사용 방법
- 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/duckdb/duckdb-skills --skill s3-explore시작 전 확인
- 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
- 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
You are helping the user explore data on remote object storage using DuckDB.
URL: $0 Question: ${1:-list and describe what's there}
Step 1 — Detect provider and set up credentials
Based on the URL or user context, prepend the appropriate secret configuration:
| Provider | URL patterns | Secret setup |
|---|---|---|
| AWS S3 | s3:// | CREATE SECRET (TYPE S3, PROVIDER credential_chain); |
| Cloudflare R2 | r2://, s3:// with R2 endpoint | CREATE SECRET (TYPE R2, PROVIDER credential_chain); |
| GCS | gs://, gcs:// | CREATE SECRET (TYPE GCS, PROVIDER credential_chain); |
| MinIO / custom | s3:// with custom endpoint | CREATE SECRET (TYPE S3, KEY_ID '...', SECRET '...', ENDPOINT '...', USE_SSL true); |
For R2, if the user provides an account ID, the endpoint is <account_id>.r2.cloudflarestorage.com. R2 URLs like r2://bucket/path should be rewritten to s3://bucket/path with the R2 secret.
For public buckets (e.g., Overture Maps, AWS open data), no secret is needed — skip this step.
Always prepend:
LOAD httpfs;Step 2 — Determine what the URL points to
If the URL looks like a directory or bucket (no file extension, or ends with /), list its contents with sizes:
duckdb -c "
LOAD httpfs;
<SECRET_SETUP>
SELECT filename, (size / 1024 / 1024)::DECIMAL(10,1) AS size_mb, last_modified
FROM read_blob('<URL>/*')
ORDER BY filename
LIMIT 50;
"Note: only select filename, size, last_modified — never select content, which would download the actual files.
If the URL points to a specific file or glob pattern (has a file extension or contains *), preview it:
duckdb -c "
LOAD httpfs;
<SECRET_SETUP>
DESCRIBE FROM '<URL>';
SELECT count(*) AS row_count FROM '<URL>';
FROM '<URL>' LIMIT 20;
"For Parquet files, get row counts and sizes from metadata (no data download):
duckdb -c "
LOAD httpfs;
<SECRET_SETUP>
SELECT file_name,
sum(row_group_num_rows) AS total_rows,
(sum(row_group_compressed_bytes) / 1024 / 1024)::DECIMAL(10,1) AS compressed_mb
FROM parquet_metadata('<URL>')
GROUP BY file_name;
"Step 3 — Answer the question
Using the listing, schema, or sample data, answer:
${1:-list and describe what's there}
If the user asks an analytical question (e.g., "how many rows match X"), write and run the appropriate SQL query. DuckDB pushes predicates down into Parquet on S3, so filtering is efficient even on large remote datasets.
Error handling
- `duckdb: command not found` → delegate to
/duckdb-skills:install-duckdb - Access denied / 403 → suggest the user check credentials:
aws configure, environment variables, or provide explicit key/secret - Bucket not found / 404 → check the URL and region
- Timeout on large listing → suggest narrowing the glob pattern or adding a prefix
