Agent skill

Hyperspacedb Graph

Graph traversal, Lorentz hierarchy, and concept relationship operations for HyperspaceDB.

MITAuto-check passedKnowledge Management

Install Hyperspacedb Graph

skills CLI

$ npx skills add YARlabs/hyperspace-db --skill hyperspacedb-graph -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install YARlabs/hyperspace-db hyperspacedb-graph --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/YARlabs/hyperspace-db.git skills-src && mkdir -p .claude/skills && cp -r skills-src/integrations/hyperspacedb-skills/skills/hyperspacedb-graph .claude/skills/hyperspacedb-graph && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "hyperspacedb-graph" agent skill from https://github.com/YARlabs/hyperspace-db/tree/main/integrations/hyperspacedb-skills/skills/hyperspacedb-graph into .claude/skills/hyperspacedb-graph/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hyperspacedb-graph", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/YARlabs/hyperspace-db/tree/main/integrations/hyperspacedb-skills/skills/hyperspacedb-graph

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add YARlabs/hyperspace-db --skill hyperspacedb-graph -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install YARlabs/hyperspace-db hyperspacedb-graph --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/YARlabs/hyperspace-db.git skills-src && mkdir -p .agents/skills && cp -r skills-src/integrations/hyperspacedb-skills/skills/hyperspacedb-graph .agents/skills/hyperspacedb-graph && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "hyperspacedb-graph" agent skill from https://github.com/YARlabs/hyperspace-db/tree/main/integrations/hyperspacedb-skills/skills/hyperspacedb-graph into .agents/skills/hyperspacedb-graph/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hyperspacedb-graph", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add YARlabs/hyperspace-db --skill hyperspacedb-graph -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install YARlabs/hyperspace-db hyperspacedb-graph --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/YARlabs/hyperspace-db.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/integrations/hyperspacedb-skills/skills/hyperspacedb-graph .cursor/skills/hyperspacedb-graph && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "hyperspacedb-graph" agent skill from https://github.com/YARlabs/hyperspace-db/tree/main/integrations/hyperspacedb-skills/skills/hyperspacedb-graph into .cursor/skills/hyperspacedb-graph/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hyperspacedb-graph", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/YARlabs/hyperspace-db.git --path integrations/hyperspacedb-skills/skills/hyperspacedb-graph

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add YARlabs/hyperspace-db --skill hyperspacedb-graph -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install YARlabs/hyperspace-db hyperspacedb-graph --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/YARlabs/hyperspace-db.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/integrations/hyperspacedb-skills/skills/hyperspacedb-graph .gemini/skills/hyperspacedb-graph && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "hyperspacedb-graph" agent skill from https://github.com/YARlabs/hyperspace-db/tree/main/integrations/hyperspacedb-skills/skills/hyperspacedb-graph into .gemini/skills/hyperspacedb-graph/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hyperspacedb-graph", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install YARlabs/hyperspace-db hyperspacedb-graph

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add YARlabs/hyperspace-db --skill hyperspacedb-graph -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/YARlabs/hyperspace-db.git skills-src && mkdir -p .github/skills && cp -r skills-src/integrations/hyperspacedb-skills/skills/hyperspacedb-graph .github/skills/hyperspacedb-graph && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "hyperspacedb-graph" agent skill from https://github.com/YARlabs/hyperspace-db/tree/main/integrations/hyperspacedb-skills/skills/hyperspacedb-graph into .github/skills/hyperspacedb-graph/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hyperspacedb-graph", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add YARlabs/hyperspace-db --skill hyperspacedb-graph -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install YARlabs/hyperspace-db hyperspacedb-graph --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/YARlabs/hyperspace-db.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/integrations/hyperspacedb-skills/skills/hyperspacedb-graph .opencode/skills/hyperspacedb-graph && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "hyperspacedb-graph" agent skill from https://github.com/YARlabs/hyperspace-db/tree/main/integrations/hyperspacedb-skills/skills/hyperspacedb-graph into .opencode/skills/hyperspacedb-graph/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hyperspacedb-graph", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: hyperspacedb-graph
GitHub stars: 162
Token cost: ~1.4k tokens
SKILL.md length: 330 words
Files: 1
Skills in repo: 5
Repo updated: Oct 8, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Graph traversal, Lorentz hierarchy, and concept relationship operations for HyperspaceDB.

