mirror of
https://github.com/NomaDamas/k-skill.git
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* feat(srt-booking): SRT 좌석 확인과 탐색 우선순위 개선 (#305)
* feat(srt): 좌석 조회와 탐색 우선순위 추가
SRT search 결과의 stable train_id로 객차별 좌석을 조회하고, 특정 호차/좌석 확인과 탐색 우선순위 옵션을 제공한다.
Constraint: SRT와 KTX는 별도 upstream 표면이므로 SRT HTML 파서와 테스트를 분리함
Rejected: KTX 좌석 helper 공유 | Korail API와 SRT 웹 좌석선택 HTML 계약이 달라 혼용하면 파서 안정성이 낮아짐
Confidence: medium
Scope-risk: moderate
Directive: SRT 좌석선택 HTML에서 노출되지 않는 속성은 추정하지 말고 명시적으로 처리할 것
Tested: PYTHONPATH=.:scripts python3 -m unittest scripts.test_srt_booking scripts.test_ktx_booking; python3 -m py_compile scripts/srt_booking.py scripts/srt_seats.py scripts/test_srt_booking.py
Not-tested: 실제 예약 API에 우선순위 좌석 선택을 연결하는 흐름
* fix(srt): 좌석 조회 JSON 출력 안정화
SRT 대기열 메시지가 stdout에 섞여 seats JSON을 깨는 실제 표면 문제를 막고, 누락된 좌석 방향/위치 속성을 unknown으로 정규화한다.
Constraint: issue #303 범위는 예약 부작용이 없는 좌석 조회 보조 흐름으로 제한됨
Rejected: 실제 예약 subcommand 추가 | 좌석 선점/예약은 외부 부작용이라 이번 acceptance criteria에 포함되지 않음
Confidence: high
Scope-risk: narrow
Directive: SRTrain upstream 출력이 추가되더라도 helper stdout은 JSON 전용으로 유지할 것
Tested: RED→GREEN in .omo/ulw-loop/evidence/srt-c002-red-green-tests.txt; live SRT tmux QA in .omo/ulw-loop/evidence/srt-c001-live-search-seats.txt; npm run ci in .omo/ulw-loop/evidence/srt-c003-regression-ci.txt
Not-tested: 실제 예약/결제/취소 부작용 흐름
* test(srt): split seat helper regression coverage
---------
Co-authored-by: Jeffrey (Dongkyu) Kim <vkehfdl1@gmail.com>
* feat: add korean-humanizer skill
AI가 쓴 티가 나는 한국어 글을 자연스러운 사람 글로 고치는 프롬프트 기반 스킬.
blader/humanizer의 구조·방법론(패턴 카탈로그 + draft→audit→final 루프 +
false positive 가이드)을 한국어에 맞게 재구성했다.
- 한국어 특화 33개 패턴: 번역체(직역 조사·무생물 주어·"~들"·"가지다"·이중피동·
명사화), AI 상투어, 3의 법칙, 과장된 의의 부여, 마무리 상투구, 챗봇 잔재,
줄표·가운뎃점·곡선따옴표 등
- Triage(최소 개입) 원칙: 서식만 문제면 산문은 그대로 두어 과교정 방지
- Length control: 목표 글자수 지정 시 ±5% 내로 맞추고 공백 포함/제외 수치 보고,
korean-character-count 스킬과 연동
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* feat(korean-humanizer): rebuild v2 on im-not-ai framework
Build on happy-nut's PR #311 korean-humanizer skill (cherry-picked,
authorship preserved) by re-centering it on the epoko77-ai/im-not-ai
(Humanize KR, MIT) methodology:
- 4대 철칙 (의미 불변 · 근거 기반 · 장르 유지 · 과윤문 금지 30%/50% 가드)
- S1/S2/S3 severity tiers and A~D quality grades
- A~J taxonomy with Korean-specific patterns (A-16 그/그녀 강박,
A-18 관계절 좌향 수식, A-19 이중 조사, C-11 연결어미 뒤 쉼표, E-7 경어법)
- detect -> rewrite -> audit -> grade loop with self-check checklist
- references/ai-tell-taxonomy.md full A~J table
- docs/features/korean-humanizer.md crediting im-not-ai and happy-nut
- README row + link, regenerated plugin.json, docs regression test
Co-authored-by: happy-nut <happynut.dev@gmail.com>
* docs(korean-law-search): document official precedent API evidence (#313)
Enhance the existing korean-law-search skill and feature doc with the
official 법제처 Open API precedent endpoints and detail retrieval, without
adding a new skill, package, workspace, or changeset.
