retriever

retriever is an agent for coding agents from GovOn-Org/GovOn. It costs 23 tokens per session (238 once invoked), scanned A, original, MIT.

An agent that selects the most relevant similar cases from a collection of searchable documents. RAG means answering with information retrieved from a document collection.

In plain words
What is it for?
It is for searching complaint records, ranking candidate cases by meaning, type, recency, and answer quality, and selecting the top three examples.
Why use it?
It reduces the need to review every search result when looking for past cases related to a new complaint.

Agent

Install

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.

agentmods
npx agentmods add agents/govon-org/govon/retriever
Clone the repo
git clone --depth 1 https://github.com/GovOn-Org/GovOn

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 retriever

README.md
[![agentmods](https://agentmods.dev/badge/agents/govon-org/govon/retriever.svg)](https://agentmods.dev/agents/govon-org/govon/retriever)
Your own site
<a href="https://agentmods.dev/agents/govon-org/govon/retriever"><img src="https://agentmods.dev/badge/agents/govon-org/govon/retriever.svg" alt="Measured on agentmods" height="20"></a>
Per session 23 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 238 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00023 $0.00238
Opus 5 $0.00012 $0.00119
Sonnet 5 $0.00005 $0.00048
Haiku 4.5 $0.00002 $0.00024

Measured 4d ago against content hash 035560dcfe93, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

retriever 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.

agents/retriever.md · 23 lines

What it actually says

당신은 민원 검색 및 유사 사례 선별 전문가입니다. 벡터 검색으로 반환된 후보 문서 중 질의와 가장 관련성 높은 사례를 선별합니다.

역할

  1. 검색 쿼리에 E5 임베딩 모델 요구사항에 맞는 "query: {text}" 접두사를 적용합니다.
  2. FAISS 벡터 검색 결과에서 상위 3개 관련 사례를 선별합니다.
  3. 선별 기준: 의미적 유사성, 민원 유형 일치도, 답변 품질

선별 기준

  • 관련성: 민원 내용과 검색 결과의 의미적 유사도
  • 최신성: 최근 처리된 사례 우선
  • 답변 품질: 구체적이고 완결된 답변을 포함한 사례 우선
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. 4d ago First seen · 23 lines · 23 tokens per session scan A 035560dcfe93

Subscribe to this mod's changes

retriever is an agent published in the GitHub repository GovOn-Org/GovOn (2 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 238 once invoked, about $0.0001 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 2026-08-31.