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persistent-memory▌

ropl-btc/agent-skills · updated Apr 8, 2026

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$npx skills add https://github.com/ropl-btc/agent-skills --skill persistent-memory
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summary

Use this skill as the single memory system for this repository.

skill.md

Persistent Memory

Use this skill as the single memory system for this repository.

Commands

Use either command style:

  • python3 .agents/skills/persistent-memory/scripts/memory.py <command>
  • .agents/skills/persistent-memory/scripts/pmem <command>

Supported commands:

  • init
  • sync (database-only health check)
  • cleanup-legacy
  • backfill-embeddings --batch 500
  • prune --source "<label>" [--older-than <days>]
  • search "<query>" --limit 8
  • add "<memory text>" --tags "<comma,tags>" --source "assistant"
  • recent --limit 10
  • stats

Required Workflow

  1. Initialize memory in a fresh workspace:
  • pmem init
  1. At the start of substantial tasks:
  • pmem sync (database-only health check)
  • pmem search "<topic keywords>" --limit 8
  1. When user explicitly says remember or when a durable preference/fact is learned:
  • pmem add "<memory text>" --tags "<tags>" --source "assistant"
  1. Before finalizing memory-sensitive work, verify recall state:
  • pmem stats

One-Time Migration (If Upgrading From Older Setup)

  1. Remove legacy imported rows:
  • pmem cleanup-legacy
  1. Generate vectors for existing notes:
  • pmem backfill-embeddings

Storage Rules

  • Store durable preferences, long-lived facts, stable workflows, and repeated constraints.
  • Do not store noisy one-off transient details unless requested.
  • Keep entries concise and specific.
  • Prefer tags that improve retrieval quality (preferences, calendar, comms, product).

Retrieval Rules

  • Use targeted search queries instead of broad terms.
  • Keep default --limit low unless deeper recall is needed.
  • search automatically reinforces recalled entries by updating hits and last_seen_at.
  • hits are analytics-oriented and not used as a direct ranking boost.
  • Search uses hybrid retrieval: lexical + semantic.
  • Semantic search tries sqlite-vec first and auto-falls back to Python cosine if needed.

Bootstrapping and Recovery

  • If .memory/ is missing, run pmem init.
  • pmem sync is a lightweight database-only check (no markdown import/export).
  • If semantic mode degrades, run pmem stats to inspect semantic_backend and embedding_coverage.
  • For command examples and quick troubleshooting, read references/usage.md.
how to use persistent-memory

How to use persistent-memory on Cursor

AI-first code editor with Composer

1

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 persistent-memory
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/ropl-btc/agent-skills --skill persistent-memory

The skills CLI fetches persistent-memory from GitHub repository ropl-btc/agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/persistent-memory

Reload or restart Cursor to activate persistent-memory. Access the skill through slash commands (e.g., /persistent-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.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

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. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 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▌

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.7★★★★★36 reviews
  • ★★★★★Daniel Dixit· Dec 24, 2024

    Useful defaults in persistent-memory — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Aarav Torres· Dec 16, 2024

    I recommend persistent-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Dhruvi Jain· Dec 12, 2024

    persistent-memory fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Ishan Brown· Nov 15, 2024

    persistent-memory has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • ★★★★★Mei Bansal· Nov 11, 2024

    Registry listing for persistent-memory matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Aarav Reddy· Nov 7, 2024

    Solid pick for teams standardizing on skills: persistent-memory is focused, and the summary matches what you get after install.

  • ★★★★★Oshnikdeep· Nov 3, 2024

    persistent-memory is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • ★★★★★Naina Ghosh· Oct 26, 2024

    persistent-memory has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • ★★★★★Ganesh Mohane· Oct 22, 2024

    Keeps context tight: persistent-memory is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • ★★★★★Hassan Mehta· Oct 6, 2024

    Solid pick for teams standardizing on skills: persistent-memory is focused, and the summary matches what you get after install.

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