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by bytesagain3

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

View Chinese version with editor review

安装

claude skill add --url github.com/openclaw/skills/tree/main/skills/bytesagain3/terminal-dashboard

文档

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

bash
# 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.

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

Utility Commands

CommandDescription
terminal-dashboard statsShow summary statistics across all log categories
terminal-dashboard export <fmt>Export all data (formats: json, csv, txt)
terminal-dashboard search <term>Search across all entries for a keyword
terminal-dashboard recentShow the 20 most recent history entries
terminal-dashboard statusHealth check — version, data dir, entry count, disk usage
terminal-dashboard helpShow the built-in help message
terminal-dashboard versionPrint 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|<input>. 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

bash
# 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:

bash
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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