HelixDB Skills

Hosted skills.sh repository for HelixDB agent skills.

These skills are for agents that need to:

• write Helix queries in the Rust DSL
• translate from Cypher, Gremlin, SQL, and legacy HelixQL (HQL) into Helix query code
• optimize Helix query shape and index usage
• build correct dynamic POST /v1/query payloads
• design and operate an agent memory system on Helix's hybrid graph + vector + full-text engine

Status

Available now:

• helix-query-authoring
• helix-query-from-cypher
• helix-query-from-gremlin
• helix-query-from-hql
• helix-query-json-dynamic
• helix-query-optimize
• helix-memory-system

Planned next:

• helix-query-from-sql

Install

npx skills add HelixDB/skills

Repository Layout

• skills/ contains the published skills
• docs/ contains shared reference material used while authoring skills
• examples/ contains generic canonical examples and before-and-after patterns
• benchmarks/ contains evaluation scaffolding for prompt and gold-answer testing
• helix-skills-repo-plan.md is the working implementation plan and checklist

Current Skills

helix-query-authoring

Use this skill when an agent needs to write or revise Helix Rust DSL queries from scratch.

It teaches agents to:

• inspect local query patterns before inventing new ones
• choose read_batch() versus write_batch() correctly
• anchor on the narrowest indexed node or edge set first
• preserve tenant scope for text and vector search
• shape outputs intentionally with project, value_map, limit, range, and dedup

helix-query-json-dynamic

Use this skill when an agent needs to build or debug dynamic inline-query requests for POST /v1/query.

It teaches agents to:

• use the correct request envelope
• distinguish stored routes from inline dynamic routes
• add parameter_types when typed coercion matters
• send DateTime values correctly
• avoid malformed bundle-shaped payloads

helix-query-from-cypher

Use this skill when an agent needs to port Neo4j or Cypher queries into Helix Rust DSL.

It teaches agents to:

• translate MATCH into explicit anchors and traversals
• map WHERE to Predicate logic
• map RETURN, DISTINCT, ordering, and limits into explicit output shaping
• handle OPTIONAL MATCH, MERGE, CASE, UNWIND, FOREACH, multi-hop traversal, null checks, and timestamps as Helix-native translations rather than literal rewrites

helix-query-from-gremlin

Use this skill when an agent needs to port Gremlin or TinkerPop traversals into Helix Rust DSL.

It teaches agents to:

• translate g.V, hasLabel, and has into anchors and predicates
• map out, in, both, outE, and inE into explicit Helix traversal steps
• map dedup, count, range, ordering, and valueMap into deliberate result shaping
• handle repeat, path, select, and side-effect-heavy traversals as semantic translations rather than literal rewrites

helix-query-from-hql

Use this skill when an agent needs to migrate legacy HelixQL (HQL) .hx queries into the v2 Rust or TypeScript DSL.

It teaches agents to:

• map every HQL construct (N<T>/E<T>, Out/In/OutE/FromN/ToN, WHERE/EQ/IS_IN, projections, GROUP_BY/AGGREGATE_BY, ORDER/RANGE, AddN/AddE/UPDATE/DROP, SearchV/SearchBM25) to its Rust and TypeScript builder
• handle the Rust-vs-TypeScript spelling differences (in_/in, where_/where, ::/., Some()/null)
• flag HQL features with no DSL equivalent (Upsert, RerankRRF/RerankMMR, shortest-path, Embed, advanced math, EXISTS/count-in-WHERE, schema defaults, #[model]/#[mcp]) and move that logic to application code
• verify each migration by compiling, diffing the Rust vs TypeScript JSON AST for parity, and running against the same data

helix-query-optimize

Use this skill when an agent needs to review or improve Helix query performance.

It teaches agents to:

• fix anchor choice before anything else
• match query shape to existing indexes
• move scope filters earlier
• shrink large projections
• review BM25 and vector search routes separately

helix-memory-system

Use this skill when an agent needs to design or operate an AI agent memory system on Helix — generation, deduplication, updating/consolidation, deletion/forgetting, and categorisation, not just retrieval.

It teaches agents to:

• model per-user memory with User, Memory, Category, Entity, and Session labels plus the edges and indexes that make it fast and tenant-safe
• choose the right mechanism per operation: properties + equality index, graph edges, vector search, or BM25 text search
• run the full write/maintain lifecycle (dedup-on-generate, reinforce-on-access, supersede/correct, soft-delete, decay and expiry sweeps, upsert-and-link categorisation)
• build hybrid recall that fuses vector + BM25 app-side and expands through the graph

It is TypeScript-first (@helixdb/enterprise-ql) with a Rust DSL variant in EXAMPLES.rust.md.

Shared References

Start here when working on the next skills:

• docs/source-canon.md
• docs/dsl-cheatsheet.md
• docs/cypher-rosetta.md
• docs/gremlin-rosetta.md
• docs/dynamic-query-examples.md
• docs/optimization-checklist.md
• examples/authoring-patterns.md
• examples/search-patterns.md
• examples/optimization-patterns.md

Notes

• This repo uses the hosted skills.sh layout: skills/<name>/SKILL.md.
• Local OpenCode discovery still uses .opencode/skills/, .claude/skills/, or .agents/skills/ after installation.
• This repo is intentionally written against public Helix behavior and repo-local canonical examples rather than app-specific implementations.