rnd-policy-research-orchestrator

rnd-policy-research-orchestrator is a skill for Claude Code from parkjui92/policy-research-kit. It costs 412 tokens per session (7,097 once invoked), scanned A, original, MIT.

A team workflow for researching public policy and producing policy reports in Korean HWPX format. It coordinates research design, evidence gathering, writing, review, and document conversion.

In plain words
What is it for?
Use it to create detailed policy reports or shorter policy briefs from a research topic, notes, and existing materials.
Why use it?
It separates the work into stages and adds checks before writing and after drafting, helping reduce unsupported claims and unclear recommendations.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the policy-research-kit plugin — 6 skills, 5 agents shipped together

Good fit Use it to create detailed policy reports or shorter policy briefs from a research topic, notes, and existing materials.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/parkjui92/policy-research-kit/rnd-policy-research-orchestrator
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 parkjui92/policy-research-kit --skill rnd-policy-research-orchestrator
Clone the repo
git clone --depth 1 https://github.com/parkjui92/policy-research-kit

Made for: Claude Code.

Or install policy-research-kit, the plugin that ships this one along with the rest of its 6 skills, 5 agents.

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 rnd-policy-research-orchestrator

README.md
[![agentmods](https://agentmods.dev/badge/skills/parkjui92/policy-research-kit/rnd-policy-research-orchestrator/github.svg)](https://agentmods.dev/skills/parkjui92/policy-research-kit/rnd-policy-research-orchestrator)
Your own site
<a href="https://agentmods.dev/skills/parkjui92/policy-research-kit/rnd-policy-research-orchestrator"><img src="https://agentmods.dev/badge/skills/parkjui92/policy-research-kit/rnd-policy-research-orchestrator/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 rnd-policy-research-orchestrator

Your own site · 80×15
<a href="https://agentmods.dev/skills/parkjui92/policy-research-kit/rnd-policy-research-orchestrator"><img src="https://agentmods.dev/badge/skills/parkjui92/policy-research-kit/rnd-policy-research-orchestrator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 412 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 7,097 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 $0.00412 $0.07097
Opus 5 $0.00206 $0.03549
Sonnet 5 $0.00082 $0.01419
Haiku 4.5 $0.00041 $0.00710

Measured 12d ago against content hash 48d00baf3649, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

rnd-policy-research-orchestrator 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 12d 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/rnd-policy-research-orchestrator/SKILL.md · 231 lines

How it starts

The opening of the file, as written. The whole thing — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.

정책연구 오케스트레이터

실제 정책연구를 5인 에이전트 팀으로 수행해 정책연구보고서(.hwpx)를 산출한다. OmO(oh-my-openagent)의 핵심 패턴인 2단계 검증 게이트(집필 전 설계 검토 + 집필 후 초안 검수)를 이식했다.

실행 모드: 에이전트 팀

산출물 모드 — 표준 보고서 vs 단형 브리프

같은 5인 팀·같은 2단계 게이트를 쓰되, 산출물 규모에 따라 두 모드로 매개변수만 달리한다. Phase 1에서 사용자 요청·분량 단서로 모드를 판별한다.

구분 표준 보고서 모드 (기본) 단형 브리프 모드
트리거 "정책연구보고서", 분량 명시 없음·"자세히" "브리프", "이슈페이퍼", "R&D Brief", "단형", "요약", "4~8쪽", "짧게"
분량 본문 최소 50p 본문 4~8p (사용자 지정 우선)
목차 서론→이론→해외→국내→대안→로드맵→제언→참고문헌(8장) 압축 목차: ① 배경·문제제기 → ② 핵심 현황·쟁점 진단 → ③ 핵심 근거(해외·국내 요점만) → ④ 정책제언(우선순위 3~5) → (요약 박스·참고문헌)
설계 게이트 정식(reviewer 모드1 + 사용자 목차 승인) 경량 게이트: designer가 핵심 메시지·압축 목차만 설계, reviewer 모드1은 "핵심 쟁점·제언이 명확하고 근거가 확보 가능한가"만 빠르게 점검, 사용자 목차 승인 1회
조사 전면 조사(통계·문헌·해외·국내) 핵심 근거만 표적 조사(제언을 떠받칠 최소 근거 + 출처)
검수 5축 정식 검수 핵심 3축(논리·근거/출처·정책타당성) 압축 검수
참고문헌 정식 일괄 정리 핵심 출처만 간결 정리(본문 인용 1:1 유지)

핵심 원칙(공통): 브리프 모드도 "근거 없는 주장 금지·출처 병기·제언은 실행 수준"은 그대로 지킨다. 짧다고 근거를 빼지 않는다 — 분량만 줄이고 밀도는 유지한다. 브리프는 결정자가 5분 안에 핵심과 제언을 잡도록 두괄식·요약 박스 우선으로 쓴다.

아래 워크플로우는 표준 모드 기준이며, 브리프 모드는 위 표대로 각 Phase의 분량·목차·게이트·검수 축을 경량화해 적용한다(에이전트 프롬프트에 "브리프 모드: 본문 N쪽, 압축 목차" 명시).

실행 프로파일 — 표준 vs 쾌속 (성능 폴백 장치)

산출물 모드(무엇을 낼 것인가)와 별개로 **실행 프로파일(얼마나 빨리 갈 것인가)**을 정한다. 무거운 세션 모델(opus급)로 전 단계를 돌리면 표준 보고서 한 편에 수 시간이 걸릴 수 있다. 이를 다루는 장치가 네 겹이다: ①단계별 모델 티어(상시 기본값) ②쾌속 프로파일(요청 시) ③지연 폴백 래더(런타임 감지) ④실패 시 업시프트(품질 역방향).

① 단계별 모델 티어 (상시 기본값)

단계 에이전트 기본 모델 근거
설계·집필·검수 designer / writer / reviewer inherit (세션 모델) 품질 결정 단계 — 기본값을 낮추지 않는다
근거조사 investigator sonnet 검색·수집 중심(I/O 병목). 출처는 뒤의 검수 게이트가 재검증
hwpx 변환 hwpx-exporter haiku 절차화된 기계적 변환 + 자체 정량 검증 스위트 보유

품질 우선이면 스폰 시 상위 모델을 명시해 되돌린다(예: 근거 판별이 까다로운 주제의 investigator). reviewer는 어떤 프로파일·래더 단계에서도 모델을 낮추지 않는다 — 검증 게이트가 이 킷의 존재 이유다.

② 쾌속 프로파일

트리거: 사용자의 속도 단서("빨리", "쾌속", "시간 없어", "오늘까지" 등) 또는 폴백 래더 4단계에서 사용자가 선택. 산출물 규모와 게이트 구조는 유지하고 실행 방식만 경량화한다:

Read the full file on GitHub · 231 lines

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. 12d ago First seen · 231 lines · 412 tokens per session scan A 48d00baf3649

Subscribe to this mod's changes

rnd-policy-research-orchestrator is a skill published in the GitHub repository parkjui92/policy-research-kit (9 stars, last pushed 1mo ago), licensed MIT. It adds 412 tokens to every session and 7,097 once invoked, about $0.0021 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.

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