agent-learner

version	2.0.1
name	agent-learner
description	Benchmark and compare agent prompts and evaluation results. Use when tuning strategies, evaluating outputs, or comparing configurations.
author	BytesAgain
homepage	https://bytesagain.com
source	https://github.com/bytesagain/ai-skills

Agent Learner

An AI toolkit for configuring, benchmarking, comparing, and optimizing agent prompts and evaluation results. Agent Learner provides persistent, file-based logging for each command category with timestamped entries, summary statistics, multi-format export, and full-text search across all records.

Commands

Command	Description
configure	Configure agent settings — log configuration entries or view recent ones
benchmark	Benchmark agent performance — log benchmark results or view history
compare	Compare agent outputs — log comparison data or view recent comparisons
prompt	Prompt management — log prompt variations or view recent prompts
evaluate	Evaluate agent outputs — log evaluation results or view history
fine-tune	Fine-tune parameters — log fine-tuning sessions or view recent ones
analyze	Analyze agent behavior — log analysis entries or view recent analyses
cost	Cost tracking — log cost data or view recent cost entries
usage	Usage monitoring — log usage metrics or view recent usage data
optimize	Optimize configurations — log optimization runs or view history
test	Test agent behavior — log test results or view recent tests
report	Report generation — log report entries or view recent reports
stats	Show summary statistics across all log categories (entry counts, data size, first entry date)
export <fmt>	Export all data in json, csv, or txt format to the data directory
search <term>	Full-text search across all log files (case-insensitive)
recent	Show the 20 most recent entries from the activity history log
status	Health check — show version, data directory, total entries, disk usage, and last activity
help	Show the full help message with all available commands
version	Print the current version string

Each data command (configure, benchmark, compare, etc.) works in two modes:

• Without arguments: displays the 20 most recent entries from that category
• With arguments: saves the input as a new timestamped entry and reports the total count

Data Storage

All data is stored in plain text files under the data directory:

• Category logs: $DATA_DIR/<command>.log — one file per command (e.g., configure.log, benchmark.log, prompt.log), each entry is timestamp|value
• History log: $DATA_DIR/history.log — audit trail of every command executed with timestamps
• Export files: $DATA_DIR/export.<fmt> — generated by the export command in json, csv, or txt format

Default data directory: ~/.local/share/agent-learner/

Requirements

• Bash (with set -euo pipefail support)
• Standard Unix utilities: grep, cat, date, echo, wc, du, head, tail, basename
• No external dependencies or API keys required

When to Use

1. Benchmarking agent performance — When you need to track and compare benchmark results across different agent configurations, models, or prompt strategies
2. Prompt engineering iteration — When you're testing multiple prompt variations and want to log each version with results for later comparison
3. Cost and usage tracking — When you need to monitor API costs and usage metrics over time to optimize spending
4. Fine-tuning experiments — When running fine-tuning sessions and you want to log parameters, results, and observations for reproducibility
5. Cross-category analysis — When you need to search across all logged data (benchmarks, prompts, evaluations, costs) to find patterns or specific entries

Examples

# Initialize and check status
agent-learner status

# Log a benchmark result
agent-learner benchmark "GPT-4o on MMLU: 88.7% accuracy, 1.2s avg latency"

# Log a prompt variation
agent-learner prompt "System: You are a helpful coding assistant. Always explain your reasoning step by step."

# Compare two configurations
agent-learner compare "GPT-4o vs Claude-3.5: GPT-4o 12% faster, Claude 5% more accurate on code tasks"

# Track costs
agent-learner cost "March batch: 12,450 tokens input, 3,200 tokens output, $0.47 total"

# View all recent benchmarks
agent-learner benchmark

# Search across all logs for a specific term
agent-learner search "accuracy"

# Export all data as JSON
agent-learner export json

# View summary statistics
agent-learner stats

# Show recent activity
agent-learner recent

Output

All commands return output to stdout. Export files are written to the data directory:

agent-learner export json   # → ~/.local/share/agent-learner/export.json
agent-learner export csv    # → ~/.local/share/agent-learner/export.csv
agent-learner export txt    # → ~/.local/share/agent-learner/export.txt

Every command execution is logged to $DATA_DIR/history.log for auditing purposes.

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