- Document 판례 목록 조회 (lawSearch.do?target=prec) and 판례 본문 조회
(lawService.do?target=prec&ID=...) as official evidence behind the
korean-law-mcp search_precedents/get_precedent_text path.
- Add supported precedent filters (query, court, case number, source
name, date, sort) and precedent-specific failure modes (missing LAW_OC,
upstream unavailable/rate-limit/timeout, empty results, body
unavailable for some sources) plus the legal-advice boundary.
- Keep korean-law-mcp first and Beopmang as the only post-failure
fallback; lawService.do?target=prec is official detail retrieval, not a
Beopmang-style fallback.
- Extend the skill-docs regression test with stable endpoint/tool
literals and concept-level filter/failure-mode/legal-boundary checks.
Closes #308
* feat(toss-securities): add official read-only OpenAPI client (#312)
Add an official Toss Securities Open API client alongside the existing
unofficial tossctl wrapper. The package ships read-only helpers backed by
the official REST API (https://openapi.tossinvest.com): OAuth2
client_credentials token issuance with an in-memory token cache, bearer +
X-Tossinvest-Account header handling, TossApiError/TossCredentialsError
with secret/token redaction, and 429 Retry-After/backoff retry.
Credentials are read from TOSSINVEST_CLIENT_ID/TOSSINVEST_CLIENT_SECRET
(optional TOSSINVEST_ACCOUNT/TOSSINVEST_API_BASE_URL) and sent directly to
Toss, never through a shared proxy. Order mutation remains out of scope;
the tossctl path is retained as a documented fallback.
Closes #306
* Revert "docs(korean-law-search): document official precedent API evidence (#313)"
This reverts commit 5faec8bb2a.
* feat(k-skill-proxy): fold Korean law lookups into k-skill-proxy, drop Beopmang (#315)
Add hosted korean-law proxy routes and make the korean-law-search skill
proxy-first, removing the unstable Beopmang fallback from the support list.
- proxy: new src/korean-law.js wrapping official 법제처 DRF lawSearch.do /
lawService.do, injecting LAW_OC + browser User-Agent/Referer (the real
cause of "사용자 정보 검증 실패") and retrying empty/HTML responses.
- proxy: /v1/korean-law/search and /v1/korean-law/detail routes + lawOc
config + koreanLawConfigured health flag; 17 module + 6 route tests.
- skill/docs: korean-law-search becomes proxy-first (no per-user LAW_OC,
no local CLI). Drop Beopmang everywhere; credit chrisryugj/korean-law-mcp
as design reference and 법제처 open.law.go.kr as official source.
- ops: LAW_OC added to deploy doc KEYS, secret accessor loop, and the
Cloud Run deploy workflow set-secrets.
- changeset: k-skill-proxy minor.
---------
Co-authored-by: iamiks <rmstjr1030@naver.com>
Co-authored-by: happy-nut <happynut.dev@gmail.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
147 lines
5 KiB
YAML
147 lines
5 KiB
YAML
name: Deploy k-skill-proxy to Cloud Run
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# Live: https://k-skill-proxy.nomadamas.org
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# GCP project: k-skill-proxy, region: asia-northeast1
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# Auth: Workload Identity Federation. Setup: docs/deploy-k-skill-proxy.md
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on:
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push:
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branches: [main]
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workflow_dispatch: {}
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permissions:
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contents: read
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id-token: write
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concurrency:
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group: deploy-k-skill-proxy
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cancel-in-progress: false
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env:
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GCP_PROJECT_ID: k-skill-proxy
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GCP_REGION: asia-northeast1
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AR_REPO: k-skill
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SERVICE_NAME: k-skill-proxy
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IMAGE_NAME: k-skill-proxy
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jobs:
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deploy:
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name: Build and deploy
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v5
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- name: Authenticate to Google Cloud (Workload Identity Federation)
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id: auth
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uses: google-github-actions/auth@v3
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with:
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project_id: ${{ env.GCP_PROJECT_ID }}
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workload_identity_provider: ${{ secrets.GCP_WIF_PROVIDER }}
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service_account: ${{ secrets.GCP_DEPLOY_SERVICE_ACCOUNT }}
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token_format: access_token
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- name: Set up gcloud CLI
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uses: google-github-actions/setup-gcloud@v3
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with:
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project_id: ${{ env.GCP_PROJECT_ID }}
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- name: Configure Docker for Artifact Registry
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run: gcloud auth configure-docker ${{ env.GCP_REGION }}-docker.pkg.dev --quiet
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- name: Resolve image URI
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id: image
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run: |
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IMAGE_URI="${GCP_REGION}-docker.pkg.dev/${GCP_PROJECT_ID}/${AR_REPO}/${IMAGE_NAME}:${GITHUB_SHA}"
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echo "uri=${IMAGE_URI}" >> "$GITHUB_OUTPUT"
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echo "Image: ${IMAGE_URI}"
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- name: Build container image
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run: |
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docker build \
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--tag "${{ steps.image.outputs.uri }}" \
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--file packages/k-skill-proxy/Dockerfile \
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.
