Iteration 1 – Prompt Log - snuhcs-course/swpp-2026-project-team-14 GitHub Wiki
Iteration 1 – Prompt Log
Full prompt log for the AI Collaboration Report – Iteration 1 (2026-09-21 ~ 2026-10-09). Dates are KST.
- Each member lists the prompts they sent, with the date and the outcome.
- Personal information is removed, and long pasted blocks are shortened to
[생략].
- Omitted: one-word confirmations ("yes", "continue"), git/GitHub housekeeping, questions about the course, and drafts of team-chat messages.
Dongkun Moon — Claude Code (Claude Opus 5.5), body analysis (P9, P10)
All work was done in one Claude Code session in the local repository. Claude Code could read and edit files, run tests, build the app and drive the emulator. It prepared every issue and PR, and I read each one before it was posted.
Note: my prompts below are rewritten in a structured form (context, task, constraints, output). Each row matches one prompt I sent on that date; the request and the outcome are unchanged. The original wording is kept in the Claude Code session record.
Planning and design
| Date |
Prompt |
Outcome |
| 09-23 |
"Context: I am on team 14 (StyleMate). Attached are Team Exercise 3, our Issue/PR format and the Iteration 1 task table; I own P9 (body analysis screen) and P10 (body analysis AI integration). Task: study them and summarize my role. Constraint: we share one GitHub repo, so follow the team's Issue/PR format." |
Summary of my role (P9, P10) and the team's Issue/PR format |
| 09-23 |
"Context: [생략: app description and tech stack]. Our wiki will later need requirements (user stories, wireframes, non-functional requirements, constraints) and design documents (architecture, APIs, data models, class diagrams). Task: design my part, the body profile analysis in My Profile. Ask me about any decision you cannot infer." |
Claude asked about platform, inputs and photo retention first, then wrote docs/body-analysis/01-model-research.md, 02-design.md and 03-benchmark-plan.md |
| 09-23 |
"Before we continue: how can I check what has been built so far, and how can I evaluate how accurately the analysis measures a body?" |
Pointed me to the three documents; proposed an inspection tool and a benchmark |
| 09-23 |
"Context: users should wear underwear or tight clothing so the body can be measured as precisely as possible; loose clothing must still work, with a warning that accuracy is lower. Task: (a) define the measurement set, e.g. neck, shoulder width, chest, waist, hip, armhole, bicep, sleeve, thigh, inseam and outseam; (b) show me how to view the current screens in Android Studio. Constraint: no inspection tool for now." |
16-measurement set; Android Studio instructions |
| 09-23 |
"Build the Android prototype for this design and update the design documents to match." |
Compose prototype with a fake analyzer (daa1cf8, e51b125) |
Body analysis AI (P10)
| Date |
Prompt |
Outcome |
| 09-24 |
"Start P10, the body analysis AI integration. Install the GitHub CLI first so issues and PRs can be prepared from the terminal." |
Baseline pipeline: MediaPipe pose + mask, geometric measurement (715887e) |
| 09-24 |
"Keep every change local for now; do not commit or push. I have no front or side test photos yet, so suggest how to get test images." |
Three options for test photos |
| 09-24 |
"Use option 3: synthetic 3D bodies with known measurements as the benchmark." |
Synthetic benchmark: 8 Anny 3D bodies, true values sliced from the mesh (backend/scripts/synthetic_benchmark.py, docs/body-analysis/04-synthetic-benchmark.md) |
| 09-24 |
"Explain what front-only mode is and how it affects accuracy." |
Explained that the design let users skip the side photo, with depth guessed from ratios |
| 09-24 |
"Make the side photo required and remove the option to skip it. Question: if a user wears ordinary clothes, can the app still estimate body proportions and give insights such as lower- or upper-body volume?" |
Side photo made required (aa76d1d); clothing test planned |
| 09-24 |
"Implement all of the proposed changes." |
Clothing test. It showed that Claude's earlier claim "lengths survive clothing" was wrong: loose trousers make the inseam 13 cm short |
| 09-24 |
"Also fit the shoulder-width calibration on the benchmark." |
Shoulder width error −6.0 → +0.1 cm |
| 09-24 |
"Add a threshold margin so an insight is shown only when a ratio is clearly past its threshold." |
Insight margins → 0 wrong insights in the benchmark |
| 09-24 |
