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 neverinfamous/memory-journal-mcp --skill rag-pipelinesgit clone --depth 1 https://github.com/neverinfamous/memory-journal-mcpWrote 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/neverinfamous/memory-journal-mcp/rag-pipelines)<a href="https://agentmods.dev/skills/neverinfamous/memory-journal-mcp/rag-pipelines"><img src="https://agentmods.dev/badge/skills/neverinfamous/memory-journal-mcp/rag-pipelines/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/neverinfamous/memory-journal-mcp/rag-pipelines"><img src="https://agentmods.dev/badge/skills/neverinfamous/memory-journal-mcp/rag-pipelines.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 | $0.00038 | $0.00285 |
| Opus 5.5 | $0.00015 | $0.00114 |
| Sonnet 5.5 | $0.00008 | $0.00057 |
| Haiku 4.5 | $0.00004 | $0.00028 |
Grade A, and why
rag-pipelines 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 Pipelines
Core Concepts
- Chunking: Chunk by semantics or document structure, not just fixed lengths. Avoid naive character-based chunking. Use semantic chunking (splitting at sentence/paragraph boundaries) or structure-aware chunking (e.g., Markdown header splitting).
- Embeddings: Select appropriate models (e.g. text-embedding-3-small).
- Retrieval: Use hybrid search (vector + keyword) and re-ranking for optimal precision. Do not rely purely on dense vector embeddings. Combine vector search (for semantic meaning) with keyword search (BM25 for exact matches/IDs/names) using Reciprocal Rank Fusion (RRF). Always add a cross-encoder reranking step after initial retrieval. Retrieve a larger pool of documents (e.g., top-20) and rerank them to select the top-5 most relevant chunks to inject into the prompt.
- Context Injection: Carefully format retrieved chunks into the prompt for maximum LLM attention. Instruct the LLM to cite specific retrieved chunks in its response. Reject answers that rely on pre-trained knowledge if the task requires strict adherence to retrieved context.
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 · 15 lines · 38 tokens per session scan A 8fa3b803b6a9
rag-pipelines is a skill published in the GitHub repository neverinfamous/memory-journal-mcp (20 stars, last pushed 3mo ago), licensed MIT. It adds 38 tokens to every session and 285 once invoked, about $0.0002 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 2026-08-30.
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