hermes-skills/save-q-memory/SKILL.md

name, description, version, author, license, metadata

name	description	version	author	license	metadata
save-q-memory	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.	1.1.0	Hermes Agent	MIT	hermes tags related_skills qdrant memories save schema manual 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

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

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",
        "_source": {"text": text}     # Original payload, preserved verbatim
    }
}

Step 4: Upsert to Qdrant

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