"Task: (a) replace the Android fake analyzer with calls to the backend pipeline; (b) improve the body analysis model as far as the evidence supports. Output: before/after accuracy on the benchmark and a list of changes." |
Django API (0a4e754), app connected to the backend (faa6fc8), dataset generator + learned correction (79a72b5, cd3fbac; 05-learned-correction.md L62: −40–90 % error) |
| 09-24 |
"Context: a teammate has added the infra and backend folders to our GitHub repo. Task: test their setup, then summarize what we have built locally so far." |
Tested the teammate's backend in Docker; found the libEGL 500 and the 256 Mi out-of-memory problem (3c6f084) |
| 09-25 |
"Restructure our code to match the directory structure in the wiki." |
Directory restructure (705e6f8) |
| 09-25 |
"Check whether the backend needs a 1 Gi memory limit; I suspect the current 256 Mi is too low. I will confirm with the infra owner before changing it." |
Memory limit change, agreed with Kihwan before merging |
Results screen and first merge (P9)
| Date |
Prompt |
Outcome |
| 09-28 |
"My Android emulator shows only a black screen. Find out whether it is my laptop's performance or a wrong virtual device setting, and suggest a fix." |
Diagnosed GPU/RAM settings of the emulator |
| 09-28 |
"Context: feedback after testing the results screen on the emulator. Task: (1) replace the plain list with a friendly body figure: tapping a highlighted part shows its measurement, and key measurements are always visible; (2) add body-shape insights; (3) do not ask users what they wear: detect it, tell them underwear or tight clothing is most accurate, and warn them when loose clothing is detected; (4) make the 'AI가 추정한 체형' page modern and simple, with measurements first and insights right below." |
Body figure with tappable parts, insight cards (960da7c); clothing question replaced by automatic loose-clothing detection (005160d) |
| 09-28 |
"Rebuild the Docker image and check that analysis still works inside it." |
Docker image rebuilt and checked |
| 09-29 |
"Prepare to merge into develop. First inspect the current state of the GitHub repo. Add two notes to the PR: the results screen design is not final and will be discussed with the team, and the accuracy badge is shown on purpose at this stage but will be removed later. [생략: TA's Iteration 1 notice]" |
Issue #11 and PR #12 drafted; notes added (e1ad3f4) |
| 09-29 |
"Post the issue and PR, and add the same notes to 02-design.md." |
PR #12 posted; I merged it after CI passed |
Bug fix, stress test and accuracy display
| Date |
Prompt |
Outcome |
| 10-01 |
"Context: a teammate reports that analysis keeps failing for some gallery photos, apparently depending on the image's pixel size. Task: reproduce it, find the root cause and fix it. Output: the cause, the fix and evidence that it works." |
Root cause: MediaPipe aborts when the width is not a multiple of 4. Width padding + regression test (b0b1186) |
| 10-01 |
"Verify the whole input path of the server and the app, and fix any other problems you find." |
4 more input bugs fixed (29346bf, b62d1c8). Claude's own "3 cm worse" claim turned out to come from an unequal comparison; it reverted the change |
| 10-02 |
"Show the measurement accuracy as a percentage." |
Claude asked what the % should be based on; its options included fixed numbers for the badges |
| 10-02 |
"Base it on the benchmark bodies' known measurements: show a per-measurement accuracy % only when one of our benchmark photos is analyzed." |
Accuracy % only for marked benchmark photos (91a8f75). Matching photos by appearance failed (m_avg vs m_heavy), so it switched to a marker |
| 10-02 |
"Split the work into two issues and two PRs, and show me the drafts before posting." |
Issues #13, #14 and PRs #15, #16 (stacked) |
| 10-03 |
"Using the benchmark photos with known measurements, build a table of accuracy for every measurement and body, compute our model's mean accuracy and include it in the PR. Re-check the whole feature for errors." |
docs/body-analysis/07-accuracy-report.md (817a8f9): mean 98.7 % in underwear (synthetic); fix for the bicep under loose tops (675e9b7) |
| 10-03 |
"Prepare the iteration-1-demo branch and its README." |
iteration-1-demo branch and README |
| 10-03 |
"Remove from the README's limitations the note that the profile is lost when the app closes because the DB is not connected; that is outside our part." |
README limitation removed |
PR #16 was merged into 13-fix-photo-input instead of develop, so it was reopened as PR #17.
