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🦀 ClawHub✦ BytesAgain

Agent Learner

by @xueyetianya

Benchmark and compare agent prompts and evaluation results. Use when tuning strategies, evaluating outputs, or comparing configurations.

Versionv2.0.2

Downloads669

Installs2

#finance #legal #education

View on ClawHub →

TERMINAL

clawhub install agent-learner

📖 About This Skill

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 | Export all data in json, csv, or txt format to the data directory | | search | 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/.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. — 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.

⚡ When to Use

Trigger	Action
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

Trigger	Action
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