---
name: dashboard-experimentos-autoinvestigacion
description: Muestra un panel de control con el estado de todos los experimentos de optimizacion autonoma en curso, incluyendo metricas, bucles activos y tendencias de mejora. Subcomando del agente autoresearch para monitorizar iteraciones de mejora continua de archivos.
license: MIT
metadata:
  id: 6db816a6
  slug: dashboard-experimentos-autoinvestigacion
  titulo: "Dashboard de Experimentos de Autoinvestigacion"
  servicio: Automatizaciones
  categoria_recurso: Automatizacion
  tipo: analisis
  nivel: avanzado
  idioma: es
  idioma_original: en
  acceso: gratis
  precio_eur: 0
  plataformas: [Claude Code, Python, Git]
  dependencias: [autoresearch-agent, log_results.py]
  licencia: { spdx: MIT, redistribuible: true, uso_comercial: true }
  seguridad: { veredicto: seguro, riesgo: bajo, escaneado: "2026-06-12", motor: "grep-estatico+auditor-llm" }
  ficha:
    que_hace: "Presenta un dashboard tabular con resultados, bucles activos e historial de progreso de todos los experimentos de autoresearch en ejecucion."
    como_lo_hace: "Ejecuta log_results.py con flags de dashboard/dominio/experimento y lee loop.json para detectar bucles activos, exportando a markdown o CSV si se solicita."
  content_hash: "6db816a6668331c3b4ddf55c5d645ded88bf858521618a47ffe13b07316448c8"
  version: 1.0.0
---

# /ar:status — Experiment Dashboard

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

## Usage

```
/ar:status                                  # Full dashboard
/ar:status engineering/api-speed            # Single experiment detail
/ar:status --domain engineering             # All experiments in a domain
/ar:status --format markdown                # Export as markdown
/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     —
```