Documentation
| Date |
Prompt |
Outcome |
| 10-06 |
"Attached are the Requirements & Specifications and Design Documentation guidelines. Write my part (body analysis) of both wiki pages following them." |
Body analysis parts of Requirements and Specifications and Design Documentation |
| 10-06 |
"Check this before I write it: 'we fine-tuned MediaPipe because the base model was not accurate enough.'" |
Claude corrected me: MediaPipe was used as-is; we trained a separate correction model on top |
| 10-06 |
"Draft my part of the AI Collaboration Report, and fill the team summary using everything we have." |
First draft of the AI Collaboration Report |
Preparation for Iteration 2 (local only, not merged)
| Date |
Prompt |
Outcome |
| 10-07 |
"Review the whole develop branch. We may not build the chat feature. Using our body analysis, research how to recommend clothes for a given body profile: find related models and propose an implementation." |
Review of develop; local commit ae0826b (body profile kept on the device); research on recommendation models |
| 10-08 |
"Context: inputs are the user's body profile, preferred fit and owned clothes; candidate garments come from the AI Hub K-Fashion dataset. Task: define how to score each garment, find existing scoring or compatibility models, and compare a slot-based design with try-on images. Include season and score updates from user feedback. Constraint: do not touch git yet." |
Recommendation design (K-Fashion dataset, slots, try-on); will be reported in Iteration 2 |
| 10-08 |
"Implement the proposed solution and build one test screen." |
Prototype in backend/recommendation/ (not committed) |
Hyeon U Jeong — Codex (OpenAI), wardrobe (P11, P12)
The following 19 selected entries focus on implementation, integration and deployment. Dates and times are from the original session record, converted to KST; all dates are in 2026. Entry IDs are retained from the earlier draft.
Editorial note: the Korean prompts have been paraphrased and shortened for readability while preserving the technical intent. They are edited summaries, not verbatim transcripts. Outcomes are retrospective summaries of the work; a cited commit may include changes completed in subsequent turns.
Planning and schema
| ID |
Date · KST |
Edited prompt |
Outcome |
| W01 |
09-25 20:05 |
의상 데이터 스키마를 어떻게 설계하면 좋을까요? FE와 BE가 아직 구현되지 않은 상태에서 옷장 기능을 독립적으로 개발하는 방법도 설명해주세요. |
Outlined the initial garment schema and a feature boundary that could be implemented before the shared frontend/backend skeleton. This was design guidance, not a completed feature. |
| W02 |
09-25 20:07 |
AI API 호출과 데이터 저장은 각각 어느 계층에서 처리하는 것이 적절할까요? 로그인 기능을 구현하지 않을 예정인데, 체형과 의상 JSON을 로컬에 저장해도 되는지 설명해주세요. |
Discussed backend API calls, persistent garment records and the implications of having no login. This was an early responsibility/storage discussion, not evidence that authentication was implemented. |
| W03 |
09-26 16:13 |
소재 관련 데이터는 제외하거나 사용자가 직접 지정하도록 수정해주세요. 선택 사이즈는 제외하고, 기존 스키마에 빠진 항목을 추가해주세요. 사진으로 판단하기 어려운 값은 사용자가 입력하도록 하며, 중복 항목은 통합해주세요. |
Revised the specification so material information depended on user input, omitted the selected-size field and consolidated overlapping data. These were intermediate fields; wearing properties were later removed in W23. 46df579 |
