shell: fish functions -> ~/.local/bin executables + bash/zsh ports; manage ~/.agents

- 16 cross-shell utilities converted to sh/bash chezmoi executables:
  ai c2p cp-appid extract_agents fab gdc gh-sonr-org-migrate ghg-clone
  ghn gho git-summary launch_omp_livediff_overlay lc-elir mdcp mdp pj
  (ghg-clone not named ghg: unrelated cargo binary owns that name)
- cwd/history helpers stay per-shell: yy c cd clip-last + ghg/ghn/gho
  cd-wrappers in bash/functions/ and zsh/functions/, loaded by rc files
- fish keeps only shell-state functions; converted ones now hit PATH
- cp-appid ignore gating moved to .local/bin path
- dot_agents/skills: 82 skills materialized from dotstate storage so
  dot_claude skill symlinks resolve on fresh bootstrap; dotstate
  profile/symlinks.json released .agents/skills .bashrc .zshrc

dot_agents/skills/cloudflare/references/vectorize/patterns.md

@@ -0,0 +1,90 @@
+# Vectorize Patterns
+
+## Workers AI Integration
+
+```typescript
+// Generate embedding + query
+const result = await env.AI.run("@cf/baai/bge-base-en-v1.5", { text: [query] });
+const matches = await env.VECTORIZE.query(result.data[0], { topK: 5 }); // Pass data[0]!
+```
+
+| Model | Dimensions |
+|-------|------------|
+| `@cf/baai/bge-small-en-v1.5` | 384 |
+| `@cf/baai/bge-base-en-v1.5` | 768 (recommended) |
+| `@cf/baai/bge-large-en-v1.5` | 1024 |
+
+## OpenAI Integration
+
+```typescript
+const response = await openai.embeddings.create({ model: "text-embedding-ada-002", input: query });
+const matches = await env.VECTORIZE.query(response.data[0].embedding, { topK: 5 });
+```
+
+## RAG Pattern
+
+```typescript
+// 1. Embed query
+const emb = await env.AI.run("@cf/baai/bge-base-en-v1.5", { text: [query] });
+
+// 2. Search vectors
+const matches = await env.VECTORIZE.query(emb.data[0], { topK: 5, returnMetadata: "indexed" });
+
+// 3. Fetch full docs from R2/D1/KV
+const docs = await Promise.all(matches.matches.map(m => env.R2.get(m.metadata.key).then(o => o?.text())));
+
+// 4. Generate with context
+const answer = await env.AI.run("@cf/meta/llama-3-8b-instruct", {
+  prompt: `Context:\n${docs.filter(Boolean).join("\n\n")}\n\nQuestion: ${query}\n\nAnswer:`
+});
+```
+
+## Multi-Tenant
+
+### Namespaces (< 50K tenants, fastest)
+
+```typescript
+await env.VECTORIZE.upsert([{ id: "1", values: emb, namespace: `tenant-${id}` }]);
+await env.VECTORIZE.query(vec, { namespace: `tenant-${id}`, topK: 10 });
+```
+
+### Metadata Filter (> 50K tenants)
+
+```bash
+wrangler vectorize create-metadata-index my-index --property-name=tenantId --type=string
+```
+
+```typescript
+await env.VECTORIZE.upsert([{ id: "1", values: emb, metadata: { tenantId: id } }]);
+await env.VECTORIZE.query(vec, { filter: { tenantId: id }, topK: 10 });
+```
+
+## Hybrid Search
+
+```typescript
+const matches = await env.VECTORIZE.query(vec, {
+  topK: 20,
+  filter: {
+    category: { $in: ["tech", "science"] },
+    published: { $gte: lastMonthTimestamp }
+  }
+});
+```
+
+## Batch Ingestion
+
+```typescript
+const BATCH = 500;
+for (let i = 0; i < vectors.length; i += BATCH) {
+  await env.VECTORIZE.upsert(vectors.slice(i, i + BATCH));
+}
+```
+
+## Best Practices
+
+1. **Pass `data[0]`** not `data` or full response
+2. **Batch 500** vectors per upsert
+3. **Create metadata indexes** before inserting
+4. **Use namespaces** for tenant isolation (faster than filters)
+5. **`returnMetadata: "indexed"`** for best speed/data balance
+6. **Handle 5-10s mutation delay** in async operations