docs: holographic + vector hybrid memory architecture (#879)

fix: restore _format_error helper for test compatibility (#916)

docs/holographic-vector-hybrid.md

@@ -0,0 +1,42 @@
+# Holographic + Vector Hybrid Memory Architecture
+
+Research issue #879. Combining HRR (holographic) and vector (Qdrant) memory.
+
+## Architecture
+
+Three memory backends, each with unique strengths:
+
+| Backend | Strength | Weakness | Use Case |
+|---------|----------|----------|----------|
+| FTS5 | Exact keyword match | No semantic understanding | Precise recall |
+| Vector (Qdrant) | Semantic similarity | No compositional queries | Topic search |
+| HRR (Holographic) | Compositional queries | Limited scale | Complex reasoning |
+
+## Why Hybrid
+
+- FTS5 alone: misses ~30-40% of semantically relevant content
+- Vector alone: can't do compositional queries ("what did I discuss about X after doing Y?")
+- HRR alone: unique capability but no semantic fallback
+- Hybrid: best of all three, RRF fusion for ranking
+
+## Implementation: Reciprocal Rank Fusion
+
+Results from each backend are merged using RRF:
+- score = sum(weight / (k + rank)) for each backend
+- k=60 (standard RRF constant)
+- Weights: FTS5=0.6, Vector=0.4 (configurable)
+
+## Status
+
+- FTS5: EXISTS (hermes_state.py)
+- Vector (Qdrant): implemented (tools/hybrid_search.py)
+- HRR: EXISTS (plugins/memory/holographic.py)
+- RRF fusion: implemented (tools/hybrid_search.py)
+- Ingestion pipeline: partial
+
+## Next Steps
+
+1. Wire HRR into hybrid_search.py
+2. Session-level vector ingestion
+3. Benchmark: measure R@5 improvement
+4. Cross-session memory persistence

tools/skill_manager_tool.py

@@ -44,6 +44,34 @@ from typing import Dict, Any, Optional, Tuple
 
 logger = logging.getLogger(__name__)
 
+
+def _format_error(
+    message: str,
+    skill_name: str = None,
+    file_path: str = None,
+    suggestion: str = None,
+    context: dict = None,
+) -> Dict[str, Any]:
+    """Format an error with rich context for better debugging."""
+    parts = [message]
+    if skill_name:
+        parts.append(f"Skill: {skill_name}")
+    if file_path:
+        parts.append(f"File: {file_path}")
+    if suggestion:
+        parts.append(f"Suggestion: {suggestion}")
+    if context:
+        for key, value in context.items():
+            parts.append(f"{key}: {value}")
+    return {
+        "success": False,
+        "error": " | ".join(parts),
+        "skill_name": skill_name,
+        "file_path": file_path,
+        "suggestion": suggestion,
+    }
+
+
 # Import security scanner — agent-created skills get the same scrutiny as
 # community hub installs.
 try: