Add save-q-memory skill (v1.0.0) — manual Qdrant write enforcer with 10-type schema
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Hermes Agent
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save-q-memory/SKILL.md
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save-q-memory/SKILL.md
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---
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name: save-q-memory
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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."
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version: 1.0.0
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author: Hermes Agent
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license: MIT
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metadata:
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hermes:
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tags: [qdrant, memories, save, schema, manual]
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related_skills: [qdrant-collection-management]
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---
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# Save Q Memory — Manual Write Enforcer
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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.
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## Trigger
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User says: "save to memory", "save to qdrant", "save this to memories", or any variant directing a manual Qdrant write.
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## Prerequisites
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- Qdrant at `http://10.0.0.22:6333`
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- Ollama at `http://localhost:11434` with `snowflake-arctic-embed2:latest`
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- Collection: `memories` (1024-dim, Cosine)
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## Mandatory Save Procedure
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### Step 1: Classify the Content
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Before embedding, classify EVERY item:
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**`type`** — pick ONE of 10:
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- `fact` — timeless/slow-changing world truth
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- `infrastructure` — hosts, IPs, services, configs, topology
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- `entity` — person, org, project, tool identity
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- `preference` — user's stable likes/dislikes, style, defaults
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- `procedure` — how-to: steps, commands, runbooks, fixes
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- `decision` — chosen approach + rationale (the why)
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- `observation` — consolidated pattern from many episodes (2nd-order)
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- `reference` — external KB/docs, URLs, citations
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- `event` — something that happened at a time; state change
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- `experience` — raw conversation turn / interaction log
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**`mem_class`** — pick ONE:
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- `semantic` — for fact, infrastructure, entity, preference, decision, observation, reference
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- `procedural` — for procedure
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- `episodic` — for event, experience
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**`entities`** — list of people, servers, IPs, tools, projects, models mentioned. Empty array `[]` if none.
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**`tags`** — 2-3 lowercase category tags for filtering. Examples: `["infrastructure", "dgx"]`, `["preference", "output_style"]`.
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**`importance`** — 0.0-1.0:
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- 0.9+: critical (court docs, credentials, core infrastructure)
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- 0.7-0.8: important (decisions, preferences, procedures)
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- 0.5-0.6: useful (facts, entities, references)
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- 0.3-0.4: contextual (events, observations)
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- 0.1-0.2: noise (raw experiences, heartbeats)
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**`confidence`**:
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- 1.0 — user explicitly stated this
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- 0.7 — agent inferred this from context
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- 0.5 — single episode, low certainty
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**`ttl_hint`**:
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- `"slow"` — semantic or procedural types (long-term)
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- `"medium"` — references, infrastructure
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- `"fast"` — episodic types (event, experience)
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### Step 2: Embed via Ollama
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```bash
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curl -s http://localhost:11434/api/embeddings \
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-H "Content-Type: application/json" \
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-d '{"model": "snowflake-arctic-embed2:latest", "prompt": "<text>"}'
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```
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### Step 3: Build the Point
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```python
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import json, hashlib, subprocess
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from datetime import datetime, timezone
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now = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
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point_id = int(hashlib.sha256(text.encode()).hexdigest()[:16], 16) % (2**63)
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point = {
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"id": point_id,
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"vector": vector, # from Step 2
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"payload": {
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"text": text,
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"type": "<classified type>",
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"mem_class": "<semantic|episodic|procedural>",
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"entities": ["entity1", "entity2"],
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"tags": ["tag1", "tag2"],
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"importance": 0.7,
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"quality_score": None, # ALWAYS null — cron computes this
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"confidence": 0.7,
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"created_at": now,
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"updated_at": now,
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"source": "agent", # "agent" for manual saves
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"ttl_hint": "slow"
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}
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}
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```
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### Step 4: Upsert to Qdrant
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```bash
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curl -s -X PUT http://10.0.0.22:6333/collections/memories/points \
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-H "Content-Type: application/json" \
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-d '{"points": [<point>]}'
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```
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Use `subprocess.run(["curl", ...])` — NOT Python `requests` (sandbox can't reach 10.0.0.22 via requests).
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### Step 5: Report
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Confirm to user: what was saved, type, mem_class, point ID.
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## Multi-Item Saves
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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).
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## What NOT to Do
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- Do NOT save without classifying type + mem_class
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- Do NOT set quality_score to anything other than null
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- Do NOT use Python `requests` library — use `subprocess.run(["curl", ...])`
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- Do NOT use string point IDs — must be unsigned integers
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- Do NOT save to any collection other than `memories` unless explicitly directed
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## Reference
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Full schema documentation: `/home/n8n/workspace/general/qdrant.md`
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Collection management (dedup, migration, registry): `qdrant-collection-management` skill
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