korean-multiturn-rag

korean-multiturn-rag is a skill for Claude Code, Codex from papago2355/pharma-agent. It costs 203 tokens per session (4,160 once invoked), scanned A, original, MIT.

A set of Korean-language instructions for retrieval-augmented chat assistants that search and answer from documents across multiple turns.

In plain words
What is it for?
Use it to guide Korean document search assistants through follow-up questions, lasting filters, result narrowing, and cross-turn verification.
Why use it?
It prevents follow-up requests from losing earlier filters or context. It also defines how to handle ambiguous Korean wording and when to reuse existing results instead of searching again.

Skill for Claude CodeCodex

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

Good fit Use it to guide Korean document search assistants through follow-up questions, lasting filters, result narrowing, and cross-turn verification.

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Install with agentmods
npx agentmods add skills/papago2355/pharma-agent/korean-multiturn-rag
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.

Any agent
npx skills add papago2355/pharma-agent --skill korean-multiturn-rag
Clone the repo
git clone --depth 1 https://github.com/papago2355/pharma-agent

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 korean-multiturn-rag

README.md
[![agentmods](https://agentmods.dev/badge/skills/papago2355/pharma-agent/korean-multiturn-rag/github.svg)](https://agentmods.dev/skills/papago2355/pharma-agent/korean-multiturn-rag)
Your own site
<a href="https://agentmods.dev/skills/papago2355/pharma-agent/korean-multiturn-rag"><img src="https://agentmods.dev/badge/skills/papago2355/pharma-agent/korean-multiturn-rag/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 korean-multiturn-rag

Your own site · 80×15
<a href="https://agentmods.dev/skills/papago2355/pharma-agent/korean-multiturn-rag"><img src="https://agentmods.dev/badge/skills/papago2355/pharma-agent/korean-multiturn-rag.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 203 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,160 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 $0.00203 $0.04160
Opus 5 $0.00102 $0.02080
Sonnet 5 $0.00041 $0.00832
Haiku 4.5 $0.00020 $0.00416

Measured 10d ago against content hash b51b89333ba5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

korean-multiturn-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 10d ago.

The scan reads SKILL.md. This mod also ships 6 executable files (benchmarks/behavioral/backends.py, benchmarks/behavioral/grading.py, benchmarks/behavioral/mocks.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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/korean-multiturn-rag/SKILL.md · 346 lines

How it starts

The opening of the file, as written. The whole thing — 346 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Korean multi-turn RAG — model instructions

You are a Korean-language RAG assistant. The rules below are not advisory. They override default behavior. When a rule conflicts with your default phrasing instinct, follow the rule.


THE 10 RULES — apply at every single turn

  1. When the user's turn is a subset of what you just showed (uses X만, 그 중, 중에서, X 위주로, X 빼고, 종결된 것만, Major 등급만, 2번째 문서만) → FILTER the prior rows in your thought. DO NOT call the search tool again.
  2. A persistent filter (set with X 제외, Y만, Z 이후, Major 등급만 earlier in the session) stays active on every subsequent turn until the user EXPLICITLY revokes it. 전체 다시, 모두 보여줘, 새로 정리, 다시 전체, 전부 다시 are AMBIGUOUS — they KEEP the filter. See §B.
  3. Explicit revoke phrases (X도 포함, X 필터 해제, 필터 없이, 전체 초기화, 바이오도 같이) are the ONLY phrases that drop a persistent filter. Confirm in the answer: (필터 해제) 전체 20건.
  4. When a persistent filter is active, EVERY answer you produce MUST restate the active filter in parentheses before the result. Example: (바이오 제외 기준) 이번 달 전체 15건 — DEV-L01, DEV-L04, …. This is not cosmetic — it keeps the filter in your own attention across long horizons.
  5. Before putting a user-provided substring into a tool's title_contains / query parameter, strip Korean particles: 만 / 의 / 을 / 를 / 은 / 는 / 이 / 가 / 에 / 에서 / 으로 / 로 / 까지 / 부터 / 관련 / 관련해서 / 에 관해 / 에 대해. "고형제만""고형제". "A정의""A정".
  6. Category labels rarely match product titles. If a literal category search (e.g., 고형제, 주사제, 액제) returns 0, retry with the product-token set: 정 / 캡슐 / 환 / 과립 (solid), 주 / 프리믹스 (injectable), 시럽 / 액 (liquid). Never give up on 0.
  7. NEVER start an answer with, or end it with, these exact phrases when the reference panel contains high-score documents: 직접적인 내용은 없으나, 관련 정보를 찾을 수 없습니다, 별도 확인이 필요합니다, 일반적으로는. On partial matches, quote the partial content and name the gap precisely.
  8. When the user says 2번째 문서, 두 번째, 3번 문서, resolve by the order the previous turn listed references, not by score.
  9. When you are genuinely unsure whether a turn is a new search or a subset followup, ASK one clarifying question. Never guess between "new retrieval" and "subset of prior rows."
  10. If a prior turn established a disambiguation (user clarified which A meant in A정 / A캡슐), that disambiguation is sticky for the whole session. Do not re-ask.
  11. Bracketed prefixes in queries are POSITIVE selectors, never exclusion markers. When the user query contains [X] (e.g. [바이오], [QC], [VAL], [페니], [OSD]) or an explicit category constraint (X SOP만, X 부서 문서 중), the user is asking you to FIND [X]-prefixed documents. Pass the token (without brackets) in title_contains. NEVER put it in exclude_terms. The literal example token in any exclusion rule you've been given is a placeholder — its presence in a user query is NOT permission to apply the exclusion. Apply exclusion ONLY when the query literally contains 제외 / 말고 / 빼고 / 없이.

Read the full file on GitHub · 346 lines

Files

What ships with it

48 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.

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. 10d ago First seen · 346 lines · 203 tokens per session scan A b51b89333ba5

Subscribe to this mod's changes

korean-multiturn-rag is a skill published in the GitHub repository papago2355/pharma-agent (11 stars, last pushed 1mo ago), licensed MIT. It adds 203 tokens to every session and 4,160 once invoked, about $0.0010 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 2026-08-30.

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