Persistent knowledge capture for insights, decisions, and procedures that span multiple agent sessions.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondistill-memoryExecute the skills CLI command in your project's root directory to begin installation:
Fetches distill-memory from nowledge-co/community and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate distill-memory. Access via /distill-memory in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Run in your terminal
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Save proactively when the conversation produces a decision, preference, plan, procedure, learning, or important context. Do not wait to be asked.
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
Good (atomic + actionable):
Poor: Vague "Fixed bugs", conversation transcript
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
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
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.
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Registry listing for distill-memory matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in distill-memory — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
distill-memory fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
distill-memory reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added distill-memory from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
distill-memory reduced setup friction for our internal harness; good balance of opinion and flexibility.
distill-memory reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: distill-memory is focused, and the summary matches what you get after install.
distill-memory is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: distill-memory is focused, and the summary matches what you get after install.
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