memory

yonatangross/orchestkit · updated Apr 8, 2026

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$npx skills add https://github.com/yonatangross/orchestkit --skill memory
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summary

Unified read-side memory skill with subcommands for searching, loading, syncing, history, and visualization.

skill.md

Memory - Read & Access Operations

Unified read-side memory skill with subcommands for searching, loading, syncing, history, and visualization.

Argument Resolution

SUBCOMMAND = "$ARGUMENTS[0]"  # First token: search, load, history, viz, status
QUERY = "$ARGUMENTS[1]"       # Second token onward: search query or flags
# $ARGUMENTS is the full string (CC 2.1.59 indexed access)

Usage

/ork:memory search <query>  # Search knowledge graph
/ork:memory load             # Load context at session start
/ork:memory history          # View decision timeline
/ork:memory viz              # Visualize knowledge graph
/ork:memory status           # Show memory system health

CRITICAL: Use AskUserQuestion When No Subcommand

If invoked without a subcommand, ask the user what they want:

AskUserQuestion(
  questions=[{
    "question": "What memory operation do you need?",
    "header": "Operation",
    "options": [
      {"label": "search", "description": "Search decisions and patterns in knowledge graph", "markdown": "```\nSearch Knowledge Graph\n──────────────────────\n  query ──▶ mcp__memory ──▶ results\n\n  Flags:\n  --category  Filter by type\n  --agent     Scope to agent\n  --limit N   Max results\n  --global    Cross-project\n```"},
      {"label": "load", "description": "Load relevant context for this session", "markdown": "```\nLoad Session Context\n────────────────────\n  Auto-detect project ──▶\n  ┌────────────────────┐\n  │ Recent decisions   │\n  │ Active patterns    │\n  │ Project entities   │\n  └────────────────────┘\n  Flags: --project, --global\n```"},
      {"label": "history", "description": "View decision timeline", "markdown": "```\nDecision Timeline\n─────────────────\n  ┌──── Feb 28 ────────────┐\n  │ Used Postgres over Mongo│\n  ├──── Feb 27 ────────────┤\n  │ Adopted MVC pattern     │\n  ├──── Feb 26 ────────────┤\n  │ Chose JWT over sessions │\n  └────────────────────────┘\n  Flags: --since, --mermaid\n```"},
      {"label": "viz", "description": "Visualize knowledge graph as Mermaid", "markdown": "```\nKnowledge Graph Viz\n───────────────────\n  Entities ──▶ Mermaid diagram\n\n  [Project] ──uses──▶ [Postgres]\n      │                    │\n      └──has──▶ [Auth] ──uses──▶ [JWT]\n\n  Output: Mermaid code block\n```"},
      {"label": "status", "description": "Check memory system health", "markdown": "```\nMemory Health Check\n───────────────────\n  ┌─────────────────────┐\n  │ MCP server    ✓/✗   │\n  │ Entity count  N     │\n  │ Relation count N    │\n  │ Last write    date  │\n  │ Graph size    N KB  │\n  └─────────────────────┘\n```"}
    ],
    "multiSelect": false
  }]
)

Subcommands

Load details: Read("${CLAUDE_SKILL_DIR}/references/memory-commands.md") for full usage, flags, output formats, and context-aware result limits for each subcommand.

Subcommand Purpose
search Search past decisions, patterns, entities. Supports --category (maps to metadata.category), --limit, --agent (scopes by agent_id), --global filter flags
load Auto-load relevant memories at session start. Supports --project, --global
history Decision timeline with table, Mermaid, or JSON output. Supports --since, --mermaid
viz Render knowledge graph as Mermaid diagram. See also Read("${CLAUDE_SKILL_DIR}/references/mermaid-patterns.md")
status Memory system health check

Workflow

1. Parse Subcommand

Extract first argument as subcommand
If no subcommand -> AskUserQuestion
Validate subcommand is one of: search, load, history, viz, status
Parse remaining flags
Check for --agent <agent-id> flag → agent_id: "ork:{agent-id}"

2. Execute Subcommand

Route to appropriate handler based on subcommand.

3. Report Results

Format output appropriate to the operation.


Rules Quick Reference

Rule Impact What It Covers
entity-extraction-patterns (load ${CLAUDE_SKILL_DIR}/rules/entity-extraction-patterns.md) HIGH Entity types, relation types, graph query semantics
deduplication-strategy (load ${CLAUDE_SKILL_DIR}/rules/deduplication-strategy.md) HIGH Edit-over-Write pattern, anchor-based insertion, verification

Session Resume

Load details: Read("${CLAUDE_SKILL_DIR}/references/session-resume-patterns.md") for CC 2.1.31 resume hints, context capture before ending, and resume workflows for PRs, issues, and implementations.


Related Skills

  • ork:remember - Store decisions and patterns (write-side)

Error Handling

  • If graph empty for viz: Show helpful message about using /ork:remember
  • If subcommand invalid: Show usage help
  • If memory files corrupt: Report and offer repair
  • If search query empty: Show recent entities instead
  • If no search results: Suggest alternatives
how to use memory

How to use 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 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/yonatangross/orchestkit --skill memory

The skills CLI fetches memory from GitHub repository yonatangross/orchestkit 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/memory

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

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

4.830 reviews
  • Shikha Mishra· Dec 24, 2024

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

  • Rahul Santra· Nov 15, 2024

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

  • Pratham Ware· Oct 6, 2024

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

  • Hiroshi Menon· Sep 25, 2024

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

  • Hiroshi Mehta· Sep 1, 2024

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

  • Anaya Li· Aug 20, 2024

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

  • Aanya Singh· Aug 16, 2024

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

  • Yash Thakker· Jul 27, 2024

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

  • Aanya Srinivasan· Jul 19, 2024

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

  • Ama Abebe· Jul 11, 2024

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

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