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- name: Push image to Artifact Registry
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run: docker push "${{ steps.image.outputs.uri }}"
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- name: Deploy to Cloud Run
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id: deploy
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uses: google-github-actions/deploy-cloudrun@v3
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with:
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service: ${{ env.SERVICE_NAME }}
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region: ${{ env.GCP_REGION }}
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image: ${{ steps.image.outputs.uri }}
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secrets: |-
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AIR_KOREA_OPEN_API_KEY=AIR_KOREA_OPEN_API_KEY:latest
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KMA_OPEN_API_KEY=KMA_OPEN_API_KEY:latest
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SEOUL_OPEN_API_KEY=SEOUL_OPEN_API_KEY:latest
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HRFCO_OPEN_API_KEY=HRFCO_OPEN_API_KEY:latest
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OPINET_API_KEY=OPINET_API_KEY:latest
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DATA_GO_KR_API_KEY=DATA_GO_KR_API_KEY:latest
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KEDU_INFO_KEY=KEDU_INFO_KEY:latest
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DATA4LIBRARY_AUTH_KEY=DATA4LIBRARY_AUTH_KEY:latest
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FOODSAFETYKOREA_API_KEY=FOODSAFETYKOREA_API_KEY:latest
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KAKAO_REST_API_KEY=KAKAO_REST_API_KEY:latest
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KRX_API_KEY=KRX_API_KEY:latest
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KOSIS_API_KEY=KOSIS_API_KEY:latest
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NAVER_SEARCH_CLIENT_ID=NAVER_SEARCH_CLIENT_ID:latest
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NAVER_SEARCH_CLIENT_SECRET=NAVER_SEARCH_CLIENT_SECRET:latest
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LAW_OC=LAW_OC:latest
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env_vars: |-
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KSKILL_PROXY_HOST=0.0.0.0
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KSKILL_PROXY_NAME=k-skill-proxy
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KSKILL_PROXY_CACHE_TTL_MS=300000
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KSKILL_PROXY_RATE_LIMIT_WINDOW_MS=60000
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KSKILL_PROXY_RATE_LIMIT_MAX=60
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flags: >-
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--platform=managed
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--allow-unauthenticated
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--cpu=1
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--memory=512Mi
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--min-instances=0
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--max-instances=3
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--concurrency=80
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--timeout=60
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--execution-environment=gen2
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--cpu-boost
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- name: Smoke test /health on the new revision
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env:
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SERVICE_URL: ${{ steps.deploy.outputs.url }}
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run: |
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set -euo pipefail
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echo "Service URL: ${SERVICE_URL}"
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for attempt in 1 2 3 4 5; do
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if curl -fsS --max-time 15 "${SERVICE_URL}/health" >/tmp/health.json; then
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break
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fi
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echo "Health probe attempt ${attempt} failed, retrying in 5s..."
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sleep 5
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done
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python3 -c "
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import json, sys
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data = json.load(open('/tmp/health.json'))
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if not data.get('ok'):
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print('Health response is not ok:', data)
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sys.exit(1)
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missing = [k for k, v in data.get('upstreams', {}).items() if k.endswith('Configured') and v is not True]
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if missing:
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print('Upstreams not configured:', missing)
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sys.exit(1)
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print('Health OK. All upstreams configured.')
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"
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- name: Smoke test custom domain (k-skill-proxy.nomadamas.org)
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run: |
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set -euo pipefail
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if curl -fsS --max-time 15 https://k-skill-proxy.nomadamas.org/health >/tmp/prod-health.json; then
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python3 -c "
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import json
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data = json.load(open('/tmp/prod-health.json'))
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print('Prod /health ok:', data.get('ok'))
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"
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else
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echo "::warning::Custom domain /health probe failed; revision may need traffic split or DNS warm-up."
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fi
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