# Bigquery Asi Interleave

> Interleave layer bridging the BigQuery cluster to plurigrid/asi. Routes BigQuery queries through asi's DuckDB stack, wires patent search into asi knowledge graph, connects Looker Studio dashboards to CatColab, and feeds BigQuery ML into the lolita physics emulation pipeline.

- Skill: `plurigrid/bigquery-asi-interleave` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add plurigrid/bigquery-asi-interleave`
- Raw SKILL.md: https://api.skillmd.com/api/skills/plurigrid/bigquery-asi-interleave/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: plurigrid (https://skillmd.com/u/plurigrid)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/plurigrid/bigquery-asi-interleave

---


# BigQuery × ASI Interleave

Bridge layer connecting the 6-skill BigQuery cluster to plurigrid/asi's 1360+ skill graph.

## Skill Cluster Map

```
bigquery (trit:0, comprehensive)          ← hub: bq CLI, GoogleSQL, ML, governance
  ├── bigquery-table-creator (-1)          ← infra: DDL, partitioned/clustered tables
  ├── restricted-bigquery-dbt-environment (-1) ← safety: dbt test schema guard
  ├── bigquery-patent-search (0)           ← bridge: 76M+ patent corpus via BQ public data
  ├── looker-studio-bigquery (0)           ← bridge: Looker Studio dashboards
  └── bigquery-table-creator (+1)          ← orchestration: GCP table lifecycle
```

## GF(3) Tripartite

`bigquery-table-creator(-1) ⊗ bigquery-asi-interleave(0) ⊗ looker-studio-bigquery(+1) = 0`

Infrastructure DDL (-1) × Bridge (0) × Visualization (+1) = balanced data stack.

## ASI Integration Points

### 1. BigQuery ↔ DuckDB — Cloud/Local Hybrid Query

asi already has rich DuckDB: `duckdb-ies`, `duckdb-spatial`, `duckdb-quadruple-interleave`,
`duckdb-timetravel`, `duckdb-temporal-versioning`.

BigQuery is the cloud-scale complement:

```bash
# Export BQ → DuckDB for local analysis
bq extract --destination_format=PARQUET \
  'project:dataset.table' gs://bucket/export/*.parquet

# Load into DuckDB for local temporal analysis
duckdb asi.db << 'EOF'
INSTALL httpfs; LOAD httpfs;
CREATE TABLE bq_export AS
  SELECT * FROM read_parquet('gs://bucket/export/*.parquet');
-- Now apply duckdb-timetravel patterns locally
EOF
```

```bash
# Inverse: push DuckDB results to BigQuery
duckdb asi.db -c "COPY (SELECT * FROM skill_graph) TO '/tmp/skills.parquet' (FORMAT PARQUET)"
bq load --source_format=PARQUET project:asi_dataset.skill_graph /tmp/skills.parquet
```

Pattern: BQ = warehouse (PB scale), DuckDB = analytical engine (GB scale), asi = skill graph on top.

### 2. Patent Search → ASI Knowledge Graph

`bigquery-patent-search` queries `patents-public-data.patents` (76M+ patents):

```python
# Search for prior art on asi's core concepts
from python.bigquery_search import BigQueryPatentSearch
searcher = BigQueryPatentSearch()

# GF(3) / ternary computing patents
gf3_patents = searcher.search_patents(
    query="ternary logic GF(3) color semantics",
    cpc_prefix="G06F",  # Computing
    start_year=2010
)

# OCapN / capability-secure networking
ocapn_patents = searcher.search_patents(
    query="object capability network distributed computing",
    cpc_prefix="H04L",  # Digital communication
)

# Latent diffusion physics emulation (lolita)
lolita_priors = searcher.search_patents(
    query="latent diffusion physics simulation neural operator",
    cpc_prefix="G06N",  # ML/neural
    start_year=2020
)
```

Wire results into `openalex-database` + `hatchery-papers` for full prior art graph.

