# Vertex Asi Interleave

> Interleave layer between Google Vertex AI skills and plurigrid/asi capabilities. Routes Vertex API calls through the asi skill graph, GF(3)-colors model endpoints, and wires Gemini/Imagen/Pipelines into asi's MCP federation, abductive reasoning, and physics emulation stack.

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

---


# Vertex × ASI Interleave

Bridge layer connecting the 7-skill Vertex AI cluster to plurigrid/asi's 1360+ skill graph.

## Skill Cluster Map

```
vertex-ai (trit:0, ERGODIC)          ← hub: gcloud OAuth2, core curl patterns
  ├── vertex-ai-endpoint-config (-1)  ← infra: endpoint CRUD
  ├── vertex-ai-deployer (-1)         ← infra: model → endpoint promotion
  ├── firebase-vertex-ai (0)          ← bridge: Firebase + Gemini + Firestore RAG
  ├── vertex-engine-inspector (0)     ← bridge: Agent Engine validation + A2A
  ├── vertex-ai-pipeline-creator (+1) ← orchestration: KFP pipelines
  └── vertex-ai-media-master (+1)     ← orchestration: multimodal media ops
```

## ASI Integration Points

### 1. Abductive Reasoning → Gemini
Wire `abductive-monte-carlo` + `abductive-repl` to Gemini as the LLM oracle:

```bash
# Gemini as hypothesis prior for MCMC
vertex_gemini() {
  local prompt="$1"
  local token=$(gcloud auth print-access-token)
  local project=$(gcloud config get project 2>/dev/null)
  curl -s "https://us-central1-aiplatform.googleapis.com/v1/projects/${project}/locations/us-central1/publishers/google/models/gemini-2.0-flash:generateContent" \
    -H "Authorization: Bearer $token" \
    -H "Content-Type: application/json" \
    -d "{\"contents\":[{\"role\":\"user\",\"parts\":[{\"text\":$(echo "$prompt" | jq -Rs .)}]}]}" \
    | jq -r '.candidates[0].content.parts[0].text'
}

# GF(3) trit-colored hypothesis: -1=reject, 0=suspend, +1=accept
hypothesis_trit() {
  local h="$1"
  local verdict=$(vertex_gemini "Rate this hypothesis {-1=false,0=uncertain,+1=true}: $h")
  echo "$verdict"
}
```

### 2. Lolita Physics Emulation → Vertex AI Pipelines
`vertex-ai-pipeline-creator` + `lolita` (NeurIPS 2025, arxiv:2507.02608):

KFP pipeline template for latent diffusion physics emulation:
- Component 1: `train_ae` — DCAE autoencoder (lat_channels=64)
- Component 2: `cache_latents` — encode dataset → latent trajectories on Ceph/GCS
- Component 3: `train_diffusion` — ViT-based diffusion on cached latents
- Component 4: `eval` — rollout evaluation on test set
- Datasets: Euler, Rayleigh-Bénard, Turbulence Gravity Cooling (from The Well)

```python
# Vertex AI Pipeline for lolita physics emulation
from kfp import dsl

@dsl.pipeline(name="lolita-physics-emulation")
def lolita_pipeline(dataset: str = "rayleigh_benard", lat_channels: int = 64):
    ae = dsl.ContainerOp(
        name="train-autoencoder",
        image="gcr.io/PROJECT/lolita:latest",
        command=["python", "train_ae.py"],
        arguments=["--dataset", dataset, "--lat_channels", str(lat_channels)]
    )
    cache = dsl.ContainerOp(
        name="cache-latents",
        image="gcr.io/PROJECT/lolita:latest",
        command=["python", "cache_latents.py"],
        arguments=["--dataset", dataset, "--run", ae.outputs["run_dir"]]
    ).after(ae)
    diff = dsl.ContainerOp(
        name="train-diffusion",
        image="gcr.io/PROJECT/lolita:latest",
        command=["python", "train_diffusion.py"],
        arguments=["--dataset", dataset, "--ae_run", ae.outputs["run_dir"]]
    ).after(cache)
```

### 3. Agent Engine → ASI Skill Routing
`vertex-engine-inspector` validates Agent Engine deployments. Wire to asi skill graph:

