| name | store-memory |
| version | 1.0.0 |
| description | Store information in the hierarchical memory system. Supports three tiers: working (ephemeral), episodic (recent events), and semantic (permanent patterns). Use for learning from observations. Keywords: memory, store, learn, remember, episodic, semantic, pattern.
|
| metadata | {"domain":"general","category":"memory","requires-approval":false,"confidence":0.9,"mcp-servers":[]} |
Store Memory
Preconditions
Before applying this skill, verify:
- Memory system is initialized (Qdrant available)
- Content to store is meaningful
- Appropriate tier is selected
Actions
1. Classify Memory Tier
Determine which memory tier to use:
- Working: Temporary context, 1-hour TTL, max 20 items
- Episodic: Recent events, 7-30 day retention, time-decayed
- Semantic: Permanent patterns, never expires
2. Extract Structured Facts
Use LLM to extract key facts from content:
input: $raw_content
max_length: 4000
temperature: 0.1
output: list[str]
3. Store in Memory Tier
For Working Memory:
memory.add_working(
content=content,
metadata={"source": source, "timestamp": now}
)
For Episodic Memory:
memory.add_episodic(
content=content,
user_id=user_id,
metadata={"event_type": type, "timestamp": now}
)
For Semantic Memory:
memory.add_semantic(
content=content,
user_id=user_id,
metadata={"pattern_type": type, "confidence": confidence}
)
Success Criteria
The skill succeeds when:
Failure Handling
If storage fails:
- Check Qdrant connectivity
- Verify content is not empty
- Fall back to working memory if persistent storage unavailable
Examples
Input Context:
{
"tier": "episodic",
"content": "Pod nginx-abc123 crashed due to OOM. Increased memory limit from 512Mi to 1Gi.",
"user_id": "k8s-monitor",
"event_type": "remediation_success",
"source": "remediator"
}
Expected Output:
{
"stored": true,
"tier": "episodic",
"memory_id": "mem_abc123",
"facts_extracted": 2
}
