Productivity

distill-memory

nowledge-co/community · updated Apr 8, 2026

$npx skills add https://github.com/nowledge-co/community --skill distill-memory
summary

Persistent knowledge capture for insights, decisions, and procedures that span multiple agent sessions.

  • Store decisions with rationale, debugging lessons, repeatable workflows, and durable preferences as searchable memories
  • Distinguish between new insights (use add ) and refinements to existing memories (use update ) to avoid duplication
  • Design memories as atomic, standalone entries with clear titles that remain useful across future sessions
  • Ideal for preserving incident learnings
skill.md

Distill Memory

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

When to Suggest (Moment Detection)

Breakthrough: Extended debugging resolves, user relief ("Finally!", "Aha!"), root cause found

Decision: Compared options, chose with rationale, trade-off resolved

Research: Investigated multiple approaches, conclusion reached, optimal path determined

Twist: Unexpected cause-effect, counterintuitive solution, assumption challenged

Lesson: "Next time do X", preventive measure, pattern recognized

Skip: Routine fixes, work in progress, simple Q&A, generic info

Memory Quality

Good (atomic + actionable):

  • "React hooks cleanup must return function. Caused leaks."
  • "PostgreSQL over MongoDB: ACID needed for transactions."

Poor: Vague "Fixed bugs", conversation transcript

Tool Usage

Use nmem CLI to create memories:

nmem m add "Insight + context for future use" \
  -t "Searchable title (50-60 chars)" \
  -i 0.8

If an existing memory already captures the same decision, workflow, or preference and the new information refines it, update that memory instead of creating a duplicate:

nmem m update <id> -t "Updated title"

Content: Outcome/insight focus, include "why", enough context

Importance: 0.8-1.0 major | 0.5-0.7 useful | 0.3-0.4 minor

Note: For programmatic use, add --json flag to get JSON response

Examples:

# High-value insight
nmem m add "React hooks cleanup must return function. Caused memory leaks in event listeners." \
  -t "React Hooks Cleanup Pattern" \
  -i 0.9

# Decision with context
nmem m add "Chose PostgreSQL over MongoDB for ACID compliance and complex queries" \
  -t "Database: PostgreSQL" \
  -i 0.9

Suggestion

Timing: After resolution/decision, when user pauses

Pattern: "This [type] seems valuable - [essence]. Distill into memory?"

Frequency: 1-3 per session typical, quality over quantity

Troubleshooting

If nmem is not in PATH: pip install nmem-cli

For remote servers: create ~/.nowledge-mem/config.json with {"apiUrl": "...", "apiKey": "..."}.

Run /status to check server connection.