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Terminal Dashboard

by @bytesagain3

Tool for shell commands execution, visualization and alerting. Configured with a simple YAML file. terminal-dashboard, go, alerting, charts, cmd.

Versionv1.0.0
Downloads353
Installs1
#clawhub
TERMINAL
clawhub install terminal-dashboard

📖 About This Skill

version: "1.0.0" name: Sampler description: "Tool for shell commands execution, visualization and alerting. Configured with a simple YAML file. terminal-dashboard, go, alerting, charts, cmd."

Terminal Dashboard

Terminal Dashboard v2.0.0 — a data toolkit for building data pipelines and tracking data operations from the command line. Ingest, transform, query, filter, aggregate, and visualize your data — all logged locally with timestamps for full traceability.

Why Terminal Dashboard?

  • Works entirely offline — your data never leaves your machine
  • Simple command-line interface, no GUI needed
  • Timestamped logging for every operation
  • Export to JSON, CSV, or plain text anytime
  • Automatic history and activity tracking
  • Searchable records across all data pipeline stages

    • Getting Started

    # See all available commands
terminal-dashboard help
    Check current health status
    
terminal-dashboard status
    View summary statistics
    
terminal-dashboard stats

    Commands

    Data Pipeline Commands

    Each command works in two modes: run without arguments to view recent entries, or pass input to record a new entry.

    | Command | Description | |---------|-------------| | terminal-dashboard ingest | Record data ingestion events (file imports, API pulls, stream captures) | | terminal-dashboard transform | Log data transformations (format conversions, cleaning steps, enrichments) | | terminal-dashboard query | Record queries executed (SQL, API calls, search operations) | | terminal-dashboard filter | Log filter operations (row filtering, column selection, deduplication) | | terminal-dashboard aggregate | Record aggregation operations (group-by, rollups, summaries) | | terminal-dashboard visualize | Log visualization outputs (charts generated, dashboards updated) | | terminal-dashboard export | Record export operations (file writes, API pushes, report generation) | | terminal-dashboard sample | Log sampling operations (random samples, stratified picks, head/tail) | | terminal-dashboard schema | Record schema operations (schema detection, validation rules, migrations) | | terminal-dashboard validate | Log validation results (data quality checks, constraint tests, anomalies) | | terminal-dashboard pipeline | Record pipeline operations (end-to-end runs, DAG executions, orchestration) | | terminal-dashboard profile | Log profiling results (data profiling, column stats, distribution analysis) |

    Utility Commands

    | Command | Description | |---------|-------------| | terminal-dashboard stats | Show summary statistics across all log categories | | terminal-dashboard export | Export all data (formats: json, csv, txt) | | terminal-dashboard search | Search across all entries for a keyword | | terminal-dashboard recent | Show the 20 most recent history entries | | terminal-dashboard status | Health check — version, data dir, entry count, disk usage | | terminal-dashboard help | Show the built-in help message | | terminal-dashboard version | Print version (v2.0.0) |

    Data Storage

    All data is stored locally in ~/.local/share/terminal-dashboard/. Structure:

  • ingest.log, transform.log, query.log, etc. — one log file per command, pipe-delimited (timestamp|value)
  • history.log — unified activity log across all commands
  • export.json / export.csv / export.txt — generated export files

    • Each entry is stored as YYYY-MM-DD HH:MM|. Use export to back up your data anytime.

    Requirements

  • Bash 4+ (uses set -euo pipefail)
  • Standard Unix utilities (date, wc, du, tail, grep, sed, cat)
  • No external dependencies or internet access needed

    • When to Use

    1. Data pipeline logging — Track every step of your ETL/ELT pipeline from ingestion through transformation to export, creating a complete audit trail 2. Data quality monitoring — Use validate and profile to record data quality checks and catch anomalies before they reach production 3. Schema change tracking — Log schema migrations and validation rules so you always know what changed and when 4. Ad-hoc analysis journaling — Record queries, filters, and aggregations during exploratory analysis so you can reproduce your findings later 5. Pipeline debugging — When a data pipeline breaks, search through ingest, transform, and export logs to pinpoint where things went wrong

    Examples

    # Record a data ingestion event
terminal-dashboard ingest "Loaded 2.4M rows from sales_2024.csv into staging"
    Log a transformation step
    
terminal-dashboard transform "Normalized phone numbers, deduplicated by email — 12k dupes removed"
    Record a query
    
terminal-dashboard query "SELECT region, SUM(revenue) FROM sales GROUP BY region — 8 rows returned"
    Log a validation check
    
terminal-dashboard validate "Schema check passed: all 47 columns match expected types"
    Record a pipeline run
    
terminal-dashboard pipeline "Daily ETL completed: ingest→clean→aggregate→export in 4m 23s"
    Export everything to JSON
    
terminal-dashboard export json
    Search logs for a dataset
    
terminal-dashboard search "sales_2024"

    Output

    All commands output to stdout. Redirect to a file if needed:

    terminal-dashboard stats > pipeline-report.txt
terminal-dashboard export csv

    Configuration

    Set TERMINAL_DASHBOARD_DIR environment variable to override the default data directory (~/.local/share/terminal-dashboard/).

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    ⚡ When to Use

    TriggerAction
    2. **Data quality monitoring** — Use `validate` and `profile` to record data quality checks and catch anomalies before they reach production
    3. **Schema change tracking** — Log schema migrations and validation rules so you always know what changed and when
    4. **Ad-hoc analysis journaling** — Record queries, filters, and aggregations during exploratory analysis so you can reproduce your findings later
    5. **Pipeline debugging** — When a data pipeline breaks, search through ingest, transform, and export logs to pinpoint where things went wrong

    💡 Examples

    # Record a data ingestion event
terminal-dashboard ingest "Loaded 2.4M rows from sales_2024.csv into staging"
    Log a transformation step
    
terminal-dashboard transform "Normalized phone numbers, deduplicated by email — 12k dupes removed"
    Record a query
    
terminal-dashboard query "SELECT region, SUM(revenue) FROM sales GROUP BY region — 8 rows returned"
    Log a validation check
    
terminal-dashboard validate "Schema check passed: all 47 columns match expected types"
    Record a pipeline run
    
terminal-dashboard pipeline "Daily ETL completed: ingest→clean→aggregate→export in 4m 23s"
    Export everything to JSON
    
terminal-dashboard export json
    Search logs for a dataset
    
terminal-dashboard search "sales_2024"

    ⚙️ Configuration

    Set TERMINAL_DASHBOARD_DIR environment variable to override the default data directory (~/.local/share/terminal-dashboard/).

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