.Claude Plugin
Persistent memory for AI agents via the Neural Memory nmem tools (nmem_remember, nmem_recall, nmem_r
by nhadaututtheky · MIT
The skill
Persistent memory for AI agents via the Neural Memory nmem tools (nmem_remember, nmem_recall, nmem_recap, nmem_auto, nmem_index, nmem_health): stores decisions, errors, fixes, and preferences as linked neurons and retrieves them through spreading activation. Use when starting or ending a coding session, saving a completed task's root cause or decision for later, recalling past project context, or running nmem memory maintenance.
Neural Memory
Persistent memory for AI agents — stores experiences as interconnected neurons, recalls through spreading activation.
Session Lifecycle
- Start:
nmem_recap()→nmem_recall("<project> <topic>") - During:
nmem_remember(...)after each completed task - End:
nmem_auto(action="process", text="brief summary") - Emergency:
nmem_auto(action="flush", text="...")before /compact
Save — When & How
nmem_remember(content="Chose X over Y because Z", type="decision", priority=7, tags=["project", "topic"])
| Signal | type | priority |
|---|---|---|
| Chose between alternatives | decision | 7 |
| Fixed a bug (root cause + fix) | error | 7 |
| Discovered a pattern | insight | 6 |
| Learned user preference | preference | 8 |
| Established a process | workflow | 6 |
| Reusable fact | fact | 5 |
| User instruction | instruction | 8 |
Quality (system scores 0-10 automatically):
- Causal language: "X because Y", "chose X over Y", "root cause was X"
- Include specifics: file paths, versions, error traces
- 50-300 chars optimal. >500 chars penalized — split instead.
- Scratch notes:
ephemeral=true(24h, never synced) - Do NOT save: routine file reads, things in git, duplicates
Recall
nmem_recall(query="project auth bug") # depth auto-detected
- Causal queries ("why", "because", "caused") activate cause-effect chains
- Fixes auto-supersede errors (old errors demoted when fix is stored)
- Prefix queries with project name for accuracy
Smart Behaviors (automatic)
- Quality scoring: specificity, structure, brevity scored per memory
- Dedup: similar memories flagged — use nmem_edit instead of duplicating
- Causal synapses: fixes create RESOLVED_BY links, demote old errors
- Stale detection: old-version references get -20% retrieval penalty
- Cold demotion: 0 access in 30d → cold; 90d → prune candidate (pinned exempt)
- SimHash merge: content-similar memories consolidated; 5+ group → summary fiber
Key Tools
| Tool | When |
|---|---|
nmem_recall |
Query memories (depth auto-detected) |
nmem_context |
Load recent memories at session start |
nmem_recap |
Resume session (level=1-3, topic="X") |
nmem_session |
Track session state (get/set/end) |
nmem_edit |
Fix wrong type/content/priority |
nmem_forget |
Remove outdated memories (soft/hard) |
nmem_index |
Scan codebase into memory graph |
nmem_train |
Train docs into permanent knowledge |
nmem_health |
Brain grade + top penalties to fix |
nmem_explain |
Trace path between two concepts |
nmem_cognitive |
Hypotheses + predictions dashboard |
All tools: compact=true saves 60-80% tokens.
Links
Use this skill
Connect your agent to the Skill Store MCP server and ask for it by ref: nhadaututtheky/claude-plugin-19679751762.
Details
- Publisher
- nhadaututtheky
- Category
- AI & Agents › Memory & context
- License
- MIT
- Updated
- 2026-10-07
References
More from nhadaututtheky
- Memory Audit Comprehensive memory quality review across 6 dimensions: purity, freshness, coverage, clarity, re…
- Memory Evolution Evidence-based memory optimization from real usage patterns.
- Memory Intake Structured memory creation workflow.
- Neural Memory Associative memory with spreading activation for persistent, intelligent recall.