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+---
+name: save-q-memory
+description: "Manual save to Qdrant memories collection with enforced 10-type schema. Load this skill EVERY time the user says 'save to memory' or 'save to qdrant' — it is the ONLY path for manual Qdrant writes."
+version: 1.0.0
+author: Hermes Agent
+license: MIT
+metadata:
+  hermes:
+    tags: [qdrant, memories, save, schema, manual]
+    related_skills: [qdrant-collection-management]
+---
+
+# Save Q Memory — Manual Write Enforcer
+
+This skill is the ONLY path for manual Qdrant writes. Load it every time the user says "save to memory" or "save to qdrant." No write bypasses this skill.
+
+## Trigger
+
+User says: "save to memory", "save to qdrant", "save this to memories", or any variant directing a manual Qdrant write.
+
+## Prerequisites
+
+- Qdrant at `http://10.0.0.22:6333`
+- Ollama at `http://localhost:11434` with `snowflake-arctic-embed2:latest`
+- Collection: `memories` (1024-dim, Cosine)
+
+## Mandatory Save Procedure
+
+### Step 1: Classify the Content
+
+Before embedding, classify EVERY item:
+
+**`type`** — pick ONE of 10:
+- `fact` — timeless/slow-changing world truth
+- `infrastructure` — hosts, IPs, services, configs, topology
+- `entity` — person, org, project, tool identity
+- `preference` — user's stable likes/dislikes, style, defaults
+- `procedure` — how-to: steps, commands, runbooks, fixes
+- `decision` — chosen approach + rationale (the why)
+- `observation` — consolidated pattern from many episodes (2nd-order)
+- `reference` — external KB/docs, URLs, citations
+- `event` — something that happened at a time; state change
+- `experience` — raw conversation turn / interaction log
+
+**`mem_class`** — pick ONE:
+- `semantic` — for fact, infrastructure, entity, preference, decision, observation, reference
+- `procedural` — for procedure
+- `episodic` — for event, experience
+
+**`entities`** — list of people, servers, IPs, tools, projects, models mentioned. Empty array `[]` if none.
+
+**`tags`** — 2-3 lowercase category tags for filtering. Examples: `["infrastructure", "dgx"]`, `["preference", "output_style"]`.
+
+**`importance`** — 0.0-1.0:
+- 0.9+: critical (court docs, credentials, core infrastructure)
+- 0.7-0.8: important (decisions, preferences, procedures)
+- 0.5-0.6: useful (facts, entities, references)
+- 0.3-0.4: contextual (events, observations)
+- 0.1-0.2: noise (raw experiences, heartbeats)
+
+**`confidence`**:
+- 1.0 — user explicitly stated this
+- 0.7 — agent inferred this from context
+- 0.5 — single episode, low certainty
+
+**`ttl_hint`**:
+- `"slow"` — semantic or procedural types (long-term)
+- `"medium"` — references, infrastructure
+- `"fast"` — episodic types (event, experience)
+
+### Step 2: Embed via Ollama
+
+```bash
+curl -s http://localhost:11434/api/embeddings \
+  -H "Content-Type: application/json" \
+  -d '{"model": "snowflake-arctic-embed2:latest", "prompt": "<text>"}'
+```
+
+### Step 3: Build the Point
+
+```python
+import json, hashlib, subprocess
+from datetime import datetime, timezone
+
+now = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
+point_id = int(hashlib.sha256(text.encode()).hexdigest()[:16], 16) % (2**63)
+
+point = {
+    "id": point_id,
+    "vector": vector,  # from Step 2
+    "payload": {
+        "text": text,
+        "type": "<classified type>",
+        "mem_class": "<semantic|episodic|procedural>",
+        "entities": ["entity1", "entity2"],
+        "tags": ["tag1", "tag2"],
+        "importance": 0.7,
+        "quality_score": None,       # ALWAYS null — cron computes this
+        "confidence": 0.7,
+        "created_at": now,
+        "updated_at": now,
+        "source": "agent",           # "agent" for manual saves
+        "ttl_hint": "slow"
+    }
+}
+```
+
+### Step 4: Upsert to Qdrant
+
+```bash
+curl -s -X PUT http://10.0.0.22:6333/collections/memories/points \
+  -H "Content-Type: application/json" \
+  -d '{"points": [<point>]}'
+```
+
+Use `subprocess.run(["curl", ...])` — NOT Python `requests` (sandbox can't reach 10.0.0.22 via requests).
+
+### Step 5: Report
+
+Confirm to user: what was saved, type, mem_class, point ID.
+
+## Multi-Item Saves
+
+If the user asks to save multiple items, classify and embed each one separately. Batch upsert all points in a single curl call (up to 100 points).
+
+## What NOT to Do
+
+- Do NOT save without classifying type + mem_class
+- Do NOT set quality_score to anything other than null
+- Do NOT use Python `requests` library — use `subprocess.run(["curl", ...])`
+- Do NOT use string point IDs — must be unsigned integers
+- Do NOT save to any collection other than `memories` unless explicitly directed
+
+## Reference
+
+Full schema documentation: `/home/n8n/workspace/general/qdrant.md`
+Collection management (dedup, migration, registry): `qdrant-collection-management` skill