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Logbook

by @ckchzh

Write journal entries, search history, and export your personal log digitally. Use when journaling thoughts, searching past entries, exporting notes.

Versionv2.0.1

Downloads609

Installs1

#writing #legal #education #communication

View on ClawHub →

TERMINAL

clawhub install logbook

📖 About This Skill

name: LogBook version: "2.0.0" author: "BytesAgain" homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills tags: ["journal","diary","log","daily","writing","personal","productivity","notes"] categories: ["Personal Management", "Productivity", "Writing"] description: "Write journal entries, search history, and export your personal log digitally. Use when journaling thoughts, searching past entries, exporting notes."

LogBook

LogBook is a data processing and analysis toolkit for querying, importing, exporting, transforming, validating, and visualizing datasets from the terminal. It provides 10 core commands for working with structured data, plus built-in history logging and a quick dashboard view. All operations are local — no external APIs, databases, or network connections required.

Commands

| Command | Description | |---------|-------------| | logbook query | Query data from the local data store. Logs the query action to history for auditing. | | logbook import | Import a data file into the local store. Accepts any file path as input. | | logbook export | Export processed results to a specified destination (defaults to stdout). | | logbook transform | Transform data from one format or structure to another. | | logbook validate | Validate data against the built-in schema. Reports schema compliance status. | | logbook stats | Display basic statistics — total record count from the data log. | | logbook schema | Show the current data schema. Default fields: id, name, value, timestamp. | | logbook sample | Preview the first 5 records from the data store, or "No data" if the store is empty. | | logbook clean | Clean and deduplicate the data store, removing redundant entries. | | logbook dashboard | Quick dashboard showing total record count and summary metrics. | | logbook help | Show help with all available commands and usage information. | | logbook version | Print version string (logbook v2.0.0). |

Data Storage

All data is stored locally in ~/.local/share/logbook/ (override with LOGBOOK_DIR or XDG_DATA_HOME environment variables).

Directory structure:

~/.local/share/logbook/
├── data.log         # Main data store (line-based records)
└── history.log      # Unified activity log with timestamps

Every command logs its action to history.log with a timestamp (MM-DD HH:MM) for full traceability. The main data file data.log holds all imported and processed records.

Requirements

Bash (with set -euo pipefail)

Standard Unix utilities: date, wc, head, du, echo

No external dependencies, databases, or API keys required

Optional: Set LOGBOOK_DIR environment variable to customize the data directory location

When to Use

1. Importing and querying datasets — Pull in CSV, log, or structured data files and run quick queries against them from the terminal without needing a full database setup. 2. Data validation workflows — Validate incoming data against the built-in schema (id, name, value, timestamp) before processing to catch format issues early. 3. Data transformation pipelines — Transform data between formats or structures as part of an ETL-like workflow, all within a lightweight bash script. 4. Quick dashboard and statistics — Get instant record counts and summary metrics via dashboard or stats without writing custom aggregation scripts. 5. Data cleanup and deduplication — Use clean to remove duplicate records and normalize the data store before exporting or feeding into downstream analysis.

Examples

# Import a data file
logbook import transactions.csv
Query the data store
logbook query "status=active"
View the data schema
logbook schema
Preview first 5 records
logbook sample
Get basic statistics (record count)
logbook stats
Transform data between formats
logbook transform raw.csv cleaned.csv
Validate data integrity against schema
logbook validate
Quick dashboard view
logbook dashboard
Export results
logbook export results.json
Clean and deduplicate the data store
logbook clean
Show version
logbook version

Environment Variables

| Variable | Description | Default | |----------|-------------|---------| | LOGBOOK_DIR | Custom data directory path | ~/.local/share/logbook | | XDG_DATA_HOME | XDG base data directory (fallback) | ~/.local/share |

⚡ When to Use

Trigger	Action
2. Data validation workflows — Validate incoming data against the built-in schema (`id, name, value, timestamp`) before processing to catch format issues early.
3. Data transformation pipelines — Transform data between formats or structures as part of an ETL-like workflow, all within a lightweight bash script.
4. Quick dashboard and statistics — Get instant record counts and summary metrics via `dashboard` or `stats` without writing custom aggregation scripts.
5. Data cleanup and deduplication — Use `clean` to remove duplicate records and normalize the data store before exporting or feeding into downstream analysis.

💡 Examples

# Import a data file
logbook import transactions.csv
Query the data store
logbook query "status=active"
View the data schema
logbook schema
Preview first 5 records
logbook sample
Get basic statistics (record count)
logbook stats
Transform data between formats
logbook transform raw.csv cleaned.csv
Validate data integrity against schema
logbook validate
Quick dashboard view
logbook dashboard
Export results
logbook export results.json
Clean and deduplicate the data store
logbook clean
Show version
logbook version

Trigger	Action
2. Data validation workflows — Validate incoming data against the built-in schema (`id, name, value, timestamp`) before processing to catch format issues early.
3. Data transformation pipelines — Transform data between formats or structures as part of an ETL-like workflow, all within a lightweight bash script.
4. Quick dashboard and statistics — Get instant record counts and summary metrics via `dashboard` or `stats` without writing custom aggregation scripts.
5. Data cleanup and deduplication — Use `clean` to remove duplicate records and normalize the data store before exporting or feeding into downstream analysis.