rag-eval

rag-eval is a skill for Claude Code, Codex from LucasSantana-Dev/hitgate. It costs 0 tokens per session (1,075 once invoked), scanned A, original, MIT.

A retrieval-quality test that checks whether a recent change improved, worsened, or left unchanged the results returned from a search system. Retrieval means finding relevant documents or passages for a question.

In plain words
What is it for?
Use it to run the repository’s retrieval regression gate, compare results with a baseline, and report measures such as Hit@5 and MRR in plain language.
Why use it?
It gives you a repeatable check before committing or releasing changes to search or retrieval code. This helps catch changes that reduce result quality.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is bash hitgate/check.sh <label>.

Good fit Use it to run the repository’s retrieval regression gate, compare results with a baseline, and report measures such as Hit@5 and MRR in plain language.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/LucasSantana-Dev/hitgate
agentmods
npx agentmods add skills/lucassantana-dev/hitgate/rag-eval

Made for: Claude Code, Codex.

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 rag-eval

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/lucassantana-dev/hitgate/rag-eval"><img src="https://agentmods.dev/badge/skills/lucassantana-dev/hitgate/rag-eval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,075 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.
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 $0.00000 $0.01075
Opus 5.5 $0.00000 $0.00430
Sonnet 5.5 $0.00000 $0.00215
Haiku 4.5 $0.00000 $0.00108

Measured 1mo ago against content hash d354d22438f2, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

rag-eval 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.

skills/rag-eval/SKILL.md · 100 lines

How it starts

The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/rag-eval

Run the retrieval regression gate against the current repo state and report whether a recent change helped, hurt, or held steady.

When to invoke

  • User runs /rag-eval or /rag-eval <label>
  • User has changed files under ragcore/, hitgate/, or retrieval config and is about to commit or push
  • User asks "did this change affect retrieval quality?" or "is it safe to ship?"

Steps

1 — Determine the label

Use the argument if provided, otherwise use rolling.

2 — Run the gate

bash hitgate/check.sh <label>

Set env vars if configured for a non-default corpus or retriever (see README.md in this skill folder):

RAG_SOURCE_ROOTS="..." RAG_EVAL_DATASET="..." RAG_EVAL_BASELINE="..." EVAL_EXTRA_FLAGS="..." \
  bash hitgate/check.sh <label>

If the command exits non-zero AND no baseline file exists at the configured path, skip to the No baseline branch below.

3 — Read the structured verdict

cat hitgate/<label>.verdict.json

4 — Report in plain language

Pass (verdict: "pass"):

Gate passed. Hit@5 held [base → current]. MRR [base → current]. [Note any improvement in Hit@1 or MRR if improvements list is non-empty.]

Improvement (verdict: "improvement", refreeze_recommended: true):

Gate passed and Hit@5 improved [base → current, +Xpp]. The frozen baseline is now stale in the positive direction — consider re-freezing:

cp hitgate/<label>.json hitgate/baseline.example.json

Regression (verdict: "regression"):

Regression: [for each item in regressions, state scope + metric + delta in pp]. Next: run the eval in verbose mode to see which cases are now missing:

python -m hitgate.run --verbose --label <label>

Then inspect the MISS rows for the affected intent class.

No baseline found (baseline path does not exist):

No baseline at [path]. To create one:

python -m hitgate.run --label baseline-v1
cp hitgate/baseline-v1.json hitgate/baseline.example.json

Then re-run /rag-eval to compare against it.

Read the full file on GitHub · 100 lines

Files

What ships with it

1 file 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.

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. 1mo ago First seen · 100 lines · 0 tokens per session scan A d354d22438f2

Subscribe to this mod's changes

rag-eval is a skill published in the GitHub repository LucasSantana-Dev/hitgate (5 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,075 tokens. 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.

Related

Other skills, from other repositories

vector-databases

Vector database engineering covering Pinecone, Weaviate, Chroma, Qdrant, pgvector, and FAISS — including embedding pipeline design, HNSW index parameter tuning, hybrid dense+sparse (BM25) search, metadata filtering, namespace and tenant sharding, and RAG retrieval patterns for production knowledge systems.

LuuOW/meridian-mcp · 71 tokens

rag-expert

Design retrieval-augmented generation systems: chunking, embeddings, vector and hybrid search, reranking, grounding and evaluation. Use when the user mentions RAG, retrieval, semantic search, embeddings, vector databases, pgvector, Chroma, Qdrant, Pinecone, chunking or reranking, wants an assistant answering over…

personamanagmentlayer/pcl · 97 tokens

knowledge-ingest

Ingest or update a codebase in the agent-knowledge base. First run bootstraps the knowledge base from scratch; subsequent runs are incremental (only changed/new/deleted files reprocessed). Uses tree-sitter for zero-token structural extraction. Trigger on "/knowledge-ingest", "ingest this codebase", "load this into…

keshrath/agent-knowledge · 96 tokens

Hybrid Search Architect

Designs a hybrid retrieval pipeline combining dense vector search and BM25 sparse search with reciprocal rank fusion, and explains when to use each configuration.

Notysoty/openagentskills · 32 tokens

using-infino

How to retrieve from and write to Infino through the Infino MCP server, including choosing keyword vs semantic vs hybrid search, SQL, creating tables, adding and replacing documents, and when to use the CLI instead. Use when the user wants to search, query, recall, store, or manage data in Infino or on object storage…

infino-ai/infino-mcp · 82 tokens

ingest-knowledge

Ingest a source path into a specified collection and report file, chunk, vector, and BM25 counts.

hyh0620/mcp-knowledge-service · 27 tokens