### 3. BigQuery ML → Lolita Physics Pipeline

BigQuery ML complements the Vertex AI pipeline (lolita, task#23):

```sql
-- Train a forecasting model on attractor time series (dysts corpus)
CREATE OR REPLACE MODEL `asi_project.physics.attractor_forecast`
OPTIONS (
  model_type = 'ARIMA_PLUS',
  time_series_timestamp_col = 'timestep',
  time_series_data_col = 'value',
  time_series_id_col = 'attractor_name',
  auto_arima = TRUE,
  data_frequency = 'AUTO_FREQUENCY'
) AS
SELECT timestep, value, attractor_name
FROM `asi_project.physics.dysts_trajectories`;

-- Predict next 100 steps
SELECT *
FROM ML.FORECAST(
  MODEL `asi_project.physics.attractor_forecast`,
  STRUCT(100 AS horizon, 0.9 AS confidence_level)
);
```

Route predictions back to `lolita` (latent diffusion) as warm-start priors.

### 4. Looker Studio → CatColab Dashboard

`looker-studio-bigquery` + `catcolab-stock-flow` + `catcolab-causal-loop`:

F-pattern dashboard for asi skill graph health:

```sql
-- Skill graph daily metrics (feeds Looker Studio)
CREATE OR REPLACE TABLE `asi_project.dashboard.skill_metrics` AS
SELECT
  CURRENT_DATE() as report_date,
  COUNT(*) as total_skills,
  COUNTIF(trit = -1) as negative_skills,
  COUNTIF(trit = 0) as neutral_skills,
  COUNTIF(trit = 1) as positive_skills,
  -- MONOTONIC_SKILL_INVARIANT
  CASE WHEN COUNT(*) >= 1360 THEN TRUE ELSE FALSE END as invariant_holds
FROM `asi_project.skills.registry`;
```

KPI tiles: total skills (≥1360), GF(3) trit distribution, hub reachability (17 hubs).

### 5. dbt Safety → ASI Skill Safety

`restricted-bigquery-dbt-environment` pattern applied to asi skill writes:

```sql
-- SAFE: Always write to test schema first
{{ config(
    schema='asi_test',  -- <- ALWAYS during development
    materialized='incremental',
    unique_key='skill_name'
) }}

SELECT * FROM {{ ref('skill_candidates') }}
WHERE validated = TRUE
  AND trit_balance = 0  -- GF(3) invariant
```

Rule: **NEVER commit skill writes without `schema='asi_test'` removed**.
Run `git diff` before push to verify MONOTONIC_SKILL_INVARIANT preserved.

### 6. Skill Prior Art Search — asi × uspto-database

Connect `bigquery-patent-search` to `uspto-database` for full prior art:

```python
# Find patent landscape around key asi concepts
concepts = [
    ("topological chemputer CRN", "C07", "Chemical reactions"),
    ("distributed capability object coloring", "H04L", "Networks"),
    ("GF(3) ternary neural network", "G06N", "ML"),
    ("category theory compositional game", "G06F", "Computing"),
    ("latent diffusion physics operator", "G06N", "ML"),
]

for query, cpc, domain in concepts:
    results = searcher.search_patents(query=query, cpc_prefix=cpc, limit=5)
    print(f"\n=== {domain}: {query} ===")
    for r in results:
        print(f"  {r['publication_number']}: {r['title'][:60]}")
```

Use results to identify white space for asi's novel contributions.

## Security Notes

- `restricted-bigquery-dbt-environment`: NEVER run `dbt run` without `schema='test'`
- All BQ queries: use `--dry_run` to estimate cost before large scans
- IAM: `bigquery.dataViewer` minimum; `bigquery.jobUser` for queries
- Patent data is public — no auth needed for `patents-public-data.*`
- Free tier: 1TB/month queries free; ~20,000 patent searches/month free

## Related ASI Skills

- `duckdb-ies` / `duckdb-quadruple-interleave` — local DuckDB complement to BQ warehouse
- `lolita` / task#23 — physics emulation pipeline fed by BQML forecasting
- `bigquery-patent-search` → `uspto-database` + `openalex-database` + `hatchery-papers`
- `catcolab-stock-flow` / `catcolab-causal-loop` — Looker Studio → CatColab olog export
- `vertex-asi-interleave` — parent GCP interleave (BigQuery lives inside the same GCP project)
- `wolframite-compass` — Wolfram data alongside BigQuery public datasets
- `restricted-bigquery-dbt-environment` → model safety pattern for all asi data writes