duckdb/duckdb-skills

s3-explore

Explore and query data on S3, Cloudflare R2, GCS, MinIO, or any S3-compatible storage.

Zobacz źródło

Jak używać

  1. Skopiuj polecenie instalacji i sprawdź źródła przed instalacją we własnym środowisku agenta.
  2. W oryginale sprawdź wymagane narzędzia, dane dostępowe i obsługiwane środowiska.
  3. Opisz dane wejściowe i oczekiwany rezultat; porównaj wynik ze źródłami.
npx skills add https://github.com/duckdb/duckdb-skills --skill s3-explore

Przed rozpoczęciem

  • Instalacja nie obejmuje subskrypcji modelu, limitu API ani uprawnień do plików.
  • Gwiazdki i instalacje pokazują aktywność, a nie niezależny test jakości lub bezpieczeństwa.
Oryginalny dokument Skill

Treść z repozytorium z zachowaniem nagłówków, przykładów, kodu, tabel, linków i obrazów.

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:

ProviderURL patternsSecret setup
AWS S3s3://CREATE SECRET (TYPE S3, PROVIDER credential_chain);
Cloudflare R2r2://, s3:// with R2 endpointCREATE SECRET (TYPE R2, PROVIDER credential_chain);
GCSgs://, gcs://CREATE SECRET (TYPE GCS, PROVIDER credential_chain);
MinIO / customs3:// with custom endpointCREATE 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:

sql
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:

bash
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:

bash
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):

bash
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

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