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 AnthonyAlcaraz/agentic-graph-rag-skills --skill kg-extraction-approach-selectorgit clone --depth 1 https://github.com/AnthonyAlcaraz/agentic-graph-rag-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/anthonyalcaraz/agentic-graph-rag-skills/kg-extraction-approach-selector)<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/kg-extraction-approach-selector"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/kg-extraction-approach-selector/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/anthonyalcaraz/agentic-graph-rag-skills/kg-extraction-approach-selector"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/kg-extraction-approach-selector.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 | $0.00199 | $0.02403 |
| Opus 5 | $0.00100 | $0.01202 |
| Sonnet 5 | $0.00040 | $0.00481 |
| Haiku 4.5 | $0.00020 | $0.00240 |
Grade A, and why
kg-extraction-approach-selector 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.
How it starts
The opening of the file, as written. The whole thing — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
KG Extraction Approach Selector
Overview
An agent's knowledge comes from heterogeneous sources — relational databases, documents, free text — and each source shape demands a different extraction strategy. The chapter's rule: the source and the reasoning need pick the approach, not the other way round. Forcing one extractor onto every source is the failure this skill exists to prevent.
Four approaches, each with a characteristic profile:
- Structured database integration: the source already has a schema, so materialize it into nodes and edges (batch, CDC stream, or virtual graph views). Deterministic and high-precision — no LLM variance to validate. Only applicable when the source is genuinely structured.
- LLM-based extraction: prompt an LLM for ontology-constrained subject-predicate-object triples from free text. Simplest to stand up and flexible across topics, but non-deterministic; validate against the ontology and route low-confidence extractions to human review.
- iText2KG (incremental): extract entities and relations section-by-section and disambiguate each against the prior set. A growing corpus is extended without re-processing what is already ingested, and no domain-specific schema is required.
- RAKG (document-level): gather every text segment where an entity appears plus related subgraphs before generating relations. Whole-document context and hallucination filtering push relationship fidelity high, at the cost of retrieval infrastructure.
The selector scores each approach across the five features weighted by the caller's source profile. A structured source is a categorical fact (you cannot LLM-extract triples from a relational table), so it hard-routes to materialization; everything else is decided by the weighted scores — an unstructured + growing corpus routes to iText2KG, unstructured + whole-document context routes to RAKG, and a one-shot unstructured pass routes to plain LLM extraction.
The incremental_cost helper makes the iText2KG advantage concrete. Adding 50
documents to a 5000-document corpus: iText2KG processes 50; a full-rebuild
approach re-processes all 5000. The savings_vs_rebuild field is the
re-extraction a batch pipeline pays on every update.
What ships with it
2 files 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.
- 12d ago First seen · 162 lines · 199 tokens per session scan A d8d50cd81f75
kg-extraction-approach-selector is a skill published in the GitHub repository AnthonyAlcaraz/agentic-graph-rag-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 199 tokens to every session and 2,403 once invoked, about $0.0010 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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