RushDB Agent Skills

Agent Skills that teach AI agents to use RushDB efficiently — querying, data modeling, and persistent memory.

Compatible with Claude, GitHub Copilot, Cursor, Windsurf, and any Agent Skills-compatible client.

Install

npx skills add rush-db/rushdb --path packages/skills

Or install from npm:

npm install @rushdb/skills

Available Skills

| Skill | What it teaches | | --------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------- | | rushdb-query-builder | Discovery-first workflow, SearchQuery syntax, aggregation, relationship traversal | | rushdb-agent-memory | Using RushDB as persistent structured memory for AI agents | | rushdb-data-modeling | LMPG model, label/property/relationship design, nested JSON import | | rushdb-faceted-search | Build faceted filter UIs — discover properties, enumerate values, map to widgets, assemble where | | rushdb-domain-template | Design a tailored schema for any domain through guided conversation — interview → labels + relationships + bootstrap payload |

rushdb-query-builder

Teaches the mandatory 3-step workflow for querying RushDB: ontology discovery → intent classification → query construction. Covers the full SearchQuery shape — where filters, aggregate functions, groupBy modes, relationship traversal, datetime operators, and vector similarity.

Includes a bundled reference file (references/search-query-spec.md) with the complete operator reference, syntax rules, and annotated examples loaded on demand.

Triggers when an agent needs to:

• List, filter, or search records
• Count, sum, average, or group data
• Traverse relationships between record types
• Run semantic/vector similarity search
• Build any findRecords query

rushdb-agent-memory

Teaches how to use RushDB as a drop-in persistent memory layer for AI agents — replacing separate vector DB, key-value store, and graph systems with a single ACID-safe, semantically searchable graph.

Includes a reference file (references/memory-patterns.md) with example JSON structures for sessions, decisions, and entities.

Triggers when an agent needs to:

• Store session data or conversation context
• Recall past decisions or prior context
• Build an entity graph that survives across sessions
• Search memory by meaning (semantic recall)
• Associate memories via relationships

rushdb-data-modeling

Teaches RushDB's property-centric LMPG (Labels, Multi-Properties, Graph) model: label naming conventions, property types, relationship design, how nested JSON auto-creates relationships on import, and schema evolution patterns.

Triggers when an agent needs to:

• Design a schema or data model
• Choose labels and property names
• Understand how nested JSON is imported
• Plan relationship structure
• Evolve or migrate an existing schema

rushdb-faceted-search

Teaches the full workflow for generating faceted filter UIs: call getOntology for structured property metadata (including id values), call propertyValues to enumerate distinct options per property, map each property type to the right UI widget (checkbox list, range slider, date picker, toggle), and assemble a live where clause as filters change.

Covers the eager/lazy loading split, context-aware vs. original value enumeration, the two-hook pattern used in real RushDB apps, active filter chips, and full reset.

Triggers when an agent needs to:

• Generate a filter sidebar, faceted search UI, or drill-down panel
• Enumerate available values for a property
• Map property types to UI controls
• Build a where clause that updates as the user selects filters
• Generate useProperties / usePropertyValues style hooks

rushdb-domain-template

Guides users through designing a tailored RushDB schema via a structured interview (5 questions), then outputs a schema summary, a ready-to-run bulkCreateRecords bootstrap payload, and starter queries. Includes compact template cards for 10 common domains (e-commerce, SaaS/DevOps, CRM, healthcare, fintech, content, agent memory, project management, scientific research, and product management) — all adapted to RushDB's record-centric LMPG model.

Triggers when a user:

• Is starting a new project and doesn't know what labels to define
• Asks "what records should I create for X?"
• Wants to get up and running quickly with a known domain
• Needs a starting point they can customise rather than designing from scratch

Skill Structure

Each skill follows the Agent Skills format:

skills/<skill-name>/
├── SKILL.md          # Required: YAML frontmatter + instructions
├── references/       # Optional: large reference docs loaded on demand
└── scripts/          # Optional: executable helpers (not used here)

License

Apache 2.0