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 h4vzz/awesome-ai-agent-skills --skill knowledge-graph-creationgit clone --depth 1 https://github.com/h4vzz/awesome-ai-agent-skillsWrote 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/h4vzz/awesome-ai-agent-skills/knowledge-graph-creation)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/knowledge-graph-creation"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/knowledge-graph-creation/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/h4vzz/awesome-ai-agent-skills/knowledge-graph-creation"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/knowledge-graph-creation.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 | $0.00031 | $0.02155 |
| Opus 5 | $0.00015 | $0.01077 |
| Sonnet 5 | $0.00006 | $0.00431 |
| Haiku 4.5 | $0.00003 | $0.00215 |
Grade A, and why
knowledge-graph-creation 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 12d 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.
This is a copy
95% identical to knowledge-graph-creation — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Graph Creation
This skill enables an AI agent to transform unstructured text into a structured knowledge graph. The agent extracts entities (people, organizations, technologies, concepts), identifies the relationships between them, generates formal graph triples (subject-predicate-object), and outputs the graph in both a queryable format (Cypher for Neo4j, JSON-LD) and a visual diagram (Mermaid). Knowledge graphs are valuable for understanding complex domains, powering semantic search, detecting implicit connections, and building recommendation systems.
Workflow
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Analyze the Source Material: Read the input text and determine its domain, scope, and complexity. Identify the types of entities likely present (people, organizations, locations, technical concepts, events, etc.) and the granularity appropriate for the graph. A technical architecture document requires fine-grained component-level entities, while a news article may need coarser actor-level entities.
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Extract Entities: Identify all named entities and significant concepts in the text. For each entity, record its canonical name, type (person, organization, technology, concept, event, location), and any notable attributes mentioned (e.g., founding date, version number, role). Deduplicate entities that appear under different names or abbreviations.
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Map Relationships: For every pair of entities that interact in the text, identify the relationship between them. Express each relationship as a directed triple: (Subject) -[PREDICATE]-> (Object). Choose predicates from a consistent vocabulary (e.g., WORKS_AT, DEPENDS_ON, CREATED_BY, PART_OF, COMPETES_WITH). Record the source sentence for traceability.
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Generate Graph Triples and Schema: Formalize the extracted data into a structured format. Output triples in one or more of: Cypher CREATE statements for Neo4j, JSON-LD for web interoperability, or a simple CSV of (subject, predicate, object) rows. Define a lightweight schema listing entity types and valid relationship types.
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.
- 12d ago First seen · 150 lines · 31 tokens per session scan A 2aed9f2c4237
knowledge-graph-creation is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (34 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 2,155 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to knowledge-graph-creation, differing in 2 lines, and is treated as a copy.
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