[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-duckdb-read-file":3,"mdc--cbxod1-key":35,"related-org-duckdb-read-file":755,"related-repo-duckdb-read-file":868},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":25,"repoUrl":26,"updatedAt":27,"license":28,"forks":29,"topics":30,"repo":31,"sourceUrl":33,"mdContent":34},"read-file","read and profile data files","Read any data file (CSV, JSON, Parquet, Avro, Excel, spatial, SQLite) or remote URL (S3, HTTPS). Use when user references a data file, asks \"what's in this file\", or wants to preview\u002Fprofile a dataset. 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If the user gave a bare filename (no `\u002F`), resolve it to a full path with `find` first.\n\nRun a single DuckDB command that defines the `read_any` macro inline and reads the file.\n\nFor **remote files**, prepend the necessary LOAD\u002FSECRET before the macro:\n\n| Protocol | Prepend |\n|---|---|\n| `https:\u002F\u002F` \u002F `http:\u002F\u002F` | `LOAD httpfs;` |\n| `s3:\u002F\u002F` | `LOAD httpfs; CREATE SECRET (TYPE S3, PROVIDER credential_chain);` |\n| `gs:\u002F\u002F` \u002F `gcs:\u002F\u002F` | `LOAD httpfs; CREATE SECRET (TYPE GCS, PROVIDER credential_chain);` |\n| `az:\u002F\u002F` \u002F `azure:\u002F\u002F` \u002F `abfss:\u002F\u002F` | `LOAD httpfs; LOAD azure; CREATE SECRET (TYPE AZURE, PROVIDER credential_chain);` |\n\nFor **local files**, no prefix needed.\n\n```bash\nduckdb -csv -c \"\nCREATE OR REPLACE MACRO read_any(file_name) AS TABLE\n  WITH json_case AS (FROM read_json_auto(file_name))\n     , csv_case AS (FROM read_csv(file_name))\n     , parquet_case AS (FROM read_parquet(file_name))\n     , avro_case AS (FROM read_avro(file_name))\n     , blob_case AS (FROM read_blob(file_name))\n     , spatial_case AS (FROM st_read(file_name))\n     , excel_case AS (FROM read_xlsx(file_name))\n     , sqlite_case AS (FROM sqlite_scan(file_name, (SELECT name FROM sqlite_master(file_name) LIMIT 1)))\n     , ipynb_case AS (\n         WITH nb AS (FROM read_json_auto(file_name))\n         SELECT cell_idx, cell.cell_type,\n                array_to_string(cell.source, '') AS source,\n                cell.execution_count\n         FROM nb, UNNEST(cells) WITH ORDINALITY AS t(cell, cell_idx)\n         ORDER BY cell_idx\n     )\n  FROM query_table(\n    CASE\n      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'\n      WHEN file_name ILIKE '%.csv' OR file_name ILIKE '%.tsv' OR file_name ILIKE '%.tab' OR file_name ILIKE '%.txt' THEN 'csv_case'\n      WHEN file_name ILIKE '%.parquet' OR file_name ILIKE '%.pq' THEN 'parquet_case'\n      WHEN file_name ILIKE '%.avro' THEN 'avro_case'\n      WHEN file_name ILIKE '%.xlsx' OR file_name ILIKE '%.xls' THEN 'excel_case'\n      WHEN file_name ILIKE '%.shp' OR file_name ILIKE '%.gpkg' OR file_name ILIKE '%.fgb' OR file_name ILIKE '%.kml' THEN 'spatial_case'\n      WHEN file_name ILIKE '%.ipynb' THEN 'ipynb_case'\n      WHEN file_name ILIKE '%.db' OR file_name ILIKE '%.sqlite' OR file_name ILIKE '%.sqlite3' THEN 'sqlite_case'\n      ELSE 'blob_case'\n    END\n  );\n\nDESCRIBE FROM read_any('RESOLVED_PATH');\nSELECT count(*) AS row_count FROM read_any('RESOLVED_PATH');\nFROM read_any('RESOLVED_PATH') LIMIT 20;\n\"\n```\n\n**If this fails:**\n- **`duckdb: command not found`** → invoke `\u002Fduckdb-skills:install-duckdb` and retry.\n- **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.\n- **Wrong reader \u002F parse error** → use the correct `read_*` function directly instead of `read_any`.\n\n## Step 2 — Answer\n\nUsing the schema, row count, and sample rows, answer:\n\n`${1:-describe the data: summarize column types, row count, and any notable patterns.}`\n",{"data":36,"body":39},{"name":4,"description":6,"argument-hint":37,"allowed-tools":38},"\u003Cfilename or URL> [question about the data]","Bash",{"type":40,"children":41},"root",[42,50,70,77,111,124,137,278,289,643,651,729,735,740,749],{"type":43,"tag":44,"props":45,"children":46},"element","p",{},[47],{"type":48,"value":49},"text","You are helping the user read and analyze a data file using DuckDB.",{"type":43,"tag":44,"props":51,"children":52},{},[53,55,62,64],{"type":48,"value":54},"Filename given: ",{"type":43,"tag":56,"props":57,"children":59},"code",{"className":58},[],[60],{"type":48,"value":61},"$0",{"type":48,"value":63},"\nQuestion: ",{"type":43,"tag":56,"props":65,"children":67},{"className":66},[],[68],{"type":48,"value":69},"${1:-describe the data}",{"type":43,"tag":71,"props":72,"children":74},"h2",{"id":73},"step-1-read-it",[75],{"type":48,"value":76},"Step 1 — Read it",{"type":43,"tag":44,"props":78,"children":79},{},[80,86,88,93,95,101,103,109],{"type":43,"tag":56,"props":81,"children":83},{"className":82},[],[84],{"type":48,"value":85},"RESOLVED_PATH",{"type":48,"value":87}," is ",{"type":43,"tag":56,"props":89,"children":91},{"className":90},[],[92],{"type":48,"value":61},{"type":48,"value":94},". 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