atxp-memory▌
atxp-dev/cli · updated May 11, 2026
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Cloud backup, restore, and local vector search for agent .md memory files.
- ›Push/pull markdown memory files to ATXP cloud servers for disaster recovery and workspace bootstrapping; only .md files are transmitted, never credentials or configs
- ›Index local memories into a zvec vector database and search by natural language query using locality-sensitive hashing embeddings, entirely offline with no authentication required
- ›Chunk memories by heading boundaries and return ranked results with
ATXP Memory — Agent Memory Management
Manage your agent's .md memory files: back up and restore to/from ATXP cloud servers, and search your local memories using zvec vector similarity search.
Capabilities
| Capability | Description |
|---|---|
| Cloud Backup | Push/pull .md files to ATXP servers for disaster recovery |
| Local Search | Index .md files into a local zvec vector database, then search by natural language query |
| Status | View cloud backup info and local index statistics |
Security Model
- Only
.mdfiles are collected and transmitted (push/pull). No credentials, JSON configs, binaries, or other file types are ever sent. - Files are sent to ATXP servers over HTTPS, associated with the authenticated agent's identity.
pushreplaces the server snapshot entirely (latest snapshot only, no history).pullis non-destructive — it writes server files to the local directory but does not delete local files absent from the server.- Local search index is stored in a
.atxp-memory-index/subdirectory inside--path. It never leaves the local machine. - index and search do not require authentication or network access.
- Filesystem access: reads from
--pathdirectory (push/index), writes to--pathdirectory (pull) and--path/.atxp-memory-index/(index). No other directories are touched. - No modification of OpenClaw config or auth files.
When to Use
| Situation | Command |
|---|---|
| After meaningful changes to SOUL.md, MEMORY.md, or at end of session | push |
| Bootstrapping a fresh workspace or recovering from environment loss | pull |
| After updating memory files and before starting a task that requires recall | index |
| Looking for relevant context in past memories | search |
| Verify backup exists before risky operations | status |
Commands Reference
| Command | Description |
|---|---|
npx atxp@latest memory push --path <dir> |
Recursively collect all *.md files from <dir> and upload to server |
npx atxp@latest memory pull --path <dir> |
Download backup from server and write files to <dir> |
npx atxp@latest memory index --path <dir> |
Chunk .md files by heading and build a local zvec search index |
npx atxp@latest memory search <query> --path <dir> |
Search indexed memories by similarity |
npx atxp@latest memory status [--path <dir>] |
Show cloud backup info and/or local index stats |
Options
| Option | Required | Description |
|---|---|---|
--path <dir> |
Yes (push/pull/index/search) | Directory to operate on |
--topk <n> |
No (search only) | Number of results to return (default: 10) |
How Local Search Works
-
Indexing (
memory index):- Scans all
.mdfiles recursively from--path - Splits each file into chunks at heading boundaries (h1/h2/h3)
- Converts each chunk into a 256-dimensional feature vector using locality-sensitive hashing (unigrams + bigrams)
- Stores vectors and metadata in a local zvec database (HNSW index) at
<path>/.atxp-memory-index/
- Scans all
-
Searching (
memory search):- Converts the query text into the same vector representation
- Performs approximate nearest neighbor search via zvec's HNSW index
- Returns the top-k most similar chunks with file paths, headings, line numbers, and similarity scores
The search is purely local — no network requests, no API keys, no cost. Re-index after modifying memory files.
Path Conventions
Typical OpenClaw workspace paths:
~/.openclaw/workspace-<id>/
~/.openclaw/workspace-<id>/SOUL.md
~/.openclaw/workspace-<id>/MEMORY.md
~/.openclaw/workspace-<id>/memory/
~/.openclaw/workspace-<id>/AGENTS.md
~/.openclaw/workspace-<id>/USER.md
Backward Compatibility
The backup command is still accepted as an alias for memory:
npx atxp@latest backup push --path <dir> # works, same as memory push
npx atxp@latest backup pull --path <dir> # works, same as memory pull
npx atxp@latest backup status # works, same as memory status
Limitations
.mdfiles only — all other file types are ignored during push/index and not present in pull.- Latest snapshot only — each push overwrites the previous backup. There is no version history.
- Requires ATXP auth for cloud operations — run
npx atxp@latest loginornpx atxp@latest agent registerfirst. --pathis required — there is no auto-detection of workspace location.- Local search requires @zvec/zvec — install with
npm install @zvec/zvecbefore using index/search. - Feature-hash embeddings — local search uses statistical text hashing, not neural embeddings. It works well for keyword and phrase matching but is not a full semantic search. For best results, use specific terms from your memory files.
How to use atxp-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 atxp-memory
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches atxp-memory from GitHub repository atxp-dev/cli 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 atxp-memory. Access the skill through slash commands (e.g., /atxp-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
4.7★★★★★62 reviews- ★★★★★Hassan Malhotra· Dec 12, 2024
We added atxp-memory from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Mei Brown· Dec 12, 2024
atxp-memory reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Hassan Kapoor· Dec 8, 2024
atxp-memory fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Luis Desai· Dec 8, 2024
Useful defaults in atxp-memory — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Hassan Garcia· Nov 27, 2024
We added atxp-memory from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Aisha Khan· Nov 23, 2024
Useful defaults in atxp-memory — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Aisha Chen· Nov 23, 2024
atxp-memory has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Aisha Ndlovu· Nov 15, 2024
Solid pick for teams standardizing on skills: atxp-memory is focused, and the summary matches what you get after install.
- ★★★★★Aisha Haddad· Nov 3, 2024
atxp-memory fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Fatima Brown· Nov 3, 2024
Registry listing for atxp-memory matched our evaluation — installs cleanly and behaves as described in the markdown.
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