rn-harness-research

rn-harness-research is a skill for Claude Code from tjdrhs90/rn-launch-harness. It costs 23 tokens per session (1,825 once invoked), scanned A, original, MIT.

A research step for finding or assessing a mobile-app idea through app-store rankings and competitor reviews. It examines popular apps, user complaints, possible gaps, ways to earn money, and technical fit for React Native and Expo.

In plain words
What is it for?
Use it to study a proposed app category, compare competing apps, identify unmet needs, outline an initial feature set, and check whether the idea can be built with the planned tools.
Why use it?
It reduces the risk of building an app without understanding its users, alternatives, or practical requirements. If the idea is unclear, it can also guide idea discovery from current store trends.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: names the AskUserQuestion tool.

Part of the rn-launch-harness plugin — 17 skills shipped together

Good fit Use it to study a proposed app category, compare competing apps, identify unmet needs, outline an initial feature set, and check whether the idea can be built with the planned tools.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tjdrhs90/rn-launch-harness/rn-harness-research
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 tjdrhs90/rn-launch-harness --skill rn-harness-research
Clone the repo
git clone --depth 1 https://github.com/tjdrhs90/rn-launch-harness

Made for: Claude Code.

Or install rn-launch-harness, the plugin that ships this one along with the rest of its 17 skills.

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 rn-harness-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/tjdrhs90/rn-launch-harness/rn-harness-research/github.svg)](https://agentmods.dev/skills/tjdrhs90/rn-launch-harness/rn-harness-research)
Your own site
<a href="https://agentmods.dev/skills/tjdrhs90/rn-launch-harness/rn-harness-research"><img src="https://agentmods.dev/badge/skills/tjdrhs90/rn-launch-harness/rn-harness-research/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 rn-harness-research

Your own site · 80×15
<a href="https://agentmods.dev/skills/tjdrhs90/rn-launch-harness/rn-harness-research"><img src="https://agentmods.dev/badge/skills/tjdrhs90/rn-launch-harness/rn-harness-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,825 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00023 $0.01825
Opus 5 $0.00012 $0.00912
Sonnet 5 $0.00005 $0.00365
Haiku 4.5 $0.00002 $0.00183

Measured 11d ago against content hash 3f1700986b71, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

rn-harness-research 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 11d 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/rn-harness-research/SKILL.md · 233 lines

How it starts

The opening of the file, as written. The whole thing — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.

rn-harness-research — Phase 1: 시장 조사 & 아이디어 발굴

아이디어가 있으면 시장을 조사하고, 없으면 직접 찾아서 제안한다.

Trigger

오케스트레이터(/rn-harness)에서 Phase 1으로 호출됨.

Input

  • docs/harness/config.mdapp_idea
  • docs/harness/references/ (있으면)

Process

Step 0: 모드 판별

app_idea를 확인:

  • 구체적 아이디어 (예: "커피 구독 앱") → Mode A: 시장 조사
  • 빈 값 / 모호한 요청 (예: "뭐 만들까", "알아서", "") → Mode B: 아이디어 발굴

Mode A: 시장 조사 (아이디어가 있을 때)

Step 1: 아이디어 분석

app_idea를 읽고 다음을 파악:

  • 핵심 도메인 (건강, 금융, 교육, 커머스 등)
  • 타겟 사용자층
  • 핵심 가치 제안

Step 2: 시장 조사

WebSearch를 사용하여:

  1. 카테고리 트렌드: 해당 카테고리 App Store/Google Play 인기 앱 조사
  2. 경쟁 앱 분석: 상위 5~10개 경쟁 앱 분석
    • 앱 이름, 평점, 주요 기능, 강점, 약점
    • 유저 리뷰에서 반복되는 불만/요청 사항
  3. 시장 기회: 경쟁 앱이 놓치고 있는 영역
  4. 수익화 모델: 해당 카테고리에서 흔한 수익 모델 (광고, 구독, 인앱결제)

Step 3: 차별화 전략 수립

경쟁 분석 기반으로:

  • 핵심 차별점 3가지
  • MVP 핵심 기능 리스트
  • "이 앱을 쓰는 이유" 한 줄 정의

Step 4: 기술 타당성 검토

React Native + Expo로 구현 가능한지:

  • 필요한 네이티브 모듈 확인
  • Expo SDK 지원 여부
  • 서드파티 라이브러리 존재 여부
  • 백엔드 요구사항 (Firebase, Supabase 등)

Step 10: 사용자 확인으로 이동


Mode B: 아이디어 발굴 (아이디어가 없을 때)

Step 5: 스토어 탑 차트 조사

WebSearch로 현재 인기 앱 트렌드 분석:

  1. App Store 카테고리별 Top 100 조사

    • 생산성, 건강/피트니스, 금융, 교육, 라이프스타일, 유틸리티
    • 최근 급상승한 앱, 새로 출시된 앱
    • 한국 스토어 + 미국 스토어 동시 조사
  2. Google Play 인기 차트 조사

    • 무료 인기, 유료 인기, 인기 급상승
    • 카테고리별 트렌드
  3. 최근 트렌드 키워드

    • "2026 앱 트렌드", "인기 앱 카테고리"
    • ProductHunt, TechCrunch 최신 앱 소식

Step 6: 1인 개발 필터링

발견한 트렌드/아이디어를 다음 기준으로 필터링:

기준 조건
1인 개발 가능 2주 이내 MVP 구현 가능한 범위
서버 불필요 로컬 저장 (AsyncStorage/SQLite) 또는 Firebase/Supabase 무료 티어
네이티브 모듈 최소 Expo SDK로 대부분 커버 가능
수익화 가능 AdMob 광고 또는 간단한 인앱결제로 수익 가능
경쟁 강도 대형 기업이 독점하지 않는 틈새 영역
유지보수 부담 낮음 콘텐츠 업데이트/서버 관리 불필요

Step 7: 아이디어 후보 생성

3~5개 아이디어를 구체적으로 제안:

### 아이디어 1: [앱 이름]
- **한 줄 소개**: ...
- **타겟**: ...
- **핵심 기능 3가지**: 
  1. ...
  2. ...
  3. ...
- **차별점**: 기존 [경쟁앱]은 [약점]이 있는데 이 앱은 [강점]
- **수익화**: AdMob 배너 + 전면 광고 (예상 DAU X명 기준 월 $Y)
- **기술 타당성**: Expo SDK 커버 / 서버 불필요 / 예상 개발 기간 N일
- **시장 근거**: [탑차트 앱]의 리뷰에서 [불만]이 반복됨 → 기회

Read the full file on GitHub · 233 lines

Files

What ships with it

1 file 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. 11d ago First seen · 233 lines · 23 tokens per session scan A 3f1700986b71

Subscribe to this mod's changes

rn-harness-research is a skill published in the GitHub repository tjdrhs90/rn-launch-harness (9 stars, last pushed 9d ago), licensed MIT. It adds 23 tokens to every session and 1,825 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.

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