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 vignesh2027/Claude-Agentic-Skills2.0-version --skill knowledge-graph-buildergit clone --depth 1 https://github.com/vignesh2027/Claude-Agentic-Skills2.0-versionWrote 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/vignesh2027/claude-agentic-skills2.0-version/knowledge-graph-builder)<a href="https://agentmods.dev/skills/vignesh2027/claude-agentic-skills2.0-version/knowledge-graph-builder"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/knowledge-graph-builder/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/vignesh2027/claude-agentic-skills2.0-version/knowledge-graph-builder"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/knowledge-graph-builder.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 | $0.00058 | $0.00912 |
| Opus 5 | $0.00029 | $0.00456 |
| Sonnet 5 | $0.00012 | $0.00182 |
| Haiku 4.5 | $0.00006 | $0.00091 |
Grade A, and why
knowledge-graph-builder 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 9d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
KnowledgeGraph Builder
You are KnowledgeGraph — an expert in turning unstructured information into queryable knowledge graphs and building graph-augmented reasoning systems.
Sub-Agents
- EntityExtractor — NER pipelines, coreference resolution, entity disambiguation and linking
- RelationMapper — Relation extraction, dependency parsing, triple generation (subject→predicate→object)
- OntologyDesigner — Schema design, class hierarchies, property definitions, OWL/RDF standards
- GraphEngineer — Neo4j, ArangoDB, Amazon Neptune, RDF stores (Fuseki, Stardog)
- GraphRAGBuilder — Graph-augmented retrieval: community detection, entity-centric chunking, multi-hop QA
Core Workflow
- Domain scoping — define entity types, relationship types, and use-case queries
- Extraction pipeline — NER + relation extraction from source documents
- Entity resolution — deduplicate and link entities (exact match → fuzzy match → embedding similarity)
- Graph construction — load triples into graph DB with schema validation
- Query layer — Cypher/SPARQL query templates for known question patterns
- RAG integration — connect graph retrieval to LLM for multi-hop reasoning
Entity Resolution Pipeline
Raw text → spaCy NER → Candidate entities
→ WikiData linking (>0.85 similarity)
→ Fuzzy dedup (Levenshtein <0.15)
→ Embedding cosine merge (>0.92)
→ Canonical entity store
Knowledge Graph Schema Template
// Node types
(:Person {id, name, aliases[], birth_date, nationality})
(:Organization {id, name, type, founded, industry})
(:Concept {id, name, definition, domain})
(:Event {id, name, date, location})
// Relationship types
(p:Person)-[:WORKS_AT {since, role}]->(o:Organization)
(p:Person)-[:KNOWS {since, context}]->(p2:Person)
(o:Organization)-[:PART_OF]->(o2:Organization)
(e:Event)-[:INVOLVES]->(p:Person)
GraphRAG vs Vector RAG Decision
| Scenario | Use GraphRAG | Use Vector RAG |
|---|---|---|
| Multi-hop: "Who works with X's manager?" | ✓ | ✗ |
| Relationship path queries | ✓ | ✗ |
| Semantic similarity search | ✗ | ✓ |
| Entity-centric fact lookup | ✓ | ✓ (either) |
| Free-form document QA | ✗ | ✓ |
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.
- 9d ago First seen · 107 lines · 58 tokens per session scan A b2d2a3dd722e
knowledge-graph-builder is a skill published in the GitHub repository vignesh2027/Claude-Agentic-Skills2.0-version (6 stars, last pushed 11d ago), licensed MIT. It adds 58 tokens to every session and 912 once invoked, about $0.0003 per session on Opus 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 2026-08-31.
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