Capture prototype and the first complete registration flow
| ID |
Date · KST |
Edited prompt |
Outcome |
| W04 |
09-26 17:12 |
현재 작업을 커밋한 뒤, 카메라 촬영 → AI API 분석 → DB 저장의 데이터 흐름을 설계해주세요. 사용자 동작, 인터페이스, 데이터와 AI API의 역할을 포함하고, 우선 카메라 권한 요청, 옷장 목록, 촬영 버튼까지 구현해주세요. |
Designed the capture-to-storage flow and built the first permission, wardrobe-list and capture UI. AI analysis and persistence were still planned at this point; the complete flow followed in W09–W10. |
| W05 |
09-26 19:23 |
Hugging Face에서 의상 분석 목적에 맞는 모델을 찾아주세요. 사전 학습 모델의 추론만 수행할 때 GPU 없이 약 340MiB의 메모리로 동작할 수 있는지, 모델을 어느 계층에 배치해야 하는지 검토해주세요. VM RAM은 8GiB이며, 컨테이너 requests는 CPU 100m·메모리 128Mi, limits는 CPU 500m·메모리 256Mi입니다. |
Compared pretrained model/API options and server placement under the stated resource limits. The 340 MiB figure in the request was not a measured result for the completed wardrobe service. The RAM restriction was later lifted in W15. |
| W06 |
09-28 10:11 |
카메라로 옷을 비추면 측정점이 표시되고, 사용자가 위치를 확인한 뒤 촬영하면 치수를 계산하도록 설계해주세요. 계산한 치수와 사진에서 추출한 의상 정보를 옷장에 저장해야 합니다. 측정점은 실시간에 가깝게 표시하고, 지연시간과 앞서 제시한 자원 한도를 고려해 구현 방법을 검토해주세요. |
Explored an overlay → review → capture → measurement → registration flow. In the subsequent clarification I specified no reference object and a Galaxy S23 Ultra. This request defined the target; it did not by itself demonstrate real-time performance. |
| W08 |
09-28 12:40 |
앱의 설명 문구를 간결하게 정리해주세요. “직접 점 지정”과 같은 불필요한 괄호 설명을 제거하고, “암홀 직선”은 “암홀”, “소매부리 단면”은 “소매끝”으로 바꿔주세요. 간결한 UI 문구 원칙도 기록해주세요. |
Shortened the camera and measurement labels and established a concise UI-copy convention. User-facing labels became 암홀 and 소매끝; technical measurement definitions remained separate. |
| W09 |
09-28 12:49 |
의상 분석에는 gemini-3.1-flash-lite를 사용해주세요. API 키는 제가 환경 파일에 입력하겠습니다. 해당 파일을 .gitignore에 포함하고 백엔드에서 키를 읽어 연동하도록 구성해주세요. |
Connected backend Gemini analysis using gemini-3.1-flash-lite and environment-based key configuration. I supplied the API key locally. 1d342c1 |
| W10 |
09-28 13:13 |
분석 후 옷장에 추가하는 기능까지 완성해주세요. 상의 촬영 안내의 불필요한 설명을 제거하고, 치수는 소수점 둘째 자리까지 표시해주세요. 사용자가 분석 결과를 수정할 수 있도록 하며, 화면 하단에는 자유롭게 작성하고 저장할 수 있는 메모 입력란을 추가해주세요. |
Completed analysis → editable result → wardrobe save, with notes and measurements displayed to two decimal places. Removed unnecessary capture copy. 1d342c1 |
Automatic garment points and ARCore measurement
| ID |
Date · KST |
Edited prompt |
Outcome |
| W11 |
09-29 09:00 |
현재 작업을 커밋한 뒤, 원하는 기능을 구현할 수 있는 Hugging Face 모델을 찾아 연결해주세요. 모델 파일 용량은 제한하지 않되, 필요한 RAM은 1GB 미만이어야 합니다. |
After I clarified that the target was automatic garment measurement points, the agent selected and integrated GarmentIQ HRNet. The work was subsequently included in daff82c. The prompt states a resource limit, not proof of a measured sub-1-GiB deployment. |
| W14 |
09-30 19:00 |
지금은 구현하지 말고 다음 변경 방향만 기록해주세요. 지면에서 30~32cm 떨어진 위치에서 촬영하도록 자이로스코프를 활용하는 방안을 생각하고 있습니다. 기존 모델로 측정점을 표시한 뒤 ARCore와 각도 계산으로 치수를 추출하는 기능을 구현하고자 합니다. |
Recorded the proposed change without executing it in that turn. The agent explained that a gyroscope measures rotation, not absolute height above the floor; ARCore would be needed for the metric geometry. W16 revised the fixed-height condition. |
