Ducklake Semantic Analyzer

Version: 1.0.0 Status: Production Ready Created: 2025-12-21 Total Mentions: 255

Overview

Loads semantic analysis from Subagent 2 (Query Analyzer) and provides functions for intent classification, semantic clustering, and co-occurrence analysis across 45 files.

Purpose

Enable semantic understanding of ducklake mentions:

• Intent classification (reference, documentation, implementation, testing)
• Semantic cluster detection (technical, color-based, parallel, testing, data)
• Keyword co-occurrence analysis
• Context window extraction

Data Sources

• Primary:

  /Users/bob/ies/ducklake_semantic_analysis_2025-12-21.json

• Coverage: 45 files, 255 mentions
• Languages: Markdown (28.9%), Julia (22.2%), Hy (11.1%), Python, Rust, Swift, SQL

Functions

classify_intent(mention: str) -> str

Classify semantic intent of a mention.

intent = classify_intent("ducklake temporal query optimization")
# Returns: "implementation"

intent = classify_intent("see ducklake schema documentation")
# Returns: "reference"

Categories:

• reference

  (45.5%) - Passive mentions

• documentation

  (17.6%) - Formal documentation

• implementation

  (5.9%) - Active code

• testing

  (6.7%) - Tests and validation

• query_discussion

  (9.8%) - SQL query discussions

Implementation:

import json
import re

INTENT_PATTERNS = {
    "implementation": r"(implement|create|build|optimize|develop)",
    "documentation": r"(document|explain|describe|guide|reference)",
    "testing": r"(test|verify|validate|check|assert)",
    "query_discussion": r"(query|select|from|where|sql)",
    "reference": r".*"  # Default
}

def classify_intent(mention: str) -> str:
    mention_lower = mention.lower()
    for intent, pattern in INTENT_PATTERNS.items():
        if re.search(pattern, mention_lower):
            return intent
    return "reference"

find_clusters(keyword: str) -> list

Find semantic clusters containing keyword.

clusters = find_clusters("color")
# Returns: [
#   {"cluster": "color_based_identity", "strength": "high", "count": 83},
#   {"cluster": "parallel_processing", "strength": "medium", "count": 34}
# ]

Available Clusters:

1. technical_architecture (102 mentions)
   • Keywords: duckdb, lake, temporal, versioning, sql, table
2. color_based_identity (83 mentions)
   • Keywords: color, gay, seed, retromap, deterministic, spi
3. parallel_processing (61 mentions)
   • Keywords: parallel, thread, integration, acset
4. testing_validation (40 mentions)
   • Keywords: test, verify, analysis
5. data_integration (43 mentions)
   • Keywords: data, parquet, integration, world

compute_cooccurrence(term1: str, term2: str) -> dict

Compute co-occurrence relationship strength.

result = compute_cooccurrence("duckdb", "lake")
# Returns: {
#   "cooccurrence": 100,
#   "significance": "DuckLake is fundamentally a DuckDB-based system",
#   "mentions": 255
# }

High Co-occurrence Pairs:

• lake

  +

  duckdb

  : 100% (always together)

• color

  +

  gay

  : 62% (color via GAY seed)

• temporal

  +

  versioning

  : 28%

• parallel

  +

  thread

  : 34%

• seed

  +

  deterministic

  : 36%

extract_context_window(mention: str, lines: int = 5) -> str

Extract surrounding context for a mention.

context = extract_context_window("ducklake temporal analysis", lines=3)
# Returns multi-line string with context before and after

Usage Example

from skills.ducklake_semantic_analyzer import *

# Find all implementation mentions
impl_files = []
with open("/Users/bob/ies/ducklake_semantic_analysis_2025-12-21.json") as f:
    data = json.load(f)
    for file_path, mentions in scan_all_files():
        for mention in mentions:
            if classify_intent(mention) == "implementation":
                impl_files.append(file_path)

print(f"Implementation files: {len(set(impl_files))}")

# Find color-related clusters
color_clusters = find_clusters("color")
for cluster in color_clusters:
    print(f"{cluster['cluster']}: {cluster['count']} mentions ({cluster['strength']})")

# Check keyword relationships
pairs = [("duckdb", "lake"), ("color", "gay"), ("temporal", "versioning")]
for term1, term2 in pairs:
    result = compute_cooccurrence(term1, term2)
    print(f"{term1} + {term2}: {result['cooccurrence']}%")

Skills Dependencies

• code-review (pattern analysis)
• llm-application-dev (semantic understanding)
• frontend-design (visualization patterns)

Integration Points

• Temporal Introspection: Combine intent with temporal clustering
• Pattern Expansion: Use semantic clusters for progressive discovery
• Categorical Model: Map intents to ACSet attributes

Key Statistics

• Total files: 45
• Total mentions: 255
• Top keyword: 'lake' (255 occurrences)
• DuckDB references: 102
• Color keywords: 83
• Temporal keywords: 28
• ACSet keywords: 27
• Documentation: 45% of mentions

Hotspot Files

1. gay_ducklake.jl

   - 29 mentions (main implementation)

2. DUCKDB_HISTORY_ANALYSIS.txt

   - 26 mentions (historical analysis)

3. rio/Gay.jl/src/gay_pliny_krep.jl

   - 23 mentions (Pliny integration)

4. rio/Gay.jl/worlds/hatchery/pliny_acset_parallel.jl

   - 19 mentions (parallel ACSet)

5. hatchery_repos/bmorphism__bafishka/src/geo_game/time_travel.rs

   - 14 mentions (Rust time-travel)

Architectural Patterns

Reafferent Detection

• Self-recognition through color identity matching
• Formula:

  color(seed) ⊻ color(observation) → recognition

• Canonical seed: 1069, iterations: 1069

Contemporaneous Timeslices

• Temporal database slicing for parallel history analysis
• Components: interactions, amp_threads, timeslices
• GF3 tracking: Red/Yellow/Blue balanced ternary polarity

Color Retromap

• Retroactive temporal color mapping to battery cycle states
• Technology: Hy language with DuckDB backend
• Purpose: Assign interactions to color slices for temporal analysis

GF(3) Distribution

This skill operates in the YELLOW (GF3=1) structural category:

• 38.9% of mentions
• Focus: Semantic relationships, classification, clustering

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Skill Type: Semantic Analysis Color: YELLOW Polarity: GF(3) = 1 Access Pattern: Read-only analysis