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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>
156 lines
5 KiB
Python
156 lines
5 KiB
Python
#!/usr/bin/env python3
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from __future__ import annotations
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import re
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from typing import TypedDict
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class SrtCar(TypedDict):
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car_no: int
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car_no_raw: str
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room_class: str
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available: bool
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current: bool
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class SrtSeat(TypedDict):
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seat: str
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seat_no: str
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available: bool
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direction: str
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position: str
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notes: list[str]
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CAR_RE = re.compile(
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r'<li class="scar-(?P<car>\d+)(?P<class>[^"]*)">(?P<body>.*?)</li>',
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re.DOTALL,
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)
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SEAT_LINK_RE = re.compile(
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r"<a[^>]+selectSeatInfo\(this,\s*'(?P<seat_no>[^']+)',\s*'(?P<seat>[^']+)'\)[^>]*>"
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r".*?<em>\((?P<detail>[^)]*)\)</em>",
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re.DOTALL,
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)
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SEAT_SPAN_RE = re.compile(
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r"<span>\s*(?P<seat>\d+[A-Z])\s*<strong><em>\((?P<detail>[^)]*)\)</em></strong></span>",
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re.DOTALL,
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)
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TAG_RE = re.compile(r"<[^>]+>")
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def strip_tags(value: str) -> str:
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return TAG_RE.sub(" ", value).replace("\xa0", " ").strip()
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def parse_detail(detail: str) -> tuple[str, str, list[str]]:
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parts = [part.strip() for part in detail.split(",")]
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direction = next((part for part in parts if part in {"정방향", "역방향"}), "unknown")
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position = next((part for part in parts if part in {"창측", "내측", "1인석"}), "unknown")
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notes = [part for part in parts if part not in {direction, position} and part]
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return direction, position, notes
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def parse_cars(html: str) -> list[SrtCar]:
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cars: list[SrtCar] = []
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for match in CAR_RE.finditer(html):
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body = match.group("body")
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text = strip_tags(body)
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room_class = "특실" if "특실" in text else "일반실"
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css_class = match.group("class")
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has_link = "selectScarInfo" in body
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cars.append(
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{
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"car_no": int(match.group("car")),
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"car_no_raw": f"{int(match.group('car')):04d}",
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"room_class": room_class,
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"available": has_link and "off" not in css_class.split(),
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"current": "on" in css_class.split(),
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}
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)
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return cars
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def parse_seats(html: str) -> list[SrtSeat]:
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seats: list[SrtSeat] = []
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seen: set[str] = set()
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for match in SEAT_LINK_RE.finditer(html):
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direction, position, notes = parse_detail(match.group("detail"))
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seat = match.group("seat")
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seen.add(seat)
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seats.append(
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{
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"seat": seat,
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"seat_no": match.group("seat_no"),
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"available": True,
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"direction": direction,
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"position": position,
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"notes": notes,
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}
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)
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for match in SEAT_SPAN_RE.finditer(html):
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seat = match.group("seat")
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if seat in seen:
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continue
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direction, position, notes = parse_detail(match.group("detail"))
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seats.append(
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{
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"seat": seat,
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"seat_no": "",
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"available": False,
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"direction": direction,
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"position": position,
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"notes": notes,
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}
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)
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return seats
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def parse_seat_label(seat_label: str) -> tuple[int | None, str]:
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match = re.match(r"^(\d+)([A-Z])$", seat_label)
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if match is None:
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return None, ""
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return int(match.group(1)), match.group(2)
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def car_center_priority(car: SrtCar, car_numbers: list[int]) -> tuple[float, int]:
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if not car_numbers:
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return (0.0, car["car_no"])
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center = (min(car_numbers) + max(car_numbers)) / 2
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return (abs(car["car_no"] - center), car["car_no"])
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def sort_cars_for_booking(cars: list[SrtCar], priority: str = "center") -> list[SrtCar]:
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match priority:
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case "center":
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car_numbers = [car["car_no"] for car in cars]
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return sorted(cars, key=lambda car: car_center_priority(car, car_numbers))
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case "low":
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return sorted(cars, key=lambda car: car["car_no"])
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case "high":
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return sorted(cars, key=lambda car: car["car_no"], reverse=True)
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case _:
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raise ValueError(f"unsupported car priority: {priority}")
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def seat_preference_key(seat: SrtSeat, priority: str = "forward-window") -> tuple[int, int, int, str]:
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row, column = parse_seat_label(seat["seat"])
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forward_rank = 0 if seat["direction"] == "정방향" else 1
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window_rank = 0 if seat["position"] in {"창측", "1인석"} else 1
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row_rank = 999 if row is None else row
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match priority:
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case "forward-window":
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return (forward_rank, window_rank, row_rank, column)
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case "window-forward":
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return (window_rank, forward_rank, row_rank, column)
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case "row-low":
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return (row_rank, forward_rank, window_rank, column)
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case _:
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raise ValueError(f"unsupported seat priority: {priority}")
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def sort_seats_for_booking(seats: list[SrtSeat], priority: str = "forward-window") -> list[SrtSeat]:
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return sorted(seats, key=lambda seat: seat_preference_key(seat, priority))
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sort_cars = sort_cars_for_booking
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sort_seats = sort_seats_for_booking
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