read-working-memory▌
nowledge-co/community · updated Apr 8, 2026
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Daily briefing of active focus areas, priorities, and recent knowledge changes for cross-session continuity.
- ›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
Read Working Memory
Start every session with context. Your Working Memory is a daily briefing synthesized from your knowledge base.
When to Use
At session start:
- Beginning of a new conversation
- Returning to a project after a break
- When context about recent work would help
During session:
- User asks "what am I working on?" or "what's my context?"
- User references recent priorities or decisions
- Need to understand what's been happening across tools
Skip when:
- Already loaded this session
- User explicitly wants a fresh start
- Working on an isolated, context-independent task
Usage
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
What You'll Find
The Working Memory briefing contains:
- Active Focus Areas — Topics you're currently engaged with, ranked by recent activity
- Priorities — Items flagged as important or needing attention
- Unresolved Flags — Contradictions, stale information, or items needing verification
- Recent Activity — What changed in your knowledge base since the last briefing
- Deep Links — References to specific memories for further exploration
How to Use This Context
- Read once at session start — don't re-read unless asked
- Reference naturally — mention relevant context when it connects to the current task
- Don't overwhelm — share only the parts relevant to what the user is working on
- Cross-tool continuity — insights saved in other tools (Cursor, Claude Code, Codex) appear here
Troubleshooting
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.
How to use read-working-memory on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add read-working-memory
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches read-working-memory from GitHub repository nowledge-co/community and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate read-working-memory. Access the skill through slash commands (e.g., /read-working-memory) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ 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.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★38 reviews- ★★★★★Hana Lopez· Dec 20, 2024
read-working-memory fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Kiara Anderson· Dec 16, 2024
read-working-memory reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Mateo Thompson· Dec 12, 2024
read-working-memory is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Shikha Mishra· Dec 4, 2024
I recommend read-working-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Rahul Santra· Nov 23, 2024
Solid pick for teams standardizing on skills: read-working-memory is focused, and the summary matches what you get after install.
- ★★★★★Kiara Rahman· Nov 7, 2024
Registry listing for read-working-memory matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Advait Jain· Nov 3, 2024
Keeps context tight: read-working-memory is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Kiara Diallo· Oct 26, 2024
Useful defaults in read-working-memory — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Advait Gonzalez· Oct 22, 2024
I recommend read-working-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Pratham Ware· Oct 14, 2024
read-working-memory is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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