hugegraph-ai connects the HugeGraph graph platform with artificial intelligence tools, including large language models, graph retrieval, knowledge-graph construction, and graph machine learning. It is intended for developers building applications that query, analyze, or reason over graph data through Python interfaces and related modules.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add apache/hugegraph-ai --skill hugegraph-schema-designergit clone --depth 1 https://github.com/apache/hugegraph-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/apache/hugegraph-ai/hugegraph-schema-designer)<a href="https://agentmods.dev/skills/apache/hugegraph-ai/hugegraph-schema-designer"><img src="https://agentmods.dev/badge/skills/apache/hugegraph-ai/hugegraph-schema-designer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/apache/hugegraph-ai/hugegraph-schema-designer"><img src="https://agentmods.dev/badge/skills/apache/hugegraph-ai/hugegraph-schema-designer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.00541 |
| Opus 5.5 | $0.00020 | $0.00216 |
| Sonnet 5.5 | $0.00010 | $0.00108 |
| Haiku 4.5 | $0.00005 | $0.00054 |
Grade A, and why
hugegraph-schema-designer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
HugeGraph Schema Designer
Tool Routes
| Goal | Tool |
|---|---|
| Inspect current schema | inspect_graph_tool(include_raw_schema=true) |
| Design schema operations | design_schema_tool(operations?) |
| Validate schema operations | apply_schema_tool(mode="validate", operations) |
| Dry-run schema operations | apply_schema_tool(mode="dry_run", operations) |
| Execute real schema apply | confirm_write_tool(plan_id) in default core, one supported schema create per plan |
| Delete or roll back schema | No V1 stable tool |
The default core toolset permits confirmed apply only for
create_property_key, create_vertex_label, and create_edge_label. In
HUGEGRAPH_MCP_TOOLSET=legacy, real schema apply remains disabled.
Review the dry-run response before applying it. Confirm only a returned, confirmable
plan_id with confirm_write_tool(plan_id), then query get_write_status_tool(plan_id).
Schema create operations use a closed field contract. Property keys accept
data_type, cardinality, aggregate_type, and user_data; vertex labels
accept id_strategy, properties, primary_keys, nullable_keys,
index_labels, enable_label_index, and user_data; edge labels accept
source_label, target_label, properties, nullable_keys, sort_keys,
frequency, enable_label_index, and user_data. Unsupported fields,
including ttl*, are rejected during validation instead of being silently
dropped. Supported fields are forwarded and checked by a live-schema post-read.
Order
inspect_graph_tool -> design_schema_tool
-> apply_schema_tool(mode="validate")
-> apply_schema_tool(mode="dry_run")
-> review operations and preview
-> confirm_write_tool(plan_id) -> get_write_status_tool(plan_id)
Structured query and plan lifecycle tools require the default core toolset. For a legacy deployment, enable HUGEGRAPH_MCP_TOOLSET=core and restart before using them.
These skills describe current source. Published 1.7.1 uses v2_core / v1; see the migration guide.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago Changed · +2 lines 9b496d1d4c8b
- 19d ago First seen · 46 lines · 51 tokens per session scan A eb34956839cf
hugegraph-schema-designer is a skill published in the GitHub repository apache/hugegraph-ai (144 stars, last pushed 3d ago), licensed Apache-2.0. It adds 51 tokens to every session and 541 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-27.
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