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/workers-ai/patterns.md

@@ -0,0 +1,120 @@
+# Workers AI Patterns
+
+## RAG (Retrieval-Augmented Generation)
+
+```typescript
+// 1. Embed query
+const embedding = await env.AI.run('@cf/baai/bge-base-en-v1.5', { text: query });
+
+// 2. Search vectors
+const results = await env.VECTORIZE.query(embedding.data[0], {
+  topK: 5, returnMetadata: true
+});
+
+// 3. Build context
+const context = results.matches.map(m => m.metadata?.text).join('\n\n');
+
+// 4. Generate with context
+const response = await env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
+  messages: [
+    { role: 'system', content: `Answer based on:\n\n${context}` },
+    { role: 'user', content: query }
+  ]
+});
+```
+
+## Streaming (SSE)
+
+```typescript
+const stream = await env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
+  messages, stream: true
+});
+
+const { readable, writable } = new TransformStream();
+const writer = writable.getWriter();
+
+(async () => {
+  for await (const chunk of stream) {
+    await writer.write(new TextEncoder().encode(`data: ${JSON.stringify(chunk)}\n\n`));
+  }
+  await writer.write(new TextEncoder().encode('data: [DONE]\n\n'));
+  await writer.close();
+})();
+
+return new Response(readable, {
+  headers: { 'Content-Type': 'text/event-stream' }
+});
+```
+
+## Error Handling & Retry
+
+```typescript
+async function runWithRetry(env, model, input, maxRetries = 3) {
+  for (let attempt = 0; attempt < maxRetries; attempt++) {
+    try {
+      return await env.AI.run(model, input);
+    } catch (error) {
+      if (error.message?.includes('7505') && attempt < maxRetries - 1) {
+        await new Promise(r => setTimeout(r, Math.pow(2, attempt) * 1000));
+        continue;
+      }
+      throw error;
+    }
+  }
+}
+```
+
+## Model Fallback
+
+```typescript
+try {
+  return await env.AI.run('@cf/meta/llama-3.1-70b-instruct', { messages });
+} catch {
+  return await env.AI.run('@cf/meta/llama-3.1-8b-instruct', { messages });
+}
+```
+
+## Prompt Patterns
+
+```typescript
+// System prompts
+const PROMPTS = {
+  json: 'Respond with valid JSON only.',
+  concise: 'Keep responses brief.',
+  cot: 'Think step by step before answering.'
+};
+
+// Few-shot
+messages: [
+  { role: 'system', content: 'Extract as JSON' },
+  { role: 'user', content: 'John bought 3 apples for $5' },
+  { role: 'assistant', content: '{"name":"John","item":"apples","qty":3}' },
+  { role: 'user', content: actualInput }
+]
+```
+
+## Parallel Execution
+
+```typescript
+const [sentiment, summary, embedding] = await Promise.all([
+  env.AI.run('@cf/mistral/mistral-7b-instruct-v0.1', { messages: sentimentPrompt }),
+  env.AI.run('@cf/meta/llama-3.1-8b-instruct', { messages: summaryPrompt }),
+  env.AI.run('@cf/baai/bge-base-en-v1.5', { text })
+]);
+```
+
+## Cost Optimization
+
+| Task | Model | Neurons |
+|------|-------|---------|
+| Classify | `@cf/mistral/mistral-7b-instruct-v0.1` | ~50 |
+| Chat | `@cf/meta/llama-3.1-8b-instruct` | ~200 |
+| Complex | `@cf/meta/llama-3.1-70b-instruct` | ~2000 |
+| Embed | `@cf/baai/bge-base-en-v1.5` | ~10 |
+
+```typescript
+// Batch embeddings
+const response = await env.AI.run('@cf/baai/bge-base-en-v1.5', {
+  text: textsArray // Process multiple at once
+});
+```