Shell Gpt
by ckchzh
A command-line productivity tool powered by AI large language models like GPT-5, will help you accom shell gpt, python, chatgpt, cheat-sheet, cli, commands.
安装
claude skill add --url github.com/openclaw/skills/tree/main/skills/ckchzh/shell-ai文档
Shell AI
Terminal-first AI toolkit for configuring, benchmarking, comparing, prompting, evaluating, and fine-tuning AI models — all from the command line.
Why Shell AI?
- Works entirely offline — your data never leaves your machine
- Full AI workflow: configure → prompt → evaluate → benchmark → compare → optimize
- Fine-tuning tracking, cost analysis, and usage monitoring built in
- Export to JSON, CSV, or plain text anytime
- Automatic history and activity logging with timestamps
Getting Started
bash
# See all available commands
shell-ai help
# Check current health status
shell-ai status
# View summary statistics
shell-ai stats
# Show recent activity
shell-ai recent
Commands
| Command | What it does |
|---|---|
shell-ai configure <input> | Configure AI model settings (or view recent configs with no args) |
shell-ai benchmark <input> | Benchmark model performance (or view recent benchmarks) |
shell-ai compare <input> | Compare models or outputs side-by-side (or view recent comparisons) |
shell-ai prompt <input> | Store and manage prompts (or view recent prompts) |
shell-ai evaluate <input> | Evaluate model outputs for quality (or view recent evaluations) |
shell-ai fine-tune <input> | Track fine-tuning jobs and parameters (or view recent fine-tunes) |
shell-ai analyze <input> | Analyze model behavior or outputs (or view recent analyses) |
shell-ai cost <input> | Track API costs and token usage (or view recent cost entries) |
shell-ai usage <input> | Monitor usage patterns and quotas (or view recent usage logs) |
shell-ai optimize <input> | Record optimization strategies (or view recent optimizations) |
shell-ai test <input> | Log test runs and results (or view recent tests) |
shell-ai report <input> | Generate reports on AI activity (or view recent reports) |
shell-ai stats | Show summary statistics across all data categories |
shell-ai export <fmt> | Export all data in a format: json, csv, or txt |
shell-ai search <term> | Search across all log entries for a keyword |
shell-ai recent | Show the 20 most recent activity entries |
shell-ai status | Health check: version, disk usage, entry counts |
shell-ai help | Show the full help message |
shell-ai version | Print current version (v2.0.0) |
Each AI command works in two modes:
- With arguments: saves the input with a timestamp to
<command>.logand logs to history - Without arguments: displays the 20 most recent entries for that command
Data Storage
All data is stored locally at ~/.local/share/shell-ai/:
configure.log,benchmark.log,prompt.log, etc. — one log file per commandhistory.log— unified activity log with timestampsexport.json,export.csv,export.txt— generated export files
Data format: each entry is stored as YYYY-MM-DD HH:MM|<value> (pipe-delimited).
Set the SHELL_AI_DIR environment variable to change the data directory.
Requirements
- Bash 4+ (uses
set -euo pipefail) - Standard UNIX utilities:
wc,du,grep,tail,sed,date,cat,basename - No external dependencies or network access required
When to Use
- Configuring AI models — use
configureto save model parameters, API keys references, and default settings - Benchmarking and comparing models — run
benchmarkandcompareto track performance across different models or prompts - Managing prompts and evaluations — store prompts with
prompt, then evaluate output quality withevaluate - Tracking costs and usage — monitor API spend with
costand usage patterns withusageto stay within budget - Optimizing and fine-tuning — log fine-tuning experiments with
fine-tuneand optimization strategies withoptimize
Examples
bash
# Configure a model
shell-ai configure "model=gpt-4 temperature=0.7 max_tokens=2048"
# Store and evaluate a prompt
shell-ai prompt "Summarize the following article in 3 bullet points"
shell-ai evaluate "gpt-4 summary: accuracy=9/10 coherence=8/10"
# Benchmark and compare
shell-ai benchmark "gpt-4 latency=1.2s tokens/sec=45 cost=$0.03"
shell-ai compare "gpt-4 vs claude-3: gpt-4 faster, claude more detailed"
# Track costs and fine-tuning
shell-ai cost "2024-01 total: $47.20 (gpt-4: $32, claude: $15.20)"
shell-ai fine-tune "job-abc123: 500 samples, 3 epochs, loss=0.42"
# Export everything as CSV, then search
shell-ai export csv
shell-ai search "gpt-4"
# Check overall health
shell-ai status
shell-ai stats
Output
All commands return human-readable output to stdout. Redirect to a file for scripting:
bash
shell-ai stats > report.txt
shell-ai export json
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