ar-status — Claude Code skill: autoresearch experiment dashboard
Shows results, active loops and progress across autoresearch experiments. Useful once you have enough runs that keeping track in your head stops working.
/plugin marketplace add alirezarezvani/claude-skills
/plugin install autoresearch-agent@claude-code-skillsWhat ar-status does
The skill prints a summary per experiment: run count, how many results were kept, best value, change and status. It calls log_results.py with --dashboard to build that table. Next to each experiment it reads .autoresearch/{domain}/{name}/loop.json and, when the file is there, adds a line about the active loop with interval, cron ID and start date.
You can narrow the view. --domain engineering lists experiments from one area, while a name like engineering/api-speed gives the detail for a single run. Exports go through --format markdown or --format csv plus --output {file}.
Everything runs on Python, since the logic lives in scripts/log_results.py. No interpreter, no output. Data comes from the .autoresearch directory, so in a project with no experiments the dashboard stays empty.
What it helps with
- see which experiments are still running and which are done
- compare the best result against the starting point
- check whether a loop is scheduled and how often it fires
- export the summary to CSV for a spreadsheet
- review every experiment in one domain at once
Example prompts for the agent
- /ar:ar-status
- /ar:ar-status engineering/api-speed
- /ar:ar-status --domain engineering --format markdown
How to install ar-status
1Plugin marketplace
/plugin marketplace add alirezarezvani/claude-skills/plugin install autoresearch-agent@claude-code-skillsThis installs the whole autoresearch-agent plugin with all its skills
2Skills CLI (Vercel)
npx skills add alirezarezvani/claude-skills --skill ar-status -a claude-code3Manually: put the files in place
~/.claude/skills/ar-status/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/ar-status/
Manually: put the files in place
.agents/skills/ar-status/SKILL.mdFor all projects use ~/.agents/skills/
Manually: put the files in place
.cursor/skills/ar-status/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install alirezarezvani/claude-skills ar-status2Manually: put the files in place
.github/skills/ar-status/SKILL.mdPlatform CLI
gemini skills install https://github.com/alirezarezvani/claude-skills --path engineering/autoresearch-agent/skills/ar-statusManually: put the files in place
.devin/skills/ar-status/SKILL.mdThe legacy .windsurf/skills/ path still works
Claude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about ar-status
How do I install ar-status in Claude?
From the marketplace: run /plugin marketplace add alirezarezvani/claude-skills first, then /plugin install autoresearch-agent@claude-code-skills.
What does ar-status do?
It renders an autoresearch dashboard: runs, kept results, best value and status for each experiment, plus the state of any active loop.
What does ar-status need to work?
Python to run scripts/log_results.py, and an .autoresearch directory holding the experiment data. Without them the dashboard comes back empty.
Source text
SKILL.md · MIT
---
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."
metadata:
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
```bash
python {skill_path}/scripts/log_results.py --experiment {domain}/{name}
```
Also check for active loop:
```bash
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
```bash
python {skill_path}/scripts/log_results.py --domain {domain}
```
### Full dashboard
```bash
python {skill_path}/scripts/log_results.py --dashboard
```
For each experiment, also check for loop.json and show loop status.
### Export
```bash
# 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 —
```
© Alireza Rezvani · MIT · alirezarezvani/claude-skills