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 vector-vs-graph-retrieval-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/vector-vs-graph-retrieval-selector)<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/vector-vs-graph-retrieval-selector"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/vector-vs-graph-retrieval-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/vector-vs-graph-retrieval-selector"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/vector-vs-graph-retrieval-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 | $0.00215 | $0.02395 |
| Opus 5 | $0.00108 | $0.01197 |
| Sonnet 5 | $0.00043 | $0.00479 |
| Haiku 4.5 | $0.00021 | $0.00239 |
Grade A, and why
vector-vs-graph-retrieval-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 11d 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vector-vs-Graph Retrieval Selector
Overview
Ch1 makes the vector-vs-graph choice evidence-based rather than ideological. Microsoft's BenchmarkQED classifies queries on two axes:
- Scope — local (specific facts in a small number of regions) vs global / sensemaking (reasoning over large portions of the dataset).
- Type — data (direct fact retrieval) vs activity (interpretive / strategic).
The chapter's numeric anchors:
- Vector RAG: ~90% accuracy on simple lookups (DataLocal); 20-30% on complex reasoning (ActivityGlobal). "The very mechanism that makes vector search efficient becomes its fundamental limitation."
- LazyGraphRAG outperforms vector RAG by 50-60% on multi-hop reasoning.
- EyeLevel.ai: at 100,000 pages, vector accuracy drops up to 12% while graph drops only ~2%.
- The larger-context-window rebuttal: BenchmarkQED tested vector RAG against LazyGraphRAG with a ~1-million-token window (essentially the whole dataset); vector RAG still lost on every query type except the most basic factual questions, and bigger windows worsen "lost in the middle."
Where vector RAG shines (Ch1): local, fact-based lookups — customer support, FAQ, recommendation. Where it collapses: multi-hop reasoning, temporal awareness, the associativity gap ("which services were affected by the database migration that followed the security patch we discussed last month").
Ch1's own recommendation for agents is a HYBRID architecture — parallel vector + graph (vector search -> graph traversal -> context synthesis) — because agentic behavior "requires constantly moving between local and global understanding." GraphRAG is not free: the chapter names upfront graph-construction cost, query latency that grows with graph size, contextual nuance lost in triples, and schema-evolution cost. The selector surfaces those costs whenever it recommends GRAPH or HYBRID.
When to Use
- Choosing a retrieval architecture for a new enterprise agent
- Answering "should we add a graph, or is vector RAG enough?"
- Rebutting "let's just use a bigger context window instead of a graph"
- Mapping a mixed query workload to the right per-query strategy
- Teaching the BenchmarkQED local/global x data/activity quadrants
What ships with it
3 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.
- 11d ago First seen · 161 lines · 215 tokens per session scan A a12219ca6a79
vector-vs-graph-retrieval-selector is a skill published in the GitHub repository AnthonyAlcaraz/agentic-graph-rag-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 215 tokens to every session and 2,395 once invoked, about $0.0011 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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