duckdb/duckdb-skills

read-file

Read any data file (CSV, JSON, Parquet, Avro, Excel, spatial, SQLite) or remote URL (S3, HTTPS).

Quelltext ansehen

Verwendung

  1. Installationsbefehl kopieren und das Quellpaket vor der Installation in der eigenen Agentenumgebung prüfen.
  2. Benötigte Werkzeuge, Zugangsdaten und Hosts im Original prüfen.
  3. Eingabe und gewünschtes Ergebnis beschreiben und die Ausgabe mit den Quellen abgleichen.
npx skills add https://github.com/duckdb/duckdb-skills --skill read-file

Vor dem Start

  • Die Installation umfasst weder Modellabo noch API-Kontingent oder Dateizugriffsrechte.
  • Sterne und Installationen zeigen Aktivität, keine unabhängige Qualitäts- oder Sicherheitsprüfung.
Originales Skill-Dokument

Aus dem Quell-Repository gerendert; Überschriften, Beispiele, Code, Tabellen, Links und Bilder bleiben erhalten.

You are helping the user read and analyze a data file using DuckDB.

Filename given: $0 Question: ${1:-describe the data}

Step 1 — Read it

RESOLVED_PATH is $0. If the user gave a bare filename (no /), resolve it to a full path with find first.

Run a single DuckDB command that defines the read_any macro inline and reads the file.

For remote files, prepend the necessary LOAD/SECRET before the macro:

ProtocolPrepend
https:// / http://LOAD httpfs;
s3://LOAD httpfs; CREATE SECRET (TYPE S3, PROVIDER credential_chain);
gs:// / gcs://LOAD httpfs; CREATE SECRET (TYPE GCS, PROVIDER credential_chain);
az:// / azure:// / abfss://LOAD httpfs; LOAD azure; CREATE SECRET (TYPE AZURE, PROVIDER credential_chain);

For local files, no prefix needed.

bash
duckdb -csv -c "
CREATE OR REPLACE MACRO read_any(file_name) AS TABLE
  WITH json_case AS (FROM read_json_auto(file_name))
     , csv_case AS (FROM read_csv(file_name))
     , parquet_case AS (FROM read_parquet(file_name))
     , avro_case AS (FROM read_avro(file_name))
     , blob_case AS (FROM read_blob(file_name))
     , spatial_case AS (FROM st_read(file_name))
     , excel_case AS (FROM read_xlsx(file_name))
     , sqlite_case AS (FROM sqlite_scan(file_name, (SELECT name FROM sqlite_master(file_name) LIMIT 1)))
     , ipynb_case AS (
         WITH nb AS (FROM read_json_auto(file_name))
         SELECT cell_idx, cell.cell_type,
                array_to_string(cell.source, '') AS source,
                cell.execution_count
         FROM nb, UNNEST(cells) WITH ORDINALITY AS t(cell, cell_idx)
         ORDER BY cell_idx
     )
  FROM query_table(
    CASE
      WHEN file_name ILIKE '%.json' OR file_name ILIKE '%.jsonl' OR file_name ILIKE '%.ndjson' OR file_name ILIKE '%.geojson' OR file_name ILIKE '%.geojsonl' OR file_name ILIKE '%.har' THEN 'json_case'
      WHEN file_name ILIKE '%.csv' OR file_name ILIKE '%.tsv' OR file_name ILIKE '%.tab' OR file_name ILIKE '%.txt' THEN 'csv_case'
      WHEN file_name ILIKE '%.parquet' OR file_name ILIKE '%.pq' THEN 'parquet_case'
      WHEN file_name ILIKE '%.avro' THEN 'avro_case'
      WHEN file_name ILIKE '%.xlsx' OR file_name ILIKE '%.xls' THEN 'excel_case'
      WHEN file_name ILIKE '%.shp' OR file_name ILIKE '%.gpkg' OR file_name ILIKE '%.fgb' OR file_name ILIKE '%.kml' THEN 'spatial_case'
      WHEN file_name ILIKE '%.ipynb' THEN 'ipynb_case'
      WHEN file_name ILIKE '%.db' OR file_name ILIKE '%.sqlite' OR file_name ILIKE '%.sqlite3' THEN 'sqlite_case'
      ELSE 'blob_case'
    END
  );

DESCRIBE FROM read_any('RESOLVED_PATH');
SELECT count(*) AS row_count FROM read_any('RESOLVED_PATH');
FROM read_any('RESOLVED_PATH') LIMIT 20;
"

If this fails:

  • `duckdb: command not found` → invoke /duckdb-skills:install-duckdb and retry.
  • Missing extension (e.g. spatial files, xlsx, sqlite) → retry with INSTALL spatial; LOAD spatial; or INSTALL sqlite_scanner; LOAD sqlite_scanner; prepended before the macro.
  • Wrong reader / parse error → use the correct read_* function directly instead of read_any.

Step 2 — Answer

Using the schema, row count, and sample rows, answer:

${1:-describe the data: summarize column types, row count, and any notable patterns.}

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aus demselben Repository

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