终端看板
Sampler
by bytesagain3
Tool for shell commands execution, visualization and alerting. Configured with a simple YAML file. terminal-dashboard, go, alerting, charts, cmd.
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
# 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 <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
| Command | Description |
|---|---|
terminal-dashboard stats | Show 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 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 commandsexport.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
- Data pipeline logging — Track every step of your ETL/ELT pipeline from ingestion through transformation to export, creating a complete audit trail
- Data quality monitoring — Use
validateandprofileto record data quality checks and catch anomalies before they reach production - Schema change tracking — Log schema migrations and validation rules so you always know what changed and when
- Ad-hoc analysis journaling — Record queries, filters, and aggregations during exploratory analysis so you can reproduce your findings later
- 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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