: [The underlying principle]
**Synthesis Opportunity**: [Potential new note title]

Step 6: Create Dated Changelog

Write to Brain/05-Meta/Changelogs/CHANGELOG - Auto-Discovery Session YYYY-MM-DD.md:

## Auto-Discovery Session: YYYY-MM-DD

### Session Parameters
- Notes sampled: [N] from [X] clusters
- Domains analyzed: [list]

### Discoveries Made
**Strong Connections**: [N]
1. [[A]] ↔ [[B]] - [pattern]

**Meta-Patterns**: [N]
**Consilience Zones**: [N]

### Session Statistics
- Total notes analyzed: [N]
- Non-obvious connections (similarity < 0.70): [N]   ← report the count, but note this is near the median on this index, not the tail

Step 7: Update Master Changelog

Add brief summary to Brain/CHANGELOG.md:

## YYYY-MM-DD - Auto-Discovery Session

See: [[CHANGELOG - Auto-Discovery Session YYYY-MM-DD]]
- [N] connections discovered
- [N] meta-patterns identified

Quality Standards

GOOD discoveries:

• Semantic similarity roughly 0.45-0.70 (a pool, not a grade - see the calibration note above)
• Clear conceptual link across domains
• "Aha!" factor - non-obvious insight
• Actionable synthesis opportunity

SKIP:

• High similarity (0.85+) - usually shared boilerplate (frontmatter/changelog twins), not insight
• Same domain - not cross-domain
• Already linked in vault

Error Handling

Completion Checklist

• Notes sampled from 3+ different clusters
• ACTUAL similarity scores recorded (not estimated)
• Cross-domain connections with conceptual analysis
• Non-obvious discoveries documented (similarity < 0.70) and justified by cross-domain reasoning, not by the score alone
• Dated changelog created in Brain/05-Meta/Changelogs/
• Master changelog updated with summary