Works in 6 steps: Get Neighbors (Local Graph Connectivity) → Graph Traversal (Multi-hop BFS/DFS) → Explore Graph (Visualization-ready) → …
Working with hierarchical data
SKILL.md covers 1. Get Neighbors (Local Graph…, 2. Graph Traversal (Multi-hop…, 3. Explore Graph… and 4. Subsumption Tree (Lorentz…, plus 4 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Hyperspacedb Graph is an agent skill from YARlabs/hyperspace-db. Graph traversal, Lorentz hierarchy, and concept relationship operations for HyperspaceDB. Use this skill whenever working with hierarchical data, knowledge graphs, ontologies, concept taxonomies, or multi-hop reasoning chains. Trigger on: "knowledge graph", "hierarchy", "traverse", "parent concepts", "subsumption", "explore graph", "Lorentz embedding", "concept tree", "ontology".

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Knowledge Management, covering Knowledge graphs and Embeddings. The repository describes itself as: [H] HyperspaceDB is a high-performance, vector database. It features 1-bit quantization, async replication, and native support for hierarchical datasets (Lorentz, Poincaré ball &… The licence is MIT.

When your agent uses it

Working with hierarchical data
Knowledge graphs
Concept taxonomies
Multi-hop reasoning chains

Example prompts

“knowledge graph”
“hierarchy”
“traverse”
“/hyperspacedb-graph”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

Get Neighbors (Local Graph Connectivity)
Graph Traversal (Multi-hop BFS/DFS)
Explore Graph (Visualization-ready)
Subsumption Tree (Lorentz Hierarchy)
Concept Parents
Semantic Clusters

What it can do on your machine

Read from SKILL.md and the folder at commit f3ef6c7. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Hyperspacedb Graph loads about 1.4k tokens when it runs. Until then it costs about 100 tokens; SKILL.md has 330 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~100
When it runs · the whole SKILL.md, loaded when a task matches: ~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from YARlabs/hyperspace-db at commit f3ef6c7, republished under its MIT licence (© YARlabs). 330 words, ~1,395 tokens.

Download SKILL.mdSave it as .claude/skills/hyperspacedb-graph/SKILL.md (or your agent's skills folder).

name: hyperspacedb-graph
description: Graph traversal, Lorentz hierarchy, and concept relationship operations for HyperspaceDB. Use this skill whenever working with hierarchical data, knowledge graphs, ontologies, concept taxonomies, or multi-hop reasoning chains. Trigger on: "knowledge graph", "hierarchy", "traverse", "parent concepts", "subsumption", "explore graph", "Lorentz embedding", "concept tree", "ontology".

HyperspaceDB Graph Operations

HyperspaceDB's HNSW index is a navigable graph — you can traverse it directly for knowledge graph exploration, ontology navigation, and multi-hop reasoning.

The Lorentz metric is specifically designed for hierarchical data: taxonomies, organizational charts, biological hierarchies, and knowledge bases.

1. Get Neighbors (Local Graph Connectivity)

Returns the nearest neighbors of a point in the HNSW graph — its direct connections.

typescript

const neighbors = await client.getNeighbors(
  pointId,      // number
  16,           // k — number of neighbors
  0,            // layer — HNSW layer (0 = most connected base layer)
  "my_collection"
);
// returns: Array<{ id: number, distance: number }>

Use case: Explore direct semantic relationships without doing a full ANN search.

2. Graph Traversal (Multi-hop BFS/DFS)

Traverse the knowledge graph starting from a node, following edges for N hops.

typescript

const result = await client.traverse(
  startId,    // starting node
  3,          // max hops
  "my_collection"
);
// returns visited node IDs and traversal paths

Use case: "What concepts are reachable from 'quantum entanglement' within 2 hops?"

