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 enterprise-readiness-scorergit 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/enterprise-readiness-scorer)<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/enterprise-readiness-scorer"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/enterprise-readiness-scorer/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/enterprise-readiness-scorer"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/enterprise-readiness-scorer.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 | $0.00190 | $0.01908 |
| Opus 5 | $0.00095 | $0.00954 |
| Sonnet 5 | $0.00038 | $0.00382 |
| Haiku 4.5 | $0.00019 | $0.00191 |
Grade A, and why
enterprise-readiness-scorer 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Enterprise Agentic-Readiness Scorer
Overview
Ch1 opens with a promise and a trap. The promise: an agent that pursues goals instead of answering questions. The trap: "you fire up your favorite LLM, add it to your agentic framework of choice, and connect it to a vector-based RAG system. Should be easy, right? Wrong." A naive vector-only approach creates five fatal flaws that are not bugs but an architectural failure preventing the system from becoming truly agentic.
This skill turns that diagnosis into a score. It checks four things the chapter argues are required for enterprise agency:
- The five fatal flaws are cured. Each flaw is cured only by a specific graph capability (context amnesia by evolving memory, relationship blindness by entity relationships, temporal ignorance by temporal evolution, reasoning paralysis by multi-hop reasoning, tool chaos by tool orchestration).
- The three agency dimensions are calibrated. Autonomy, action, and authority are sliding scales, not binary — and Ch1's point is calibration, not maximization (a real-estate agent has high autonomy but deliberately low pricing authority).
- The four emergent capabilities are present. Autonomous decision-making, contextual understanding, strategic tool utilization, memory persistence.
- The decision-trace test passes. Per Marple's test in the Enterprise Context Graphs section: can the system tell you not just what happened, but what alternatives were considered and rejected?
When to Use
- Before greenlighting an enterprise agent for production deployment
- Reviewing a vendor's "context graph" claim against the rejected-alternatives test
- Comparing a naive-vector prototype to a graph-augmented redesign
- Architecture review where someone proposes "just add a bigger vector store"
Phrases: "is this agent production-ready", "enterprise agentic readiness", "score my RAG architecture", "are we naive vector RAG", "context graph vs search index".
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 · 138 lines · 190 tokens per session scan A 7b39665abe28
enterprise-readiness-scorer is a skill published in the GitHub repository AnthonyAlcaraz/agentic-graph-rag-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 190 tokens to every session and 1,908 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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