---
name: Distill Memory
slug: distill-memory-3
category: AI Engineering
description: Distill Memory saves key decisions, procedures, learnings, preferences, and plans as durable memories. Use it when something non-obvious is discovered or a significant choice is made so future sessions can find it again.
github: "https://github.com/nowledge-co/community/tree/main/nowledge-mem-codex-plugin/skills/distill-memory"
language: Python
stars: 166
forks: 35
install: "npx degit https://github.com/nowledge-co/community/tree/main/nowledge-mem-codex-plugin/skills/distill-memory ~/.claude/skills/distill-memory"
installs_to: ~/.claude/skills/distill-memory
source_path: nowledge-mem-codex-plugin/skills/distill-memory/SKILL.md
collection_size: 25
category_size: 3670
collection_url: "https://dirskills.com/collections/nowledge-co/community"
added: 2026-09-08T05:34:53.007Z
last_synced: 2026-09-08T05:34:53.007Z
canonical_url: "https://dirskills.com/skills/distill-memory-3"
---

# Distill Memory

Distill Memory saves key decisions, procedures, learnings, preferences, and plans as durable memories. Use it when something non-obvious is discovered or a significant choice is made so future sessions can find it again.

**Install:**

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

## README

Capture what matters before the session ends. Save decisions, procedures, and learnings as durable memories that any connected AI tool can find later.

## What to distill

- **Decisions** with rationale ("we chose PostgreSQL because ACID is required")
- **Procedures**: non-obvious steps, workarounds, setup sequences
- **Learnings**: surprises, gotchas, corrections to prior assumptions
- **Preferences**: user's stated preferences for future reference
- **Plans** that future sessions will need to resume
- **Context** that would be lost when the session ends

## What to skip

- Routine code changes, obvious fixes, standard operations
- Work in progress that will change before it matters
- Generic information already widely known
- Simple Q&A answerable from documentation

## Workflow

If this session already exposes the Nowledge Mem MCP server:

1. Search first with `memory_search` to avoid duplicates.
2. If an existing memory captures the same concept, use `memory_update`.
3. Otherwise, create it with `memory_add`.

Otherwise:

1. Search first to avoid duplicates: `nmem --json m search "concept"`
2. If an existing memory captures the same concept, update it:
   ```bash
   nmem --json m update <memory_id> -c "updated content"
   ```
3. Otherwise, create a new memory:
   ```bash
   nmem --json m add "content" -t "Title" --unit-type decision -l "label" -s codex -i 0.8
   ```

At the end of a substantial task, explicitly check whether one durable memory should be added or updated. Do not skip that review just because the user did not ask.

## Unit types

`fact`, `preference`, `decision`, `plan`, `procedure`, `learning`, `context`, `event`

## Additional flags

- Temporal context: `--when past` (or `present`, `future`, `timeless`)
- Event date: `--event-start 2026-03` (for time-anchored knowledge)

## Importance scale

- `0.8-0.9`: Major decisions, important procedures, breakthroughs
- `0.5-0.7`: Useful patterns, conventions, secondary decisions
- `0.3-0.4`: Minor notes, preferences, contextual observations
- `1.0`: rare, only for critical corrections or pivotal choices

## After saving

Report what was stored, which unit type was used, and why each memory was worth keeping. One strong memory is better than three weak ones.

## Links

- [Documentation](https://mem.nowledge.co/docs)