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/ericrisco/rsc-harness/ragnpx skills add ericrisco/rsc-harness --skill raggit clone --depth 1 https://github.com/ericrisco/rsc-harnessWrote 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/ericrisco/rsc-harness/rag)<a href="https://agentmods.dev/skills/ericrisco/rsc-harness/rag"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/rag.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 | $0.00079 | $0.02824 |
| Opus 5 | $0.00039 | $0.01412 |
| Sonnet 5 | $0.00016 | $0.00565 |
| Haiku 4.5 | $0.00008 | $0.00282 |
Grade A, and why
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 yesterday.
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 — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
rag — own the retrieve → rerank → ground → cite → refuse pipeline
You own the pipeline that turns a corpus plus a question into a grounded, cited answer: chunk, optionally contextualize, index, retrieve hybrid, rerank, assemble a grounded prompt, cite the sources, and refuse when the context does not contain the answer.
You are judged by retrieval quality and answer faithfulness, not by raw vector math. If you
find yourself tuning HNSW parameters, you wandered into the store underneath you
(../vector-db/SKILL.md). If you are comparing embedding models or chunk sizes, that is the
science beside you (embeddings-search).
The pipeline, and where each stage hands off
Each stage is a real branch — most failures live in one specific stage, and several stages delegate to a sibling skill rather than living here.
| Stage | What you do | Hands off to |
|---|---|---|
| Ingest | Get clean text out of PDFs/DOCX/HTML/OCR | you assume text exists → ../document-processing/SKILL.md |
| Chunk | Heading/semantic-aware splits with overlap, stable ids | model, dims + chunk-size science → embeddings-search |
| Contextualize | Prepend an LLM-written context blurb per chunk (optional) | stays here |
| Index | Embed + write dense vectors and a BM25/keyword index | you upsert, it owns the knobs → ../vector-db/SKILL.md |
| Retrieve | Hybrid dense + BM25, fuse with RRF, top ~150 | hybrid query mechanics → ../vector-db/SKILL.md |
| Rerank | Cross-encoder over the 150, keep top ~20 | stays here |
| Ground + cite | System prompt: answer only from context, cite chunk ids | stays here |
| Refuse | Output "I don't have enough information" on weak context | stays here |
| Evaluate | Faithfulness, answer relevancy, context precision/recall | general harness → agent-eval |
Three neighbors are not stages at all. Surfacing this answer inside a chat product (sessions,
channels, UI) is ../chatbot/SKILL.md — it calls you, not the reverse. A multi-step tool loop with
state, where retrieval is one tool among many, is ../building-agents/SKILL.md. Pulling
schema-constrained fields out of text instead of a grounded prose answer is
structured-extraction. rag is the retrieval brain those products call.
What ships with it
5 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.
- yesterday First seen · 210 lines · 79 tokens per session scan A 9162cdeb18a4
rag is a skill published in the GitHub repository ericrisco/rsc-harness (64 stars, last pushed 2d ago), licensed MIT. It adds 79 tokens to every session and 2,824 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
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pinecone:n8n
Build n8n workflows using the Pinecone Assistant node or Pinecone Vector Store node. Use when building RAG pipelines, chat-with-docs workflows, configuring Pinecone nodes in n8n, troubleshooting Pinecone n8n nodes, or asking about best practices for Pinecone in n8n.
pinecone:quickstart
Interactive Pinecone quickstart for new developers. Choose between two paths - Database (create an integrated index, upsert data, and query using Pinecone MCP + Python) or Assistant (create a Pinecone Assistant for document Q&A). Use when a user wants to get started with Pinecone for the first time or wants a guided…