Status

What Is Status?

The Status skill is an integral component of the Claude Code ecosystem, specifically designed to provide a comprehensive experiment dashboard for the autoresearch-agent. It delivers real-time visibility into experiment results, active automation loops, and overall progress across all tracked experiments. By consolidating this information into a unified, easily accessible interface, Status empowers developers and researchers to monitor, audit, and export experiment data with minimal overhead.

Why Use Status?

In modern research and engineering workflows, running multiple experiments in parallel is common—but tracking their outcomes, progress, and automation status can quickly become unwieldy. The Status skill addresses this challenge by:

• Centralizing Experiment Data: Consolidates experiment logs, loop states, and results into a single dashboard, reducing context switching and manual tracking.
• Real-Time Monitoring: Instantly displays which experiments are running, scheduled, or completed, minimizing human error and missed updates.
• Actionable Insights: Presents key metrics such as the number of runs, best results, and recent changes, enabling data-driven decisions and rapid iteration.
• Automation Transparency: Clearly indicates active experiment loops, including interval schedules and unique identifiers, fostering reproducibility and operational clarity.
• Flexible Exporting: Supports exporting dashboards in Markdown or CSV formats for reporting, sharing, or further analysis.

How to Get Started

The Status skill is invoked via the /ar:status command, which can be tailored to various scopes and output formats. To begin:

1. Install and Configure:

   • Ensure the autoresearch-agent and its dependencies are installed per the official documentation.
   • Place experiments in the expected directory structure, e.g., .autoresearch/{domain}/{experiment}.

2. Basic Usage:

   • To display the full experiment dashboard:
     plaintext

     /ar:status

   • For a detailed view of a single experiment:
     plaintext

     /ar:status engineering/api-speed

   • To filter by domain:
     plaintext

     /ar:status --domain engineering

3. Exporting Results:

   • To export the dashboard as Markdown:
     plaintext

     /ar:status --format markdown --output results.md

   • For CSV export:
     plaintext

     /ar:status --format csv --output results.csv

4. Direct Script Usage:

   • Underlying these commands is a Python script. Direct calls can be made for advanced integration:
     bash

     python {skill_path}/scripts/log_results.py --dashboard
     python {skill_path}/scripts/log_results.py --experiment engineering/api-speed
     python {skill_path}/scripts/log_results.py --domain engineering

Key Features

1. Comprehensive

Dashboard

The core function of Status is to display a dashboard summarizing all experiments, their results, and loop status. For example:

DOMAIN       EXPERIMENT        RUNS  KEPT  BEST     CHANGE    STATUS   LOOP
engineering  api-speed        38    7     95.2%    +1.2%     active   every 1h (cron ID: 1234, started: 2024-05-01)
engineering  data-cleaning    12    2     88.0%    -0.5%     stopped  -

This overview provides immediate insight into experiment health and automation status.

2. Single Experiment and Domain

View

Focus on details at granularity you need:

• Single Experiment:
  plaintext

  /ar:status engineering/api-speed

  Shows run logs and, if present, the active loop configuration (from loop.json).
• Domain View:
  plaintext

  /ar:status --domain engineering

  Aggregates results for all experiments in the domain.

3. Active Loop

Detection

Status automatically checks for the presence of .autoresearch/{domain}/{experiment}/loop.json. If found, it parses and displays the loop interval, cron ID, and start date, making it easy to see which experiments are automated and their schedules.

4. Export

Capabilities

Easily export dashboards:

• Markdown: For documentation, sharing in wikis, or reports.
• CSV: For data analysis or integration with spreadsheets and BI tools.

Example export command:

/ar:status --format csv --output results.csv

5. Scriptable

Integration

Use the underlying Python scripts for custom workflows or automation:

python skills/status/scripts/log_results.py --dashboard --format csv --output results.csv

Best Practices

• Regularly Monitor the Dashboard: Integrate /ar:status into your daily workflow or CI/CD pipelines to maintain situational awareness of ongoing experiments.
• Automate Exports: Schedule periodic exports of the dashboard for archiving, compliance, or reporting purposes.
• Leverage Domain Filtering: Use the --domain option to focus on relevant subsets of experiments, especially in large projects.
• Check Active Loops: Always review the loop status before making manual changes to experiments to avoid conflicts or duplicated runs.
• Version Control Outputs: Store exported CSV or Markdown files in version control for traceability and historical analysis.

Important Notes

• Access Control: Ensure appropriate permissions are set for the .autoresearch directory and experiment data to protect sensitive results.
• Data Consistency: Only modify experiment data and loop configurations via approved workflows to prevent dashboard inconsistencies.
• Output Accuracy: The accuracy of the dashboard is dependent on proper log and loop management. Corrupted or missing files can lead to incomplete or misleading displays.
• Skill Updates: As the Status skill evolves, periodically check the official repository for updates, bug fixes, and new features.
• Resource Utilization: Running frequent or numerous automated loops may impact system resources. Monitor usage and adjust intervals as needed.

The Status skill streamlines experiment monitoring and reporting, making it an essential tool for data-driven research and engineering teams.