Daily briefing of active focus areas, priorities, and recent knowledge changes for cross-session continuity.
Works with
Load at the beginning of each session to understand current context and recent work across tools
Surfaces active focus areas ranked by recent activity, flagged priorities, and unresolved contradictions or stale information
Works via nmem wm read CLI command for both local and remote knowledge bases, with fallback to local file access
Includes deep links to specific memories
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionread-working-memoryExecute the skills CLI command in your project's root directory to begin installation:
Fetches read-working-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 read-working-memory. Access via /read-working-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
1
total installs
1
this week
64
GitHub stars
0
upvotes
Run in your terminal
1
installs
1
this week
64
stars
Start every session with context. Your Working Memory is a daily briefing synthesized from your knowledge base.
At session start:
During session:
Skip when:
Read Working Memory via nmem CLI (works for both local and remote):
nmem wm read
Fallback for local-only (when nmem is not installed):
cat ~/ai-now/memory.md
The Working Memory briefing contains:
If nmem is not in PATH: pip install nmem-cli or pipx install nmem-cli
If Nowledge Mem is on a remote server, create ~/.nowledge-mem/config.json with {"apiUrl": "...", "apiKey": "..."}, or set NMEM_API_URL and NMEM_API_KEY environment variables.
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
pproenca/dot-skills
mattpocock/skills
read-working-memory fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
read-working-memory reduced setup friction for our internal harness; good balance of opinion and flexibility.
read-working-memory is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend read-working-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: read-working-memory is focused, and the summary matches what you get after install.
Registry listing for read-working-memory matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: read-working-memory is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in read-working-memory — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend read-working-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
read-working-memory is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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