3. Explore Graph (Visualization-ready)

Returns nodes and edges in a structured format ready for graph visualization (D3, Cytoscape, etc.).

typescript

const graph = await client.exploreGraph(
  startId,   // number
  2,         // max_depth
  256,       // max_nodes
  "my_collection"
);
// returns: { nodes: [{id, metadata}], edges: [{source, target, weight}] }

4. Subsumption Tree (Lorentz Hierarchy)

Retrieves the full hierarchical tree of concepts rooted at a given ID. Only meaningful for collections using the Lorentz metric.

typescript

const tree = await client.getSubsumptionTree(
  rootId,      // root concept ID
  3,           // max_depth
  "ontology_collection"
);

Use case: "Show me the complete taxonomy under the concept 'Mammal'."

5. Concept Parents

Retrieve the parent concepts of a node in the hierarchy.

typescript

const parents = await client.getConceptParents(
  conceptId,   // number
  0,           // HNSW layer
  32,          // max parents to return
  "ontology_collection"
);

Use case: "What is the parent category of 'Golden Retriever'?" → Dog → Canine → Mammal

6. Semantic Clusters

Detects emergent thematic regions within the vector space — unsupervised conceptual grouping.

typescript

const clusters = await client.findSemanticClusters(
  "my_collection",
  8           // number of clusters
);
// returns: Array<{ centroid: number[], member_ids: number[], label?: string }>

Use case: "What are the main topic clusters in this document corpus?"

Best Practices for Hierarchical Data

Setting Up a Lorentz Collection

typescript

import { CollectionSchema } from 'hyperspace-sdk-ts';

const schema: CollectionSchema = {
  components: [
    { name: "ontology", metric: "lorentz", fullDimension: 128, weight: 1.0 }
  ],
  cascadePipeline: [
    { componentName: "ontology", cutoffDimension: 128, storeInRam: true, rerankTopK: 64 }
  ]
};
await client.createCollection("knowledge_graph", schema);

Setting Up a Hybrid Collection (Hierarchy + Semantics)

Use hybrid when your data has both hierarchical structure and dense semantic content. The fixed layout is: 33 Lorentz dims (hierarchy) + N Euclidean dims (semantics).

typescript

// 33 Lorentz + 768 Euclidean = 801 total dimensions
const hybridSchema: CollectionSchema = {
  components: [
    { name: "hybrid", metric: "hybrid", fullDimension: 801, weight: 1.0 }
  ],
  cascadePipeline: [
    { componentName: "hybrid", cutoffDimension: 801, storeInRam: false, rerankTopK: 200 }
  ]
};
await client.createCollection("knowledge_with_semantics", hybridSchema);

Embedding Hierarchical Data

When inserting hierarchical data, use embeddings that preserve hyperbolic distance:

Poincaré embeddings
Lorentzian embeddings (e.g., from hyperspace-sdk-ts math utilities)
Standard embeddings from models fine-tuned on ontological data

typescript

import { HyperbolicMath } from 'hyperspace-sdk-ts';

// Project a Euclidean embedding onto the Lorentz hyperboloid
const lorentzVector = HyperbolicMath.toLorentz(euclideanEmbedding);

// For hybrid: concatenate Lorentz (33d) + Euclidean (Nd)
const hybridVector = [...lorentzVector.slice(0, 33), ...semanticEmbedding];

Traversal vs. ANN Search

Need	Use
"Find similar vectors"	`search()` or `searchText()`
"Follow edges in the graph"	`traverse()`
"Show concept hierarchy"	`getSubsumptionTree()`
"What is this concept's parent?"	`getConceptParents()`
"Visualize the graph"	`exploreGraph()`
"Find topic clusters"	`findSemanticClusters()`

Files

Just SKILL.md in integrations/hyperspacedb-skills/skills/hyperspacedb-graph of YARlabs/hyperspace-db.

