# Memory

> Neural memory operations: search stored lessons, store new lessons, inspect memory stats, consolidate episodic patterns, and run decay/pruning.

- Skill: `fusengine/memory` (Agent Skill)
- Install (CLI): `npx skillmds@latest add fusengine/memory`
- Raw SKILL.md: https://api.skillmd.com/api/skills/fusengine/memory/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: fusengine (https://skillmd.com/u/fusengine)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/fusengine/memory

---


# Neural Memory Operations

## Operations
- `search <query>` - Search lessons
- `store <lesson>` - Store a lesson manually
- `stats` - Memory statistics
- `consolidate` - Force consolidation
- `decay` - Force decay/pruning

When the user asks for a memory operation, infer the operation and arguments
from the prompt. If the operation is ambiguous, ask one concise clarifying
question before calling memory MCP tools.

## Subcommands

### search
Search both Graphiti (episodic) and Qdrant (semantic):
1. Call mcp__graphiti__search_nodes with the query
2. Call mcp__qdrant__qdrant-find with the query
3. Merge and deduplicate results
4. Present ranked by relevance

### store
Manually store a lesson:
1. Parse the lesson text from arguments
2. Call mcp__graphiti__add_episode with the lesson
3. Call mcp__qdrant__qdrant-store with the lesson
4. Confirm storage with node/vector IDs

### stats
Display memory statistics:
1. Call mcp__graphiti__get_status for graph stats
2. Report: node count, edge count, episode count
3. Show last consolidation timestamp
4. Show log file sizes from ~/.codex/fusengine/logs/00-memory/

### consolidate
Force episodic to semantic consolidation:
1. Read recent episodes from Graphiti
2. Identify patterns (recurring errors, common solutions)
3. Store consolidated patterns in Qdrant
4. Report consolidation summary

### decay
Force memory decay and pruning:
1. Apply power law decay: R(t) = strength * (1+t)^(-rate)
2. Prune nodes with retention < 0.10
3. Prune edges with weight < 0.01
4. Report pruned count

