Use this skill as the single memory system for this repository.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpersistent-memoryExecute the skills CLI command in your project's root directory to begin installation:
Fetches persistent-memory from ropl-btc/agent-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 persistent-memory. Access via /persistent-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.
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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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Use this skill as the single memory system for this repository.
Use either command style:
python3 .agents/skills/persistent-memory/scripts/memory.py <command>.agents/skills/persistent-memory/scripts/pmem <command>Supported commands:
initsync (database-only health check)cleanup-legacybackfill-embeddings --batch 500prune --source "<label>" [--older-than <days>]search "<query>" --limit 8add "<memory text>" --tags "<comma,tags>" --source "assistant"recent --limit 10statspmem initpmem sync (database-only health check)pmem search "<topic keywords>" --limit 8remember or when a durable preference/fact is learned:pmem add "<memory text>" --tags "<tags>" --source "assistant"pmem statspmem cleanup-legacypmem backfill-embeddingspreferences, calendar, comms, product).--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.sqlite-vec first and auto-falls back to Python cosine if needed..memory/ is missing, run pmem init.pmem sync is a lightweight database-only check (no markdown import/export).pmem stats to inspect semantic_backend and embedding_coverage.references/usage.md.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
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Useful defaults in persistent-memory — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend persistent-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
persistent-memory fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
persistent-memory has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for persistent-memory matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: persistent-memory is focused, and the summary matches what you get after install.
persistent-memory is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
persistent-memory has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: persistent-memory is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: persistent-memory is focused, and the summary matches what you get after install.
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