Open the folder on GitHubat commit f3ef6c7

Compare with similar skills

Hyperspacedb Graph next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Hyperspacedb Graph compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Hyperspacedb Graph this skillYARlabs/hyperspace-db	162	—	~1.4k	Automated safety check: Pass	MIT	yesterday
Cortexdbliliang-cn/cortexdb	274	—	~6k	Automated safety check: Warn	MIT	yesterday
Memory Statusdimetron/pi-go	208	—	~537	Automated safety check: Pass	MIT	today
Cortexdbliliang-cn/cortexdb	274	—	~18k	Automated safety check: Warn	MIT	yesterday
SDK AI Bot Run EvaluationAzure/azure-sdk-tools	134	—	~1.1k	Automated safety check: Notes	MIT	today
Neo4j Genai Plugin Skillneo4j-contrib/neo4j-skills	114	—	~3k	Automated safety check: Notes	MIT	3 days ago

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Questions about Hyperspacedb Graph

What does Hyperspacedb Graph do?

Graph traversal, Lorentz hierarchy, and concept relationship operations for HyperspaceDB. Hyperspacedb Graph is an agent skill from YARlabs/hyperspace-db. Graph traversal, Lorentz hierarchy, and concept relationship operations for HyperspaceDB.

When should I use Hyperspacedb Graph?

Hyperspacedb Graph fits situations like: working with hierarchical data; knowledge graphs; concept taxonomies; multi-hop reasoning chains.

How do I install Hyperspacedb Graph in Claude Code?

Run `npx skills add YARlabs/hyperspace-db --skill hyperspacedb-graph -a claude-code`. Or copy the skill folder (integrations/hyperspacedb-skills/skills/hyperspacedb-graph in YARlabs/hyperspace-db) into .claude/skills/hyperspacedb-graph in your project. Claude Code loads it when a task matches its description.

How do I install Hyperspacedb Graph in Codex?

Run `npx skills add YARlabs/hyperspace-db --skill hyperspacedb-graph -a codex`. Or copy the skill folder (integrations/hyperspacedb-skills/skills/hyperspacedb-graph in YARlabs/hyperspace-db) into .agents/skills/hyperspacedb-graph in your project. Codex loads it when a task matches its description.

Can I use Hyperspacedb Graph in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add YARlabs/hyperspace-db --skill hyperspacedb-graph -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hyperspacedb-graph, .gemini/skills/hyperspacedb-graph, .github/skills/hyperspacedb-graph and .opencode/skills/hyperspacedb-graph in your project.

What does Hyperspacedb Graph need to run?

SKILL.md names no scripts, command-line tools or credentials: Hyperspacedb Graph is instructions for the agent only.

Does Hyperspacedb Graph access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Hyperspacedb Graph safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Hyperspacedb Graph use?

Hyperspacedb Graph is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Hyperspacedb Graph use?

About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Hyperspacedb Graph?

Skills that share tags, products or a category with Hyperspacedb Graph: Cortexdb (liliang-cn/cortexdb, 274 stars), Memory Status (dimetron/pi-go, 208 stars), Cortexdb (liliang-cn/cortexdb, 274 stars) and SDK AI Bot Run Evaluation (Azure/azure-sdk-tools, 134 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Hyperspacedb Graph?

YARlabs (a GitHub user) maintains it in YARlabs/hyperspace-db, which has 162 GitHub stars. The repository holds 5 skills in this directory. The repository was last updated on October 8, 2026.

Source: YARlabs/hyperspace-db on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Hyperspacedb Graph

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

HyperspaceDB Graph Operations

1. Get Neighbors (Local Graph Connectivity)

2. Graph Traversal (Multi-hop BFS/DFS)

3. Explore Graph (Visualization-ready)

4. Subsumption Tree (Lorentz Hierarchy)

5. Concept Parents

6. Semantic Clusters

Best Practices for Hierarchical Data

Setting Up a Lorentz Collection

Setting Up a Hybrid Collection (Hierarchy + Semantics)

Embedding Hierarchical Data

Traversal vs. ANN Search

See Also

Files

Compare with similar skills

Similar skills

More from YARlabs/hyperspace-db

Related

Questions about Hyperspacedb Graph