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 agentmods add skills/aryanacoder/superragskills/superrag-buildnpx skills add Aryanacoder/superragskills --skill superrag-buildgit 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/skills/aryanacoder/superragskills/superrag-build)<a href="https://agentmods.dev/skills/aryanacoder/superragskills/superrag-build"><img src="https://agentmods.dev/badge/skills/aryanacoder/superragskills/superrag-build.svg" alt="Measured on agentmods" 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 | $0.00131 | $0.01857 |
| Opus 5 | $0.00066 | $0.00928 |
| Sonnet 5 | $0.00026 | $0.00371 |
| Haiku 4.5 | $0.00013 | $0.00186 |
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 5d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
superRAG-build
Mission
Be the RAG architect, interviewer, implementation planner, and deployment guide. Turn an unclear RAG idea into the simplest architecture that can satisfy the user's data, risk, quality, budget, and deployment constraints.
Do not jump straight to code for broad RAG requests. First classify the RAG type, choose the stack, define the evaluation path, and identify deployment constraints. If the user asks for a tiny prototype, ask only the missing high-risk questions and build a narrow vertical slice.
First Response
Ask the first batch of questions, then adapt:
- What job should this RAG app help users complete?
- Who are the users, and what is the cost of a wrong answer?
- What data exists: file types, volume, languages, scans, images, tables, databases, websites, code, APIs?
- Does every answer need citations, page previews, exact quotes, source downloads, or audit logs?
- Is this local/offline, cloud, hybrid, multi-tenant, or enterprise/private?
- Preferred stack: AWS, Azure, OpenAI-hosted, open source, LangChain, LlamaIndex, custom, no preference?
- What is the first demo query that must work?
Then say which RAG families are likely candidates and what you need next. Ask questions in batches; do not dump the whole questionnaire unless the user asks for a worksheet. Use references/question-bank.md for exhaustive discovery.
Architecture Selection
Use references/rag-types-decision-matrix.md before choosing tools.
Default choices:
- Naive/basic RAG: small corpus, low risk, natural-language lookup, quick prototype.
- Hybrid RAG: exact terms, IDs, part numbers, policy clauses, support tickets, code, procedures, or mixed vague/specific queries.
- Reranked RAG: quality matters and first-stage retrieval returns too many candidates.
- Metadata-filtered RAG: tenants, permissions, product/version filters, regions, dates, languages, document status.
- Hierarchical/parent-child RAG: long PDFs, books, policy docs, manuals, multi-section reports.
- Agentic RAG: multi-hop questions, query planning, iterative retrieval, ambiguity, tool use, cross-document synthesis.
- Corrective/Self-RAG: retrieval may be weak and the system must evaluate, retry, or refuse.
- GraphRAG: entity relationships, communities, investigations, knowledge discovery, cross-document relationship queries.
- Multimodal RAG: images, diagrams, scans, charts, screenshots, video frames, CAD/flowcharts, visual tables.
- Structured-data RAG: SQL/BI/metrics/doc hybrids where the answer needs database queries plus text evidence.
- Streaming/real-time RAG: fast-changing data, events, logs, tickets, CDC, queues.
- Local/offline RAG: air-gapped, privacy, edge machines, field laptops, classified or regulated data.
What ships with it
13 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.
- agents/openai.yaml 267 B
- examples/final-plan-skeleton.md 1.6 KB
- examples/first-interview.md 1.0 KB
- references/architecture-playbook.md 5.3 KB
- references/build-from-scratch.md 4.3 KB
- references/deployment-playbooks.md 4.0 KB
- references/evaluation-observability.md 2.5 KB
- references/output-templates.md 3.4 KB
- references/provider-stack-matrix.md 4.2 KB
- references/question-bank.md 5.6 KB
- references/rag-types-decision-matrix.md 5.9 KB
- references/research-sources.md 2.7 KB
- references/security-governance.md 2.1 KB
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.
- 5d ago First seen · 131 lines · 131 tokens per session scan A 8bb332cbdfdc
superrag-build is a skill published in the GitHub repository Aryanacoder/superragskills (3 stars, last pushed 2mo ago), licensed MIT. It adds 131 tokens to every session and 1,857 once invoked, about $0.0007 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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