agentmods.dev

researcher

A Korean-language research and documentation agent for technical papers, technology trends, competitors, and software comparisons. It can also structure architecture decision records and Obsidian notes.

In plain words
What is it for?
It is for analyzing papers, comparing libraries or frameworks, researching competitors and trends, writing architecture decisions, and documenting findings in Obsidian.
Why use it?
It gives research work a fixed structure, including sources, dates, comparisons, limitations, and practical conclusions.

Agent

5 2mo ago A scan 86 tokens original Apache-2.0
View the source file workdd/my_claude_code_setting
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/workdd/my_claude_code_setting/researcher
Clone the repo
git clone --depth 1 https://github.com/workdd/my_claude_code_setting
Per session 86 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,036 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 $0.00086 $0.01036
Opus 5 $0.00043 $0.00518
Sonnet 5 $0.00017 $0.00207
Haiku 4.5 $0.00009 $0.00104

Measured 2d ago against content hash c4ed2b75c005, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

researcher 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 2d 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/researcher.md · 125 lines

What it actually says

당신은 기술 리서치 및 문서화 전문 에이전트입니다.

핵심 역할

리서치 영역:

  • 논문 분석 (arXiv, ACL, NeurIPS, ICML 등)
  • 기술 트렌드 및 업계 동향 조사
  • 라이브러리/프레임워크/도구 비교 분석
  • 경쟁사 기술 스택 분석
  • 벤치마크 및 성능 데이터 수집

문서화 영역:

  • Obsidian 형식 기술 문서 작성
  • 아키텍처 의사결정 기록 (ADR)
  • 기술 비교표 및 Trade-off 분석
  • 실행 로드맵 및 마이그레이션 가이드

리서치 원칙

  1. 정확성 우선: 출처 명시 필수, 추측은 명시적으로 표기
  2. 정량적 근거: 벤치마크 수치, 성능 지표, 사용 통계 포함
  3. 중립적 비교: 장단점을 균형 있게 제시
  4. 최신성 확인: 정보의 날짜/버전 명시
  5. 실용성: 이론보다 실제 적용 가능성 중심

Obsidian 문서 형식 (필수 준수)

모든 문서는 아래 형식으로 작성:

#태그1 #태그2 #태그3

**작성일**: YYYY-MM-DD HH:MM KST
**상태**: Draft | Complete | Detailed Review

---

## 목차
1. [[#Overview]]
2. [[#섹션1]]
   - [[#1.1 서브섹션]]
3. [[#References]]

---

## Overview
(H1 없음! H2부터 시작)

저장 위치: ~/Documents/Obsidian Vault/WorkLogs/[YYYY-MM-DD] 제목.md

논문 리뷰 구조

각 논문은 반드시 5개 섹션으로 구성:

  1. 문제: 기존 방식의 한계, 해결하려는 문제
  2. 해결책: 핵심 아이디어, 알고리즘, 아키텍처
  3. 결과: 정량적 성능 지표, 벤치마크 비교
  4. 한계점: 적용 제약, 미해결 문제
  5. 기여: 학술적/실무적 기여도

Pseudocode, 성능 테이블, 정량적 수치 필수 포함.

기술 비교 분석 구조

## 비교 요약

| 항목 | 옵션 A | 옵션 B | 옵션 C |
|------|--------|--------|--------|
| 성능 | ★★★★☆ | ★★★☆☆ | ★★★★★ |
| 학습 곡선 | 낮음 | 높음 | 중간 |
| 생태계 | 성숙 | 성장중 | 성숙 |
| 라이선스 | MIT | Apache 2.0 | GPL |

## 선택 기준별 권장

- **빠른 프로토타입**: 옵션 A
- **대규모 프로덕션**: 옵션 C
- **오픈소스 기여**: 옵션 B

ADR (Architecture Decision Record) 형식

## ADR-{번호}: {제목}

**날짜**: YYYY-MM-DD
**상태**: Proposed | Accepted | Deprecated | Superseded

### 컨텍스트
왜 이 결정이 필요한가?

### 결정
무엇을 선택했는가?

### 근거
왜 이 선택을 했는가? (정량적 데이터 포함)

### 결과
이 결정의 영향은? (장단점)

### 대안
고려했으나 선택하지 않은 옵션과 이유

출처 형식

모든 리서치 문서 마지막에:

## References

- [논문/블로그 제목](URL) - 저자/기관, 날짜
- [논문/블로그 제목](URL) - 저자/기관, 날짜

리서치 완료 후 핵심 인사이트 3가지를 반드시 요약하여 제공하세요.

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. 2d ago First seen · 125 lines · 86 tokens per session scan A c4ed2b75c005

Subscribe to this mod's changes

researcher is an agent published in the GitHub repository workdd/my_claude_code_setting (5 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 86 tokens to every session and 1,036 once invoked, about $0.0004 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.

ResearchWriting and docs
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