Inspection checklist (A2A protocol + asi invariants):
- [ ] Code Execution Sandbox isolated
- [ ] Memory Bank TTL set (align with game history TTL)
- [ ] A2A protocol compliance verified
- [ ] Security posture: auth_token gate present
- [ ] Skill routing: every agent call traces a GF(3) tripartite path
- [ ] MONOTONIC_SKILL_INVARIANT: agent cannot delete skills (≥1360)

```bash
# Inspect a deployed Agent Engine + score against asi invariants
inspect_agent_engine() {
  local endpoint="$1"
  local token=$(gcloud auth print-access-token)
  local project=$(gcloud config get project)

  # Get deployment status
  gcloud ai endpoints describe "$endpoint" --region=us-central1

  # Validate A2A
  curl -s "https://us-central1-aiplatform.googleapis.com/v1/projects/${project}/locations/us-central1/agents/${endpoint}:validateA2A" \
    -H "Authorization: Bearer $token" | jq '.complianceScore'
}
```

### 4. Firebase + Firestore → ASI Skill RAG
`firebase-vertex-ai` powers a RAG layer over the 1360 asi skills:

```javascript
// Cloud Function: skill retrieval via Firestore + Gemini embeddings
const {VertexAI} = require('@google-cloud/vertexai');
const admin = require('firebase-admin');

const vertex = new VertexAI({project: process.env.GCP_PROJECT, location: 'us-central1'});

exports.skillSearch = functions.https.onCall(async (query) => {
  // Embed query
  const embeddingModel = vertex.getGenerativeModel({model: 'text-embedding-005'});
  const embedding = await embeddingModel.embedContent(query);

  // Search Firestore skill index (cosine similarity)
  const skills = await admin.firestore()
    .collection('asi-skills')
    .orderBy('embedding', 'NEAREST', {distanceMeasure: 'COSINE', queryVector: embedding.values})
    .limit(5)
    .get();

  return skills.docs.map(d => ({name: d.id, trit: d.data().trit, description: d.data().description}));
});
```

### 5. Imagen → Gay.jl Visual Authentication
`vertex-ai-media-master` + `gay-tofu` + Gay.jl:

Generate TOFU-authenticated images where pixel colors encode GF(3) capability class:

```bash
# Generate image → extract dominant colors → map to GF(3) trits
imagen_gay() {
  local prompt="$1"
  local token=$(gcloud auth print-access-token)
  local project=$(gcloud config get project)

  # Generate with Imagen 3
  curl -s "https://us-central1-aiplatform.googleapis.com/v1/projects/${project}/locations/us-central1/publishers/google/models/imagen-3.0-generate-002:predict" \
    -H "Authorization: Bearer $token" \
    -H "Content-Type: application/json" \
    -d "{\"instances\":[{\"prompt\":\"$prompt\"}],\"parameters\":{\"sampleCount\":1}}" \
    | jq -r '.predictions[0].bytesBase64Encoded' | base64 -d > /tmp/imagen_out.png

  echo "Image written to /tmp/imagen_out.png"
  echo "GF(3) color fingerprint: $(julia -e 'using Gay; println(colorize("/tmp/imagen_out.png"))')"
}
```

## GF(3) Tripartite Tag

`vertex-ai-endpoint-config(-1) ⊗ vertex-asi-interleave(0) ⊗ vertex-ai-pipeline-creator(+1) = 0`

Infrastructure (-1) × Bridge (0) × Orchestration (+1) = balanced capability.

## Security Notes

- `vertex-ai-pipeline-creator`: Gen flagged **High Risk** — review before production use
- `vertex-engine-inspector`: Gen flagged **Med Risk** — inspect Agent Engine output carefully
- All Vertex calls require OAuth2 bearer tokens (60min TTL) — never use API keys
- Firebase functions: secrets via Secret Manager only, never in client bundles

## Related ASI Skills

- `abductive-monte-carlo` — MCMC hypothesis sampling (feeds Gemini as oracle)
- `lolita` / task#23 — physics emulation pipeline target
- `agent-o-rama` — Clojure agent routing (receives Vertex Agent Engine outputs)
- `gay-tofu` — TOFU visual auth (Imagen output verification)
- `gay-monte-carlo` — GF(3) colored sampling (pairs with Gemini generation)
- `mcp-tripartite` — MCP federation hub (Vertex as one spoke)
- `firebase-vertex-ai` — Firebase/Firestore RAG layer