---
name: reanudar-experimento-autoresearch
description: Sub-skill del agente autoresearch que retoma un experimento de optimización pausado o interrumpido, cargando todo el historial y contexto para continuar iterando de forma autónoma. Útil cuando se agota el contexto o se quiere reanudar un loop de mejora continua sobre cualquier archivo.
license: MIT
metadata:
  id: 8d25732b
  slug: reanudar-experimento-autoresearch
  titulo: "Reanudar Experimento de Optimización Autónoma"
  servicio: Agentes-IA
  categoria_recurso: Automatizacion
  tipo: automatizacion
  nivel: avanzado
  idioma: es
  idioma_original: en
  acceso: gratis
  precio_eur: 0
  plataformas: [Claude Code, git]
  dependencias: [autoresearch-agent, setup_experiment.py, run_experiment.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: "Reanuda un experimento de optimización autónoma pausado, restaurando el historial completo de iteraciones y el estado del agente."
    como_lo_hace: "Hace checkout de la rama del experimento, lee config, estrategia y resultados previos en TSV, reporta el estado actual y ofrece continuar con una iteración única o en loop automático."
  content_hash: "8d25732bfc656b24841cbbd1cf376a92359fadc1347e92638a9a73bfb018adca"
  version: 1.0.0
---

# /ar:resume — Resume Experiment

Resume a paused or context-limited experiment. Reads all history and continues where you left off.

## Usage

```
/ar:resume                                  # List experiments, let user pick
/ar:resume engineering/api-speed            # Resume specific experiment
```

## What It Does

### Step 1: List experiments if needed

If no experiment specified:

```bash
python {skill_path}/scripts/setup_experiment.py --list
```

Show status for each (active/paused/done based on results.tsv age). Let user pick.

### Step 2: Load full context

```bash
# Checkout the experiment branch
git checkout autoresearch/{domain}/{name}

# Read config
cat .autoresearch/{domain}/{name}/config.cfg

# Read strategy
cat .autoresearch/{domain}/{name}/program.md

# Read full results history
cat .autoresearch/{domain}/{name}/results.tsv

# Read recent git log for the branch
git log --oneline -20
```

### Step 3: Report current state

Summarize for the user:

```
Resuming: engineering/api-speed
  Target: src/api/search.py
  Metric: p50_ms (lower is better)
  Experiments: 23 total — 8 kept, 12 discarded, 3 crashed
  Best: 185ms (-42% from baseline of 320ms)
  Last experiment: "added response caching" → KEEP (185ms)

  Recent patterns:
  - Caching changes: 3 kept, 1 discarded (consistently helpful)
  - Algorithm changes: 2 discarded, 1 crashed (high risk, low reward so far)
  - I/O optimization: 2 kept (promising direction)
```

### Step 4: Ask next action

```
How would you like to continue?
  1. Single iteration (/ar:run)  — I'll make one change and evaluate
  2. Start a loop (/ar:loop)     — Autonomous with scheduled interval
  3. Just show me the results    — I'll review and decide
```

If the user picks loop, hand off to `/ar:loop` with the experiment pre-selected.
If single, hand off to `/ar:run`.