agentmods.dev

journey-rag

journey-rag is a skill for Claude Code from butterbase-ai/butterbase-skills. It costs 58 tokens per session (558 once invoked), scanned A, original, MIT.

A setup step for a Butterbase knowledge base that stores documents so an application can search them and use their content in answers. RAG, or retrieval-augmented generation, means finding relevant stored text before generating a response.

In plain words
What is it for?
It helps create document collections, ingest initial source files, confirm the planned RAG setup, and run a search test to check that the stored content can be retrieved.
Why use it?
It turns the knowledge-base portion of an approved Butterbase plan into collections and ingested documents, with checks before and after setup.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the butterbase-skills plugin — 23 skills, 33 commands shipped together

not rated 533repo 2mo ago A scan SkillSpector: pass 58 tokens original MIT

Good fit It helps create document collections, ingest initial source files, confirm the planned RAG setup, and run a search test to check that the stored content can be retrieved.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/butterbase-ai/butterbase-skills/journey-rag
View source ↗ butterbase-ai/butterbase-skills
Install

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.

Any agent
npx skills add butterbase-ai/butterbase-skills --skill journey-rag
Clone the repo
git clone --depth 1 https://github.com/butterbase-ai/butterbase-skills

Made for: Claude Code.

Or install butterbase-skills, the plugin that ships this one along with the rest of its 23 skills, 33 commands.

Wrote 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.

agentmods badge for journey-rag

README.md
[![agentmods](https://agentmods.dev/badge/skills/butterbase-ai/butterbase-skills/journey-rag/github.svg)](https://agentmods.dev/skills/butterbase-ai/butterbase-skills/journey-rag)
Your own site
<a href="https://agentmods.dev/skills/butterbase-ai/butterbase-skills/journey-rag"><img src="https://agentmods.dev/badge/skills/butterbase-ai/butterbase-skills/journey-rag/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.

agentmods 80×15 button for journey-rag

Your own site · 80×15
<a href="https://agentmods.dev/skills/butterbase-ai/butterbase-skills/journey-rag"><img src="https://agentmods.dev/badge/skills/butterbase-ai/butterbase-skills/journey-rag.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 58 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 558 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00058 $0.00558
Opus 5 $0.00029 $0.00279
Sonnet 5 $0.00012 $0.00112
Haiku 4.5 $0.00006 $0.00056

Measured 13d ago against content hash be89b7276721, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

journey-rag 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 13d 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.

skills/journey-rag/SKILL.md · 46 lines

What it actually says

Journey: RAG

Stage 3g of the guided journey. Create RAG collections and ingest initial documents.

When to use

  • Dispatched by journey when current_stage: rag.
  • Directly via /butterbase-skills:journey-rag.
  • Skipped (annotated (n/a)) if the plan has no RAG section.

Preflight

If docs/butterbase/03-preflight.md is missing, older than 24 hours, or 00-state.md has app_id: null, invoke butterbase-skills:journey-preflight first. Wait for it to return successfully before proceeding.

Inputs

  • docs/butterbase/02-plan.md — the RAG section.
  • docs/butterbase/00-state.md — for app_id.

Procedure

  1. Refresh docs. Call butterbase_docs with topic: "rag". For ingestion + retrieval patterns, also WebFetch https://docs.butterbase.ai/ai/rag. Skip if cache is fresh.

  2. Read the RAG section. Print it back: "About to set up RAG: collections=<list>. Proceed?". Wait for yes.

  3. Invoke butterbase-skills:rag-dev via the Skill tool with the RAG plan and app_id. The wrapped skill calls manage_rag_content action: create_collection, then ingest_document for any seed sources the user provides.

  4. Smoke: call rag_query with a representative question and show the user the top hit.

  5. Append one line to docs/butterbase/04-build-log.md: <ISO timestamp> rag manage_rag_content ok

  6. Tick - [x] rag in 00-state.md, set current_stage: to the next unchecked stage.

  7. Return to journey orchestrator (or ask "Continue to the next stage? (yes/no)").

Outputs

  • Created RAG collections, ingested seed docs.
  • One line in 04-build-log.md.

Anti-patterns

  • ❌ Ingesting huge document sets in a single call — chunk and poll get_document_status.
  • ❌ Skipping the smoke rag_query — silent embedding failures are common.
Changes

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.

  1. 13d ago First seen · 46 lines · 58 tokens per session scan A be89b7276721

Subscribe to this mod's changes

journey-rag is a skill published in the GitHub repository butterbase-ai/butterbase-skills (533 stars, last pushed 2mo ago), licensed MIT. It adds 58 tokens to every session and 558 once invoked, about $0.0003 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 2026-08-30.

Claude CodeData and AIRAG and vectorsagent-skillsai-agentsbackend-as-a-serviceclaudemcp
Related

Other skills, from other repositories

embeddings

Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.

ruvnet/ruflo · 62 tokens

agent-platform-rag-engine-management

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…

google/skills · 85 tokens

llm-app-patterns

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

davila7/claude-code-templates · 54 tokens

9router-embeddings

Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.

decolua/9router · 66 tokens

azure-search-documents-dotnet

Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: "Azure Search .NET"…

microsoft/skills · 102 tokens

browserwing-admin

Manage and operate BrowserWing — an intelligent browser automation platform. Install dependencies, configure LLM, create/manage/execute automation scripts, use AI-driven exploration to generate scripts, browse the script marketplace, and troubleshoot issues.

MemTensor/MemOS · 47 tokens