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 vikasudasi/skill-vault --skill rag-pipelinegit clone --depth 1 https://github.com/vikasudasi/skill-vaultWrote 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/vikasudasi/skill-vault/rag-pipeline)<a href="https://agentmods.dev/skills/vikasudasi/skill-vault/rag-pipeline"><img src="https://agentmods.dev/badge/skills/vikasudasi/skill-vault/rag-pipeline/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/vikasudasi/skill-vault/rag-pipeline"><img src="https://agentmods.dev/badge/skills/vikasudasi/skill-vault/rag-pipeline.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 | $0.00025 | $0.00377 |
| Opus 5.5 | $0.00010 | $0.00151 |
| Sonnet 5.5 | $0.00005 | $0.00075 |
| Haiku 4.5 | $0.00003 | $0.00038 |
Grade A, and why
rag-pipeline 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 1mo 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
RAG: Retrieval-Augmented Generation
Use when an LLM should answer from your documents instead of its memorized knowledge.
Pipeline
documents -> chunk -> embed -> index
|
query -> embed -> retrieve top-k -> prompt(generated) -> LLM -> grounded answer
Chunking
- Split on semantic boundaries (headings, paragraphs), not fixed N-char blobs.
- Keep chunks ~200-500 words — enough context, not noise.
- Overlap slightly (10-20%) so a concept spanning a boundary isn't lost.
Retrieval
- Retrieve top-k (5-10) by similarity, then re-rank if the corpus is large.
- Filter by metadata (scope, tenant, trust) before final ranking (Skill Vault filters scopes + trust tiers after similarity).
Grounded generation prompt
Give the LLM only the retrieved passages + the question, and instruct it to answer from the passages, citing them — and to say it doesn't know rather than hallucinate.
Pitfalls
- If retrieval returns irrelevant chunks, no prompt fixes it — fix chunking/index first.
- Don't stuff the entire source into context; that's not RAG, that's context-dumping.
- Measure retrieval quality (recall@k) separately from answer quality.
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.
- 1mo ago First seen · 49 lines · 25 tokens per session scan A 6215b93844b0
rag-pipeline is a skill published in the GitHub repository vikasudasi/skill-vault (0 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 25 tokens to every session and 377 once invoked, about $0.0001 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.
Other skills, from other repositories
autorag-setup
Install and configure AutoRAG, register its Lite MCP server for external agents, or repair its single search model, approved roots, indexes, datasources, and health checks without exposing credentials. Use when AutoRAG or MCP is missing, init/refresh/health fails, indexes are stale, or the user wants to add folders or…
autorag-lite-setup
Install and register the model-free AutoRAG Lite MCP server, configure approved roots and datasources, build indexes, and verify MCP discovery and search. Use when autorag-mcp is missing, MCP connection fails, indexes are stale, or the user wants document search without configuring a search model.
autorag-doctor
Diagnose and repair a broken or half-working AutoRAG install so every configured source is both indexed and searchable. Checks AutoRAG, MinSync, Jikji, the embedding backend, and every CLI-backed datasource (lazykatok, discrawl, slacrawl, wacrawl, telecrawl, notcrawl, mailcrawl, rclone, Spotlight), then fixes orphan…
autorag
Search, summarize, compare, and answer questions from an already configured AutoRAG librarian over local documents and configured datasources. Use when the user asks AutoRAG to search PDFs, wikis, notes, or a knowledge base. Use autorag-setup for install, model, roots, indexing, or datasource changes.
Vector Databases
Guides retrieval-store design, indexing, and query behavior for embedding-backed systems without confusing storage with application truth.
molecular-rag
Retrieve structurally similar compounds with known properties from ChEMBL/ZINC to ground predictions and inform optimization. Based on MolRAG (Xian 2025, ACL).