Add single-line visual headers to 66 command files across 10 plugins: - clarity-assist (2 commands): 💬 - claude-config-maintainer (5 commands): ⚙️ - cmdb-assistant (11 commands): 🖥️ - code-sentinel (3 commands): 🔒 - contract-validator (5 commands): ✅ - data-platform (10 commands): 📊 - doc-guardian (5 commands): 📝 - git-flow (8 commands): 🔀 - pr-review (7 commands): 🔍 - viz-platform (10 commands): 🎨 Each command now displays a consistent header at execution start: ┌────────────────────────────────────────────────────────────────┐ │ [icon] PLUGIN-NAME · Command Description │ └────────────────────────────────────────────────────────────────┘ Addresses #275 (other plugin commands visual output) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
57 lines
1.6 KiB
Markdown
57 lines
1.6 KiB
Markdown
# /ingest - Data Ingestion
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## Visual Output
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When executing this command, display the plugin header:
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```
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┌──────────────────────────────────────────────────────────────────┐
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│ 📊 DATA-PLATFORM · Ingest │
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└──────────────────────────────────────────────────────────────────┘
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```
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Then proceed with the ingestion.
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Load data from files or database into the data platform.
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## Usage
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```
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/ingest [source]
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```
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## Workflow
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1. **Identify data source**:
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- If source is a file path, determine format (CSV, Parquet, JSON)
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- If source is "db" or a table name, query PostgreSQL
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2. **Load data**:
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- For files: Use `read_csv`, `read_parquet`, or `read_json`
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- For database: Use `pg_query` with appropriate SELECT
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3. **Validate**:
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- Check row count against limits
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- If exceeds 100k rows, suggest chunking or filtering
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4. **Report**:
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- Show data_ref, row count, columns, and memory usage
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- Preview first few rows
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## Examples
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```
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/ingest data/sales.csv
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/ingest data/customers.parquet
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/ingest "SELECT * FROM orders WHERE created_at > '2024-01-01'"
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```
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## Available Tools
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Use these MCP tools:
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- `read_csv` - Load CSV files
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- `read_parquet` - Load Parquet files
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- `read_json` - Load JSON/JSONL files
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- `pg_query` - Query PostgreSQL database
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- `list_data` - List loaded DataFrames
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