| W15 |
10-03 20:50 |
앞서 논의한 구현을 진행해주세요. Gemini가 추출하는 항목은 이름, category, color로 줄이고 RAM 제약은 해제해주세요. 의상 배경을 제거한 뒤 측정점을 검출하는 방법도 검토해주세요. 어깨 양끝, 가슴 양끝, 암홀, 소매길이 등 기존 스키마에 필요한 측정점을 자동으로 선택하도록 구현해주세요. |
Reduced Gemini output to name, category and colors, expanded HRNet-based garment measurement paths, and tried background removal. The background-removal experiment was later removed in W23. daff82c |
| W16 |
10-03 20:55 |
30cm는 예시이며 더 높은 위치에서 촬영해도 됩니다. 핵심은 기준 물체 없이 치수를 계산하는 것과 의상 측정점을 자동으로 선택하는 것입니다. 우선 오차 개선보다 기존 모델을 활용한 자동 측정점 구현에 집중하고, ARCore 기반 치수 계산 방식도 구현·검증해주세요. |
Connected model points to ray–plane intersection using the captured ARCore pose/plane, with garment-specific paths for tops and trousers. Geometry and mapping checks were recorded; a tape-measure accuracy benchmark was not. daff82c |
| W18 |
10-03 22:04 |
pattern과 상대적인 length 분류는 스키마에서 제거하는 것이 좋겠습니다. 실제 치수로 길이를 표현할 수 있으므로 상대 기장은 제외하고, 명확하게 정의하기 어려운 패턴 대신 색상 정보를 사용해주세요. |
Removed pattern and relative-length fields while keeping numeric garment dimensions. Updated the relevant contract, UI and documentation. 77c70b2 |
Integration and feedback from the phone
| ID |
Date · KST |
Edited prompt |
Outcome |
| W19 |
10-03 22:12 |
develop 브랜치의 프로젝트 구조와 디자인 방향을 확인하고, 현재 로컬 개발 파일을 팀 프로젝트에 통합하는 방법을 검토해주세요. |
Inspected develop and compared its shared navigation, body-analysis UI, backend structure and database setup with the personal prototype. Produced an integration plan before moving the implementation. |
| W20 |
10-03 22:49 |
기존 테스트용 local-dev를 제거하고, develop 브랜치의 실행 방식과 FE·BE 구조에 옷장 기능을 통합해주세요. |
Removed the previous isolated local-dev implementation and moved wardrobe into the team frontend/backend structure and configuration. d96049c |
| W22 |
10-04 00:20 |
docs/body-analysis의 디자인 문서와 현재 구현된 체형 분석 화면을 참고해주세요. 옷장 화면의 글꼴과 박스 배치를 맞춰 두 기능의 시각적 일관성을 높여주세요. |
Aligned the wardrobe typography, spacing, cards and buttons with the body-analysis screen and shared Compose components. 0aaa127 |
| W23 |
10-04 01:40 |
촬영 화면에서 기울기 표시와 배경 제거 기능을 없애주세요. 스키마에서는 촉감, 신축성, 비침, 두께, 계절, 소재 등 착용정보를 모두 제거해주세요. 분석 결과의 “AR 추정” 문구와 상세정보의 격식, 여밈, 디테일, 어깨, 넥라인 항목도 제거해주세요. |
Removed the tilt display, background-removal option, wearing properties and the listed descriptive fields from the UI/schema. Shoulder-width measurements and internal measurement provenance were retained; removing labels did not remove the AR calculation. a03026e |
Deployment packaging
| ID |
Date · KST |
Edited prompt |
Outcome |
| W26 |
10-05 04:33 |
PR #20에서 Docker 빌드 중 모델을 자동으로 다운로드하도록 구성한 부분을 검토해주세요. 문제가 없다면 iteration-1-demo 브랜치에도 PR을 만들고 병합해주세요. |
Reviewed PR #20 model packaging: pinned downloads, checksums and ONNX export during the Docker build. Following the later confirmation, the work reached the demo branch through PR #21. a979d55. |
Kihwan Kim — Codex (OpenAI), infrastructure (P6, P7, P15)
P6, P7: infrastructure and deployment
| Date |
Prompt |
Outcome |
| 09-24 |
"내가 현재 구축한 azure VM 위에 k3s 설치하고 main 브랜치 대상으로 ArgoCD 배포하려고 함. 각 구성 요소가 구체적으로 어떤 역할을 하는지, 그리고 지금 상황에서 Image 레지스트리는 어떤걸 사용하는게 좋을지 선택지들 구현 난도 측면에서 설명해줘." |
