Borrowing it
Nothing to install: this file belongs to Querry-AI/Runnywhere-PlayMCP. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Querry-AI/Runnywhere-PlayMCP/main/CLAUDE.mdgit clone --depth 1 https://github.com/Querry-AI/Runnywhere-PlayMCPWrote 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.
[](https://agentmods.dev/instructions/querry-ai/runnywhere-playmcp/claude-md)<a href="https://agentmods.dev/instructions/querry-ai/runnywhere-playmcp/claude-md"><img src="https://agentmods.dev/badge/instructions/querry-ai/runnywhere-playmcp/claude-md/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.
<a href="https://agentmods.dev/instructions/querry-ai/runnywhere-playmcp/claude-md"><img src="https://agentmods.dev/badge/instructions/querry-ai/runnywhere-playmcp/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.06453 | $0.06453 |
| Opus 5 | $0.03227 | $0.03227 |
| Sonnet 5 | $0.01291 | $0.01291 |
| Haiku 4.5 | $0.00645 | $0.00645 |
Grade A, and why
Runnywhere-PlayMCP CLAUDE.md 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.
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
프로젝트
Runnywhere(러니웨어) — 서울 보행 도로망 위에 러닝 코스와 동물 모양 GPS 아트 코스를 생성하는 MCP 서버. 카카오 PlayMCP 공모전 출품작이며, PlayMCP 심사 기준(stateless, p99 ≤ 3s, avg ≤ 100ms)이 설계 전반을 지배한다. 사용자 대면 텍스트는 전부 한국어, 코드 주석은 영어다. README.md에 데이터 스냅숏·환경변수 전체 목록이 있다.
명령어
source .venv/bin/activate && pip install -e '.[dev]'
python -m runart.server # http://localhost:8000/mcp (Streamable HTTP, JSON)
pytest # 전체 (실제 58MB 그래프 로드 — 첫 테스트가 느림)
pytest 'tests/test_shapes.py::test_shape_course_passes_similarity_gate[dog]' -q # 단일 테스트
pytest -k "zigzag or detour" -q # 이름으로 골라 실행
python scripts/loadtest.py 1000 10 # 서버 구동 상태에서 부하 테스트
RUNART_LOADTEST_REPORT=artifacts/playmcp-loadtest.json python scripts/loadtest.py 1000 10
docker build -t runnywhere . && docker run -p 8000:8000 -e RUNART_BASE_URL=https://<endpoint> runnywhere
ETL(그래프·RFS·프리셋 재생성)은 pip install -e '.[etl]' 후 README.md "실행" 절의 순서를 그대로 따른다. 오래 걸리고 결과물이 코드의 체크섬과 묶여 있으므로, 요청받지 않았다면 실행하지 않는다.
아키텍처
의존 방향은 단방향이다: geo → graph/rfs/facilities/infrastructure → course → shapes → server. render·widget·insights·exploration은 표현 계층이고 models는 전역 공유된다. 역방향 import를 만들지 말 것.
server.py— MCP 툴 9개 + 웹 라우트 17개를 한 앱에서 서빙. FastMCPstateless_http=True, json_response=True.course.py— 목표 거리의 원주에 waypoint 4개를 놓고 RFS 가중 Dijkstra로 잇는 순환 코스. 여러 bearing을 시도하고 ±5% 안에 들 때까지 원을 rescale. 후보 순위에는 RFS·경사·따라가기 쉬움과 함께retrace_share(같은 길 되돌아오는 비율)가 들어간다 — 공원 보행망은 격자가 아니라 나무 구조라 이게 없으면 왕복 코스가 뽑힌다.shapes.py— 동물 템플릿(닫힌 폴리라인)을 스케일·회전·스냅한 뒤 유사도 게이트로 판정. 게이트를 못 넘으면 나쁜 코스를 내보내는 대신 거절하고 대안을 제안한다. 게이트 값은 동물마다 다르다(SHAPE_STYLES).rfs.py— 러닝 친화도 점수(6개 항목 가중합) + 라우팅 가중치.night_mode는 별도 프로파일,include_hills는 경사 항을 감점에서 가점으로 뒤집는다.insights.py— 코스가 스스로에 대해 말하는 것(특징 칩·좋은 점·참고할 점). 파생 문장은 전부 코스가 이미 가진 데이터(RFS 성분·누적 오르막·횡단 수·편의시설)에서만 만든다. 성분 임계값은 서울 전역 실측 중앙값 기준이라 절대 수치가 아니다 — 조명은 도시 전체가 무데이터 기본값 0.5 아래라, 고정 컷오프를 쓰면 "조명 어두움"이 네 코스 중 셋에 붙는다.etl/— 오프라인 파이프라인. 런타임에는 경로·안전·시설 데이터를 위해 외부 API를 절대 호출하지 않는다(지오코딩만 예외).
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.
- 11d ago First seen · 140 lines · 6,453 tokens per session scan A d5fb0bf02c6f
Runnywhere-PlayMCP CLAUDE.md is an instructions file published in the GitHub repository Querry-AI/Runnywhere-PlayMCP (0 stars, last pushed 2d ago), licensed MIT. It adds 6,453 tokens to every session, about $0.0323 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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