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.
git clone --depth 1 https://github.com/Aryanacoder/superragskillsWrote 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/rules/aryanacoder/superragskills/superrag-build)<a href="https://agentmods.dev/rules/aryanacoder/superragskills/superrag-build"><img src="https://agentmods.dev/badge/rules/aryanacoder/superragskills/superrag-build/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/rules/aryanacoder/superragskills/superrag-build"><img src="https://agentmods.dev/badge/rules/aryanacoder/superragskills/superrag-build.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 | $0.00065 | $0.00206 |
| Opus 5.5 | $0.00026 | $0.00082 |
| Sonnet 5.5 | $0.00013 | $0.00041 |
| Haiku 4.5 | $0.00006 | $0.00021 |
Grade A, and why
superrag-build 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 2mo 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.
What it actually says
superRAG-build
When this rule applies, read skills/superrag-build/SKILL.md and follow it.
Key behavior:
- Start with a structured interview.
- Classify the needed RAG type before choosing tools.
- Ask about goal, users, data, evidence, risk, runtime constraints, preferred stack, deployment target, and demo success.
- Produce a RAG brief, type decision, architecture, data contract, pipeline plan, retrieval plan, prompt policy, evaluation plan, and deployment plan.
- Prefer source-grounded answers, citation mapping, access control, retrieval debug traces, eval gates, and no-answer guardrails.
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.
- 2mo ago First seen · 17 lines · 65 tokens per session scan A 3cb70c2946ba
superrag-build is a cursor rule published in the GitHub repository Aryanacoder/superragskills (4 stars, last pushed 4mo ago), licensed MIT. It adds 65 tokens to every session and 206 once invoked, about $0.0003 per session on Opus 5.5. 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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