duckdb-ies
Layer 4: IES Interactome Analytics with GF(3) Momentum Tracking
Best use case
duckdb-ies is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Layer 4: IES Interactome Analytics with GF(3) Momentum Tracking
Teams using duckdb-ies should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/duckdb-ies/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How duckdb-ies Compares
| Feature / Agent | duckdb-ies | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Layer 4: IES Interactome Analytics with GF(3) Momentum Tracking
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# duckdb-ies
> Layer 4: IES Interactome Analytics with GF(3) Momentum Tracking
**Version**: 2.0.0
**Trit**: +1 (Generative - produces analysis artifacts)
**Bundle**: analytics
**Extends**: duckdb-timetravel
## Overview
DuckDB-IES provides unified interactome analytics across Claude history, GitHub activity, workspace files, and skill manifests. It implements GF(3) momentum tracking, topic clustering, and cross-source fingerprint correlation.
## Database Location
```
/Users/bob/ies/ducklake_data/ies_interactome.duckdb
```
## Core Tables
| Table | Rows | Description |
|-------|------|-------------|
| `claude_history_colored` | 1316+ | Claude interactions with Gay.jl coloring |
| `gh_repos_colored` | 50 | GitHub repos with trit values |
| `gh_contributions` | 366 | Daily contribution counts |
| `skill_manifests` | 1+ | Skill metadata with fingerprints |
| `workspace_files` | 200+ | Workspace file index by type |
| `topic_clusters` | 14 | Content-based topic extraction |
| `skill_dependency_graph` | 5 | Skill domain → file mappings |
## Core Views
### unified_interactions
Merges all sources into single stream:
```sql
SELECT timestamp, source, content, category, fingerprint, color_hex, trit
FROM unified_interactions
WHERE source = 'claude' AND timestamp > '2025-12-20';
```
### gf3_flow_analysis
Daily GF(3) balance tracking:
```sql
SELECT day, total_interactions, daily_gf3_sum, gf3_status, breakdown
FROM gf3_flow_analysis
WHERE gf3_status = '✓ balanced';
```
### gf3_momentum_detector
Hourly drift detection with velocity:
```sql
SELECT hour, cumulative_gf3, gf3_velocity_6h, momentum_status
FROM gf3_momentum_detector
WHERE momentum_status LIKE '%DRIFT%';
```
### fingerprint_correlations
Cross-source co-occurrence within 1-hour windows:
```sql
SELECT edge_type, correlation_count, avg_time_delta
FROM fingerprint_correlations
ORDER BY correlation_count DESC;
```
### interaction_velocity
Hourly momentum with cumulative GF(3):
```sql
SELECT hour, interactions, velocity, cumulative_gf3
FROM interaction_velocity
WHERE velocity > 20; -- High activity spikes
```
### simultaneity_surfaces
High-density interaction periods:
```sql
SELECT hour_bucket, density, gf3_sum, gf3_status, palette
FROM simultaneity_surfaces;
```
## Capabilities
### 1. ingest-claude-history
```sql
CREATE OR REPLACE TABLE claude_history AS
SELECT
display, timestamp,
to_timestamp(timestamp/1000) as ts,
project, sessionId,
CASE
WHEN LOWER(display) LIKE '%duckdb%' THEN 'duckdb'
WHEN LOWER(display) LIKE '%skill%' THEN 'skill'
ELSE 'other'
END as interaction_type
FROM read_json('~/.claude/history.jsonl',
format='newline_delimited',
ignore_errors=true
);
```
### 2. apply-gay-coloring
```sql
-- Add Gay.jl deterministic coloring
CREATE OR REPLACE TABLE claude_history_colored AS
SELECT
*,
hash(display || COALESCE(project,'') || CAST(timestamp AS VARCHAR)) as fingerprint,
'#' || printf('%06x', ABS(hash(display)) % 16777216) as color_hex,
CAST(ABS(hash(display)) % 3 AS INTEGER) - 1 as trit
FROM claude_history;
```
### 3. topic-extraction
```sql
-- Content-based topic clustering via regex
CREATE OR REPLACE TABLE topic_clusters AS
WITH topics AS (
SELECT
content, source,
CASE
WHEN LOWER(content) LIKE '%duckdb%' THEN 'duckdb'
WHEN LOWER(content) LIKE '%gay%' OR LOWER(content) LIKE '%color%' THEN 'gay-coloring'
WHEN LOWER(content) LIKE '%acset%' THEN 'acsets'
WHEN LOWER(content) LIKE '%skill%' THEN 'skills'
WHEN LOWER(content) LIKE '%mcp%' THEN 'mcp'
ELSE 'general'
END as topic,
trit, color_hex, timestamp
FROM unified_interactions
)
SELECT
topic, COUNT(*) as mentions,
SUM(trit) as gf3_sum,
CASE WHEN SUM(trit) % 3 = 0 THEN '✓' ELSE '⚠' END as balanced,
MIN(timestamp) as first_seen,
MAX(timestamp) as last_seen
FROM topics
GROUP BY topic
ORDER BY mentions DESC;
```
### 4. momentum-detection
```sql
-- GF(3) momentum with 6h/24h velocity windows
CREATE OR REPLACE VIEW gf3_momentum_detector AS
WITH cumulative AS (
SELECT
DATE_TRUNC('hour', timestamp) as hour,
SUM(trit) as hourly_trit,
SUM(SUM(trit)) OVER (ORDER BY DATE_TRUNC('hour', timestamp)) as cumulative_gf3
FROM unified_interactions
WHERE timestamp IS NOT NULL
GROUP BY 1
),
with_velocity AS (
SELECT
