distill-memory - SKILL.md Agent Skill

name: distill-memory description: Recognize breakthrough moments, blocking resolutions, and design decisions worth preserving. Detect high-value insights that save future time. Suggest distillation at valuable moments, not routine work.

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 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, or on Arch Linux yay -S nmem-cli / paru -S nmem-cli

For remote servers: run nmem config client set url https://... and nmem config client set api-key ... once on this machine.

Run /status to check server connection.