Detalle del Skill

resume

Narrow experiment-loop component.

CoincidenciaPosibleRevisado para ingeniería
Fuentealirezarezvani/claude-skillsFuente externa
Instalaciones reportadasNo reportadoSolo señal de popularidad

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La revisión automática comprueba relevancia, no seguridad ni respaldo. Lee las instrucciones de la fuente antes de usar este Skill.

Vista previa guardada

SKILL.md

Este extracto es una copia guardada durante la revisión. La fuente externa contiene la versión completa y actual.

---
name: "resume"
description: "Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating. Use when the user runs /ar:resume or asks to pick up a previously started autoresearch experiment."
command: /ar:resume
---

# /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`.
Leer la fuente completa en GitHub (abre una página externa)
Contexto

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