*,
cumulative_gf3 - LAG(cumulative_gf3, 6) OVER (ORDER BY hour) as gf3_velocity_6h,
cumulative_gf3 - LAG(cumulative_gf3, 24) OVER (ORDER BY hour) as gf3_velocity_24h
FROM cumulative
)
SELECT
hour, hourly_trit, cumulative_gf3,
gf3_velocity_6h, gf3_velocity_24h,
CASE
WHEN ABS(gf3_velocity_6h) > 15 THEN '🔴 HIGH DRIFT'
WHEN ABS(gf3_velocity_6h) > 8 THEN '🟡 MODERATE DRIFT'
WHEN cumulative_gf3 % 3 = 0 THEN '🟢 BALANCED'
ELSE '⚪ STABLE'
END as momentum_status
FROM with_velocity
ORDER BY hour DESC;
```
### 5. parquet-export
```sql
-- Export to Parquet for external analysis
COPY (SELECT * FROM unified_interactions WHERE timestamp IS NOT NULL)
TO 'ducklake_data/parquet/unified_interactions.parquet' (FORMAT PARQUET);
COPY (SELECT * FROM gf3_flow_analysis)
TO 'ducklake_data/parquet/gf3_flow.parquet' (FORMAT PARQUET);
COPY (SELECT * FROM simultaneity_surfaces)
TO 'ducklake_data/parquet/simultaneity_surfaces.parquet' (FORMAT PARQUET);
```
## GF(3) Triad Integration
| Trit | Skill | Role |
|------|-------|------|
| -1 | duckdb-timetravel | Temporal versioning |
| 0 | gay-mcp | Color stream generation |
| +1 | **duckdb-ies** | Interactome analytics |
**Conservation**: (-1) + (0) + (+1) = 0 ✓
## Current Interactome Stats
```
Total Interactions: 1733
Sources: 4 (claude, github_repo, github_contrib, skill)
Global GF(3): 2 (⚠ drift)
Balanced Topics: duckdb, gay-coloring, acsets, crdt, mcp, world-modeling
```
## Topic Distribution
| Topic | Mentions | GF(3) | Status |
|-------|----------|-------|--------|
| general | 1359 | 27 | ✓ balanced |
| gay-coloring | 117 | -6 | ✓ balanced |
| duckdb | 74 | -3 | ✓ balanced |
| skills | 50 | 2 | ⚠ drift |
| world-modeling | 34 | -3 | ✓ balanced |
| mcp | 31 | -9 | ✓ balanced |
| acsets | 20 | 0 | ✓ balanced |
## Parquet Outputs
```
ducklake_data/parquet/
├── unified_interactions.parquet
├── gf3_flow.parquet
└── simultaneity_surfaces.parquet
```
## CLI Recipes
```bash
# Quick interactome status
duckdb /Users/bob/ies/ducklake_data/ies_interactome.duckdb -c "
SELECT source, COUNT(*), SUM(trit) as gf3 FROM unified_interactions GROUP BY source;"
# Check momentum drift
duckdb /Users/bob/ies/ducklake_data/ies_interactome.duckdb -c "
SELECT * FROM gf3_momentum_detector WHERE momentum_status LIKE '%DRIFT%' LIMIT 10;"
# Topic balance check
duckdb /Users/bob/ies/ducklake_data/ies_interactome.duckdb -c "
SELECT topic, mentions, gf3_sum, balanced FROM topic_clusters ORDER BY mentions DESC;"
# Recent high-density hours
duckdb /Users/bob/ies/ducklake_data/ies_interactome.duckdb -c "
SELECT * FROM simultaneity_surfaces ORDER BY density DESC LIMIT 5;"
```
## Related Skills
- `duckdb-timetravel` - Temporal versioning layer
- `gay-mcp` - Deterministic color generation
- `acsets` - Category-theoretic schema
- `entropy-sequencer` - Temporal arrangement
- `bisimulation-game` - Cross-agent skill dispersal
## Scientific Skill Interleaving
This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:
### Dataframes
- **polars** [○] via bicomodule
- High-performance dataframes
### Bibliography References
- `general`: 734 citations in bib.duckdb
## Cat# Integration
This skill maps to **Cat# = Comod(P)** as a bicomodule in the equipment structure:
```
Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826
```
### GF(3) Naturality
The skill participates in triads satisfying:
```
(-1) + (0) + (+1) ≡ 0 (mod 3)
```
This ensures compositional coherence in the Cat# equipment structure.
## Forward Reference
- unified-reafference (IES session unification)Related Skills
fswatch-duckdb
FileSystemWatcher over /tmp with DuckDB/DuckLake persistence. Auto-starts on Amp sessions for resilient file monitoring with temporal queries.
duckdb-timetravel
Layer 3: Temporal Versioning and ACSet Schema Generation for DuckDB
duckdb-quadruple-interleave
Chaotic interleaving across local DuckDB databases modeled as coupled quadruple pendula. Random walks both BETWEEN databases and WITHIN tables for context injection.
duckdb-temporal-versioning
Temporal versioning and interaction history with time-travel queries, causality tracking, and deterministic replay
DuckDB Spatial Skill
H3 hexagonal indexing, PostGIS-compatible spatial queries, and geographic analysis with GF(3) coloring.
zx-calculus
Coecke's ZX-calculus for quantum circuit reasoning via string diagrams with Z-spiders (green) and X-spiders (red)
zulip-cogen
Zulip Cogen Skill 🐸⚡
zls-integration
zls-integration skill
zig
zig skill
zig-syrup-bci
Multimodal BCI pipeline in Zig: DSI-24 EEG, fNIRS mBLL, eye tracking IVT, LSL sync, EDF read/write, GF(3) conservation
zig-programming
zig-programming skill
zeroth-bot
Zeroth Bot - 3D-printed open-source humanoid robot platform for sim-to-real and RL research. Affordable entry point for humanoid robotics.