Searches long-term memories in OpenViking, returns relevant results.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionopenviking-memoryExecute the skills CLI command in your project's root directory to begin installation:
Fetches openviking-memory from volcengine/openviking 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 openviking-memory. Access via /openviking-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.
Submit your Claude Code skill and start earning
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
0
total installs
0
this week
21.3K
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
21.3K
stars
afterTurn (end of one user turn run), automatically extracts memories from user/assistant messages
semantic mode: captures all qualifying user text, relying on OpenViking's extraction pipeline to filterkeyword mode: only captures text matching trigger words (e.g. "remember", "preference", etc.)before_prompt_build, automatically searches for relevant memories and injects them into contextSearches long-term memories in OpenViking, returns relevant results.
| Parameter | Required | Description |
|---|---|---|
query |
Yes | Search query text |
limit |
No | Maximum number of results (defaults to plugin config) |
scoreThreshold |
No | Minimum relevance score 0-1 (defaults to plugin config) |
targetUri |
No | Search scope URI (defaults to plugin config) |
Example: User asks "What programming language did I say I like?"
Writes text to an OpenViking session and runs memory extraction.
| Parameter | Required | Description |
|---|---|---|
text |
Yes | Information text to store |
role |
No | Session role (default user) |
sessionId |
No | Existing OpenViking session ID |
Example: User says "Remember my email is [email protected]"
Delete by exact URI, or search and delete.
| Parameter | Required | Description |
|---|---|---|
uri |
No | Exact memory URI (direct delete) |
query |
No | Search query (find then delete) |
targetUri |
No | Search scope URI |
limit |
No | Search limit (default 5) |
scoreThreshold |
No | Minimum relevance score |
Example: User says "Forget my phone number"
| Field | Default | Description |
|---|---|---|
mode |
remote |
local (start local server) or remote (connect to remote) |
baseUrl |
http://127.0.0.1:1933 |
OpenViking server URL (remote mode) |
apiKey |
— | OpenViking API Key (optional) |
agentId |
default |
Identifies this agent to OpenViking |
configPath |
~/.openviking/ov.conf |
Config file path (local mode) |
port |
1933 |
Local server port (local mode) |
targetUri |
viking://user/memories |
Default search scope |
autoCapture |
true |
Automatically capture memories |
captureMode |
semantic |
Capture mode: semantic / keyword |
captureMaxLength |
24000 |
Maximum text length per capture |
autoRecall |
true |
Automatically recall and inject context |
recallLimit |
6 |
Maximum memories injected during auto-recall |
recallScoreThreshold |
0.01 |
Minimum relevance score for recall |
ingestReplyAssist |
true |
Add reply guidance when detecting multi-party conversation text |
# Start (local mode: source env first)
source ~/.openclaw/openviking.env && openclaw gateway
# Start (remote mode: no env needed)
openclaw gateway
# Check status
openclaw status
openclaw config get plugins.slots.contextEngine
# Disable memory
openclaw config set plugins.slots.contextEngine legacy
# Enable memory
openclaw config set plugins.slots.contextEngine openviking
Restart the gateway after changing the slot.
If you have multiple OpenClaw instances, use --workdir to target a specific one:
# Install script
curl -fsSL ... | bash -s -- --workdir ~/.openclaw-openclaw-second
# Setup helper
npx ./examples/openclaw-plugin/setup-helper --workdir ~/.openclaw-openclaw-second
# Manual config (prefix openclaw commands)
OPENCLAW_STATE_DIR=~/.openclaw-openclaw-second openclaw config set ...
| Symptom | Cause | Fix |
|---|---|---|
extracted 0 memories |
Wrong API Key or model name | Check api_key and model in ov.conf |
port occupied |
Port used by another process | Change port: openclaw config set plugins.entries.openviking.config.port 1934 |
| Plugin not loaded | Env file not sourced or slot not configured | Check openclaw status output |
| Inaccurate recall | recallScoreThreshold too low | Increase threshold or adjust recallLimit |
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
pproenca/dot-skills
jezweb/claude-skills
We added openviking-memory from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
openviking-memory is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend openviking-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: openviking-memory is focused, and the summary matches what you get after install.
openviking-memory reduced setup friction for our internal harness; good balance of opinion and flexibility.
openviking-memory reduced setup friction for our internal harness; good balance of opinion and flexibility.
openviking-memory has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: openviking-memory is focused, and the summary matches what you get after install.
openviking-memory fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in openviking-memory — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
showing 1-10 of 68