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/lucassantana-dev/hitgate/rag-evalnpx skills add LucasSantana-Dev/hitgate --skill rag-evalgit clone --depth 1 https://github.com/LucasSantana-Dev/hitgateWrote 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/lucassantana-dev/hitgate/rag-eval)<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="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 | $0.00000 | $0.01075 |
| Opus 5 | $0.00000 | $0.00537 |
| Sonnet 5 | $0.00000 | $0.00215 |
| Haiku 4.5 | $0.00000 | $0.00108 |
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 4d 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.
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-evalor/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
improvementslist 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.jsonThen re-run
/rag-evalto compare against it.
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.
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.
- 4d ago First seen · 100 lines · 0 tokens per session scan A d354d22438f2
rag-eval is a skill published in the GitHub repository LucasSantana-Dev/hitgate (4 stars, last pushed 22d ago), 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.
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