k-skill/scripts/srt_booking_test_support.py
Jeffrey (Dongkyu) Kim 66f12cb43d
dev → main: srt-booking 좌석 탐색, korean-humanizer 신규 스킬, toss-securities 공식 OpenAPI 클라이언트, korean-law k-skill-proxy 편입 (#314)
* 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>
2026-06-12 19:06:18 +09:00

128 lines
4 KiB
Python

from __future__ import annotations
SEAT_HTML = "\n".join([
'<li class="scar-01 off"><strong>일반실<br />1호차</strong></li>',
'<li class="scar-04 on"><a href="#none" onclick="selectScarInfo(\'0004\'); return false;"><strong>일반실<br />4호차</strong></a></li>',
'<li class="scar-05"><a href="#none" onclick="selectScarInfo(\'0005\'); return false;"><strong>일반실<br />5호차</strong></a></li>',
'<li class="scar-03 off"><strong>특실<br />3호차</strong></li>',
'<a href="#none" onclick="selectSeatInfo(this, \'23\', \'6C\'); return false;">6C<strong><em>(정방향, 내측)</em></strong></a>',
'<a href="#none" onclick="selectSeatInfo(this, \'11\', \'3A\'); return false;">3A<strong><em>(역방향, 창측)</em></strong></a>',
"<span>5C<strong><em>(정방향, 내측, 선택불가)</em></strong></span>",
])
SPECIAL_SEAT_HTML = "\n".join([
'<li class="scar-03 on"><a href="#none" onclick="selectScarInfo(\'0003\'); return false;"><strong>특실<br />3호차</strong></a></li>',
'<li class="scar-05 off"><strong>일반실<br />5호차</strong></li>',
'<a href="#none" onclick="selectSeatInfo(this, \'31\', \'1A\'); return false;">1A<strong><em>(정방향, 1인석)</em></strong></a>',
"<span>2C<strong><em>(역방향, 내측, 선택불가)</em></strong></span>",
])
class FakeTrain:
train_number = "313"
dep_date = "20260610"
dep_time = "080000"
arr_date = "20260610"
arr_time = "103400"
train_code = "17"
train_name = "SRT"
dep_station_code = "0551"
dep_station_name = "수서"
arr_station_code = "0020"
arr_station_name = "부산"
dep_station_run_order = "000001"
arr_station_run_order = "000007"
general_seat_state = "예약가능"
special_seat_state = "매진"
reserve_wait_possible_code = "-2"
def general_seat_available(self) -> bool:
return True
def special_seat_available(self) -> bool:
return False
def reserve_standby_available(self) -> bool:
return False
class FakeResponse:
def __init__(self, text: str) -> None:
self.text = text
class FakeSession:
def __init__(self) -> None:
self.calls: list[dict[str, str]] = []
def get(self, _url: str, params: dict[str, str]) -> FakeResponse:
self.calls.append(params)
car = params["scarNo1"] or "0004"
return FakeResponse(SEAT_HTML.replace("scar-04 on", f"scar-{car[-2:]} on"))
class FakeClient:
def __init__(self, train: FakeTrain) -> None:
self.train = train
self._session = FakeSession()
def search_train(
self,
_dep: str,
_arr: str,
_date: str,
_time: str,
_time_limit: str | None = None,
available_only: bool = True,
) -> list[FakeTrain]:
return [self.train]
class NoisySession(FakeSession):
def get(self, _url: str, params: dict[str, str]) -> FakeResponse:
print("접속자가 많아 대기열에 들어갑니다.")
return super().get(_url, params)
class NoisyClient(FakeClient):
def __init__(self, train: FakeTrain) -> None:
self.train = train
self._session = NoisySession()
def search_train(
self,
_dep: str,
_arr: str,
_date: str,
_time: str,
_time_limit: str | None = None,
available_only: bool = True,
) -> list[FakeTrain]:
print("대기인원: 6명")
return [self.train]
class EmptyClient(FakeClient):
def search_train(
self,
_dep: str,
_arr: str,
_date: str,
_time: str,
_time_limit: str | None = None,
available_only: bool = True,
) -> list[FakeTrain]:
return []
class SpecialSession(FakeSession):
def get(self, _url: str, params: dict[str, str]) -> FakeResponse:
self.calls.append(params)
return FakeResponse(SPECIAL_SEAT_HTML)
class SpecialClient(FakeClient):
def __init__(self, train: FakeTrain) -> None:
self.train = train
self._session = SpecialSession()