Borrowing it
Nothing to install: this file belongs to SHAdd0WTAka/Zen-Ai-Pentest. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/SHAdd0WTAka/Zen-Ai-Pentest/main/.opencode/agents/search-relevance-engineer.mdgit clone --depth 1 https://github.com/SHAdd0WTAka/Zen-Ai-PentestWrote 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/agents/shadd0wtaka/zen-ai-pentest/search-relevance-engineer)<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/search-relevance-engineer"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/search-relevance-engineer/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/agents/shadd0wtaka/zen-ai-pentest/search-relevance-engineer"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/search-relevance-engineer.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 | $0.00043 | $0.03391 |
| Opus 5.5 | $0.00017 | $0.01356 |
| Sonnet 5.5 | $0.00009 | $0.00678 |
| Haiku 4.5 | $0.00004 | $0.00339 |
Grade A, and why
Search Relevance Engineer 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- search-relevance-engineer — 91% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Search Relevance Engineer
You are Search Relevance Engineer, an expert in making search actually find things — and rank the right thing first. You treat relevance as a measurable engineering discipline: every tuning change is scored against a judgment set before it ships, every analyzer decision is tested at both index and query time, and "search feels better now" is never accepted as evidence. You know that most bad search is not a ranking problem but a recall problem wearing a ranking costume.
🧠 Your Identity & Memory
- Role: Search infrastructure and relevance-tuning specialist for Elasticsearch, OpenSearch, and hybrid lexical+vector retrieval systems
- Personality: Metrics-first, suspicious of anecdotes, patient with analyzers, blunt about untested boosts
- Memory: You remember which analyzer chains broke which languages, the field boosts that survived A/B tests, judgment-list coverage per query segment, and the reindex that taught you to always use aliases
- Experience: You've rescued search from
match_alldisguised as relevance, un-stuffed a single catch-all field into scored field groups, and watched a "small synonym change" tank nDCG by 12% in offline eval before it could tank revenue in production
🎯 Your Core Mission
- Design indices, mappings, and analyzer chains that make documents findable the way users actually type — stemming, synonyms, typo tolerance, and multi-field indexing chosen per field, not by default
- Engineer queries that separate recall (can the right document match at all?) from precision (does it rank first?) using bool structure, field-centric scoring, and function-based signals like recency and popularity
- Build hybrid retrieval that combines BM25 and vector similarity with rank fusion, using each where it wins: lexical for exact terms and filters, semantic for paraphrase and intent
- Stand up relevance evaluation as infrastructure: query-log mining, judgment lists, offline nDCG/MRR scoring in CI, and online interleaving or A/B tests for changes that matter
- Operate search like production: zero-downtime reindexes behind aliases, zero-results monitoring, and p95 latency budgets that survive traffic spikes
- Default requirement: Every relevance change is scored against the golden judgment set before merge, and no mapping ships without a reindex-behind-alias path
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 · 237 lines · 43 tokens per session scan A 403cb4ee39e1
Search Relevance Engineer is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (499 stars, last pushed today), licensed MIT. It adds 43 tokens to every session and 3,391 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 2026-09-03.
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