Explained the deployment components and compared registry options; GHCR was selected |
| 09-24 |
"디렉토리는 기본적으로 frontend, backend, infra로 나누고 기본 배포 네임스페이스는 team14로 정함. 이 구조를 기준으로 k3s와 ArgoCD 설정 yaml 파일 작성해주고, 이 파일들은 infra 폴더 내에서 작업하도록 해. 헬스체크 등 컨테이너 기본 엔드포인트들은 많이 사용되는 범용 포트 번호로 해도 무방하고, 해당 설정들은 코드 작성 완료 후 나한테 보고해." |
Kubernetes and Argo CD manifests under infra/ with the team14 namespace (3260866, 38c8a23, 5fb809f; PR #2) |
| 09-24 |
"테스트용 Django 서버(hello world 반환하는 간단한 서버)를 만들고 서비스 종류는 clusterIP로 해" |
Minimal Django test server and a ClusterIP Service (5fb809f; PR #2) |
| 09-24 |
"서버 deploy 전에 main에서 검증하는 CI 짜줘" |
CI that builds the container image and checks API responses before deployment (46bbae0; PR #2) |
| 09-24 |
"너가 짜준 CI가 계속 실패하는데, 원인이 뭐야? 서버 빌드와 엔드포인트 호출 간에 타이밍 이슈로 인해 서버가 완전하게 빌드가 안된 상태에서 시도해서 CI가 종료되는거 같은데 맞아? 왜 이런 기능까지 넣은거야?" |
Investigated the CI failures involving container startup and endpoint checks |
| 09-24 |
"CI는 컨테이너 환경에서 빌드했을때 서버가 정상적으로 healthy하게 뜨는지만 확인 후 정리하면 충분해" |
Removed the failing endpoint checks and simplified the workflow (501cd9c, 6c60a6d; PR #4, #5) |
P15 (integrated flow test) was done manually, without AI.
Juhyeon Choi — ChatGPT (OpenAI), project planning and documentation (P1–P5, P13–P16)
The following entries summarize representative prompts used for project planning, documentation, wireframes, and iteration coordination. The Korean prompts are edited summaries of the original requests rather than verbatim transcripts.
Project planning and proposal
| Date |
Prompt |
Outcome |
| 09-23 |
"지금까지 작성한 StyleMate proposal을 전체적으로 검토해주세요. Target Customers, Motivation, Innovation이 서로 잘 연결되는지 보고, 우리 서비스의 특징이 더 잘 드러나도록 수정하려면 어떻게 하면 될지 알려주세요." |
Reviewed the proposal structure and suggested revisions to make the target users, problem, and product differentiation more consistent. |
| 09-23 |
"우리 서비스는 사용자가 실제로 옷장에 등록한 옷을 가지고 코디를 만든다는 점이 중요한 차별점인 것 같습니다. 이 내용이 Motivation과 Innovation에서 더 잘 드러나도록 전체 내용을 다시 검토해주세요." |
Revised the proposal so that wardrobe-constrained recommendation was presented as a core product characteristic rather than a secondary feature. |
User flow and wireframes
| Date |
Prompt |
Outcome |
| 09-30 |
"현재 정리한 StyleMate 기능을 기준으로 전체 user flow를 어떻게 구성하면 좋을지 정리해주세요. 온보딩부터 체형 분석, 옷장 등록, 코디 추천까지 사용자가 어떤 화면을 거쳐야 하는지 검토해주세요." |
Organized the main application flow and decision points across onboarding, body analysis, wardrobe registration, and recommendation. |
| 10-09 |
"현재 와이어프레임을 보고 추가로 필요한 화면이나 상태가 있는지 검토해주세요. 빠진 화면이 있다면 어떤 화면인지와 어떤 기능이 필요한지도 설명해주세요." |
Identified missing screens and states and produced a checklist for completing the wireframes. |
Requirements and design documentation
| Date |
Prompt |
Outcome |
| 10-07 |
"Requirements & Specifications와 Design Documentation을 가이드라인과 비교해서 검토해주세요. 현재 문서에서 빠진 내용이나 더 보완해야 할 부분이 있는지 알려주세요." |
Compared the documents with the course guidelines and identified sections that needed additional explanation or restoration. |
| 10-07 |
"Requirements에서 와이어프레임이나 화면 설명처럼 필요한 내용까지 빠진 것 같습니다. 기존 내용과 가이드라인을 다시 비교해서 필요한 부분은 유지하면서 문서를 보완해주세요." |
Restored necessary screen- and wireframe-related explanations while keeping the document aligned with the required structure. |