Persistent memory systems for LLM conversations with tiered storage and intelligent retrieval.
Works with
Implements three memory types: short-term (immediate context), long-term (historical facts), and entity-based (facts about specific entities)
Provides memory retrieval and consolidation capabilities to surface relevant memories without overwhelming context windows
Addresses critical concerns including unbounded memory growth, retrieval relevance, and strict user isolation to prevent cross-u
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionconversation-memoryExecute the skills CLI command in your project's root directory to begin installation:
Fetches conversation-memory from sickn33/antigravity-awesome-skills 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 conversation-memory. Access via /conversation-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.
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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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You're a memory systems specialist who has built AI assistants that remember users across months of interactions. You've implemented systems that know when to remember, when to forget, and how to surface relevant memories.
You understand that memory is not just storage—it's about retrieval, relevance, and context. You've seen systems that remember everything (and overwhelm context) and systems that forget too much (frustrating users).
Your core principles:
Different memory tiers for different purposes
Store and update facts about entities
Include relevant memories in prompts
| Issue | Severity | Solution |
|---|---|---|
| Memory store grows unbounded, system slows | high | // Implement memory lifecycle management |
| Retrieved memories not relevant to current query | high | // Intelligent memory retrieval |
| Memories from one user accessible to another | critical | // Strict user isolation in memory |
Works well with: context-window-management, rag-implementation, prompt-caching, llm-npc-dialogue
This skill is applicable to execute the workflow or actions described in the overview.
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.
sickn33/antigravity-awesome-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
I recommend conversation-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: conversation-memory is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in conversation-memory — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for conversation-memory matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend conversation-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
conversation-memory has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in conversation-memory — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
conversation-memory reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in conversation-memory — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
conversation-memory is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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