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openviking-memory▌

volcengine/openviking · updated Apr 8, 2026

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$npx skills add https://github.com/volcengine/openviking --skill openviking-memory
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summary

Searches long-term memories in OpenViking, returns relevant results.

skill.md

OpenViking Memory Guide

How It Works

  • Auto-Capture: At 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 filter
    • keyword mode: only captures text matching trigger words (e.g. "remember", "preference", etc.)
  • Auto-Recall: At before_prompt_build, automatically searches for relevant memories and injects them into context

Available Tools

memory_recall — Search Memories

Searches 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?"

memory_store — Manual Store

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]"

memory_forget — Delete Memories

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"

Configuration

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

Daily Operations

# 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.

Multi-Instance Support

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 ...

Troubleshooting

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
how to use openviking-memory

How to use openviking-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 openviking-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/volcengine/openviking --skill openviking-memory

The skills CLI fetches openviking-memory from GitHub repository volcengine/openviking 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/openviking-memory

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

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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. 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★★★★★68 reviews
  • ★★★★★Noor Nasser· Dec 16, 2024

    We added openviking-memory from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • ★★★★★Mateo Shah· Dec 16, 2024

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

  • ★★★★★Hassan Sanchez· Dec 12, 2024

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

  • ★★★★★Min Lopez· Dec 12, 2024

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

  • ★★★★★Min Zhang· Dec 12, 2024

    openviking-memory reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Zaid Chen· Dec 8, 2024

    openviking-memory reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Aditi Thomas· Dec 8, 2024

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

  • ★★★★★Aanya Choi· Nov 27, 2024

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

  • ★★★★★Aanya Thompson· Nov 7, 2024

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

  • ★★★★★Noor Desai· Nov 3, 2024

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

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