Add cross-project pattern registry for retrieval-augmented generalization

- Create patterns/ directory with README, manifest, and 10 initial patterns
  covering Ollama JSON fallback, API escaping, deprecation, PTY auth,
  queue-poll, LLM-as-parser, credential rotation, reverse proxy binding,
  human approval gates, and transient retry.
- Wire pattern loading into architecture/pipeline.js based on task tags.
- Update architecture/orchestrator.js to load patterns and surface them in
  the system prompt.
- Update MEMORY.md, ARCHITECTURE.md, and CONTEXT.md to document the registry
  and record the decision.

patterns/json-escaping-downstream-api.md

@@ -0,0 +1,32 @@
+# Pattern: JSON Escaping for Downstream APIs
+
+## Symptom
+An n8n workflow passes LLM-generated text into a downstream API call, and the request fails with a JSON parse error, malformed payload, or unexpected truncation. The generated text contains quotes, newlines, backslashes, emojis, or control characters that break JSON encoding.
+
+## Affected Projects
+- LinkedIn content automation (LinkedIn REST API posts)
+- AI video generation pipeline (ComfyUI / JSON2Video payloads)
+- Any n8n workflow that injects LLM output into an HTTP Request node body
+
+## Root Cause
+LLMs produce human-readable text; downstream APIs consume machine-readable JSON. Naive string concatenation or weak JSON serialization allows unescaped characters to corrupt the payload. The failure often appears at the receiving API, making root-cause diagnosis slower.
+
+## Standard Fix
+1. Treat LLM output as untrusted string data.
+2. Always serialize it through a proper JSON encoder (`JSON.stringify` in JS, `json.dumps` in Python) before embedding in a payload.
+3. If building a payload string manually, escape quotes, backslashes, newlines, and control characters; better, avoid manual string building entirely.
+4. Add a validation step that parses the final payload with `JSON.parse` before sending.
+5. For n8n, prefer expression mapping through structured fields rather than raw body strings.
+
+## When to Apply
+- Any new integration where LLM-generated content becomes part of an API request body.
+- Any HTTP Request node in n8n that builds a JSON body from expressions containing LLM output.
+
+## Verification
+- Test with adversarial LLM output containing quotes, newlines, unicode, and backslashes.
+- Confirm the receiving API parses the payload correctly.
+- Log payload shape (without secrets) for debugging.
+
+## Related Patterns
+- `ollama-structured-output-fallback`
+- `llm-as-parser-fallback`