---
name: Distill Memory
slug: distill-memory
category: AI Engineering
description: Distill Memory saves durable facts, decisions, preferences, and procedures from a conversation into searchable memories. Use it when a session produces insight that should be reused later.
github: "https://github.com/nowledge-co/community/tree/main/nowledge-mem-agent-plugin/skills/distill-memory"
language: Python
stars: 166
forks: 35
install: "npx degit https://github.com/nowledge-co/community/tree/main/nowledge-mem-agent-plugin/skills/distill-memory ~/.claude/skills/distill-memory"
installs_to: ~/.claude/skills/distill-memory
source_path: nowledge-mem-agent-plugin/skills/distill-memory/SKILL.md
collection_size: 25
category_size: 3670
collection_url: "https://dirskills.com/collections/nowledge-co/community"
added: 2026-09-08T05:34:48.472Z
last_synced: 2026-09-08T05:34:48.472Z
canonical_url: "https://dirskills.com/skills/distill-memory"
---

# Distill Memory

Distill Memory saves durable facts, decisions, preferences, and procedures from a conversation into searchable memories. Use it when a session produces insight that should be reused later.

**Install:**

```bash
npx degit https://github.com/nowledge-co/community/tree/main/nowledge-mem-agent-plugin/skills/distill-memory ~/.claude/skills/distill-memory
```

## README

# Distill Memory

Save proactively when the conversation produces a durable fact, preference, decision, plan, procedure, learning, event, or important context. Do not wait to be asked.

## When to Save

Good candidates include:

- decisions with rationale ("we chose PostgreSQL because ACID is required")
- repeatable procedures or workflows
- lessons from debugging, incidents, or root cause analysis
- durable preferences or constraints
- plans that future sessions will need to resume cleanly
- important context that would be lost when the session ends

Skip routine fixes with no generalizable lesson, work in progress that will change, simple Q&A answerable from documentation, and generic information already widely known.

## Add vs Update

- Use `nmem --json m add` when the insight is genuinely new.
- If an existing memory already captures the same decision, workflow, or preference and the new information refines it, use `nmem m update <id> ...` instead of creating a duplicate.
- At the end of a substantial task, explicitly check whether one durable memory should be added or updated.

Prefer atomic, standalone memories with strong titles and clear meaning. Focus on what was learned or decided, not routine chatter.

Use structured saves when possible: `--unit-type` (`fact`, `preference`, `decision`, `plan`, `procedure`, `learning`, `context`, `event`), `-l` labels, `-i` importance (0.8–1.0 major decisions, 0.5–0.7 useful patterns, 0.3–0.4 minor notes). For MCP/native tools, pass the same value as `unit_type` when you know it.

## Native Connector

These skills work in any agent via CLI. For auto-recall, auto-capture, and graph tools, check if your agent has a native Nowledge Mem connector — run the `check-integration` skill or see https://mem.nowledge.co/docs/integrations