remembering-conversations▌
obra/episodic-memory · updated Apr 8, 2026
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Search conversation history to avoid reinventing solutions and inform architectural decisions.
- ›Dispatch the search-conversations subagent to query past conversations; the agent synthesizes results into actionable insights with sources
- ›Use after understanding the task when facing \"how should I...\" questions, architectural decisions, or unfamiliar workflows
- ›Activate when stuck on complex problems, following past patterns, or when user references previous work or decisions
- ›Avoid se
Remembering Conversations
Core principle: Search before reinventing. Searching costs nothing; reinventing or repeating mistakes costs everything.
Mandatory: Use the Search Agent
YOU MUST dispatch the search-conversations agent for any historical search.
Announce: "Dispatching search agent to find [topic]."
Then use the Task tool with subagent_type: "search-conversations":
Task tool:
description: "Search past conversations for [topic]"
prompt: "Search for [specific query or topic]. Focus on [what you're looking for - e.g., decisions, patterns, gotchas, code examples]."
subagent_type: "search-conversations"
The agent will:
- Search with the
searchtool - Read top 2-5 results with the
showtool - Synthesize findings (200-1000 words)
- Return actionable insights + sources
Saves 50-100x context vs. loading raw conversations.
When to Use
You often get value out of consulting your episodic memory once you understand what you're being asked. Search memory in these situations:
After understanding the task:
- User asks "how should I..." or "what's the best approach..."
- You've explored current codebase and need to make architectural decisions
- User asks for implementation approach after describing what they want
When you're stuck:
- You've investigated a problem and can't find the solution
- Facing a complex problem without obvious solution in current code
- Need to follow an unfamiliar workflow or process
When historical signals are present:
- User says "last time", "before", "we discussed", "you implemented"
- User asks "why did we...", "what was the reason..."
- User says "do you remember...", "what do we know about..."
Don't search first:
- For current codebase structure (use Grep/Read to explore first)
- For info in current conversation
- Before understanding what you're being asked to do
Direct Tool Access (Discouraged)
You CAN use MCP tools directly, but DON'T:
mcp__plugin_episodic-memory_episodic-memory__searchmcp__plugin_episodic-memory_episodic-memory__show
Using these directly wastes your context window. Always dispatch the agent instead.
See MCP-TOOLS.md for complete API reference if needed for advanced usage.
How to use remembering-conversations 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 remembering-conversations
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches remembering-conversations from GitHub repository obra/episodic-memory 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 remembering-conversations. Access the skill through slash commands (e.g., /remembering-conversations) 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
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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
4.7★★★★★64 reviews- ★★★★★Aisha Kapoor· Dec 28, 2024
Keeps context tight: remembering-conversations is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Isabella Iyer· Dec 20, 2024
Useful defaults in remembering-conversations — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Zaid Choi· Dec 8, 2024
remembering-conversations has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Zaid Sethi· Nov 27, 2024
Useful defaults in remembering-conversations — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Isabella Khanna· Nov 19, 2024
Registry listing for remembering-conversations matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Isabella Menon· Nov 11, 2024
remembering-conversations has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Ira Haddad· Nov 3, 2024
remembering-conversations fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Isabella Johnson· Oct 22, 2024
We added remembering-conversations from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Maya Khanna· Oct 18, 2024
I recommend remembering-conversations for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Isabella Tandon· Oct 10, 2024
remembering-conversations reduced setup friction for our internal harness; good balance of opinion and flexibility.
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