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Skill

/ar-status

Show experiment dashboard with results, active loops, and progress. Use when the user runs /ar:ar-status or asks how an autoresearch experiment is going.

From plugin
alirezarezvani-claude-skills
26k200 skills116 agents150 commands2 MCP
Install
$ npx -y skills add alirezarezvani/claude-skills --skill ar-status --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/ar-status

Context preview

The summary Claude sees to decide when to auto-load this skill.

Show experiment dashboard with results, active loops, and progress. Use when the user runs /ar:ar-status or asks how an autoresearch experiment is going.

SKILL.md

ar-status.SKILL.md
name: "ar-status"
description: "Show experiment dashboard with results, active loops, and progress. Use when the user runs /ar:ar-status or asks how an autoresearch experiment is going."
command: /ar:ar-status

/ar:ar-status — Experiment Dashboard

Show experiment results, active loops, and progress across all experiments.

Usage

/ar:ar-status                                  # Full dashboard
/ar:ar-status engineering/api-speed            # Single experiment detail
/ar:ar-status --domain engineering             # All experiments in a domain
/ar:ar-status --format markdown                # Export as markdown
/ar:ar-status --format csv --output results.csv  # Export as CSV

What It Does

Single experiment

python {skill_path}/scripts/log_results.py --experiment {domain}/{name}

Also check for active loop:

cat .autoresearch/{domain}/{name}/loop.json 2>/dev/null

If loop.json exists, show:

Active loop: every {interval} (cron ID: {id}, started: {date})

Domain view

python {skill_path}/scripts/log_results.py --domain {domain}

Full dashboard

python {skill_path}/scripts/log_results.py --dashboard

For each experiment, also check for loop.json and show loop status.

Export

# CSV
python {skill_path}/scripts/log_results.py --dashboard --format csv --output {file}

# Markdown
python {skill_path}/scripts/log_results.py --dashboard --format markdown --output {file}

Output Example

DOMAIN          EXPERIMENT          RUNS  KEPT  BEST         CHANGE    STATUS   LOOP
engineering     api-speed            47    14   185ms        -76.9%    active   every 1h
engineering     bundle-size          23     8   412KB        -58.3%    paused   —
marketing       medium-ctr           31    11   8.4/10       +68.0%    active   daily
prompts         support-tone         15     6   82/100       +46.4%    done     —
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