resume
Implement — Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating.
Datos de origen
- Repositorio
- thiagofernandes1987-create/APEX
- Última actividad en el origen
- 18 de abril de 2026 a las 09:35
- Idioma detectado de SKILL.md
- inglés
- Estrellas
- 2
- Forks
- 0
Opciones de instalación
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Lee SKILL.md y los archivos complementarios que muestra SkillsMP antes de decidir si quieres instalarlo.
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SKILL.md
Instrucciones de origen · Vista previa de solo lectura- name
- resume
- description
- Implement — Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating.
- command
- /ar:resume
- executor
- LLM_BEHAVIOR
- skill_id
- engineering.cs_engineering.autoresearch_agent.resume
- status
- ADOPTED
- security
- {"level":"standard","pii":false,"approval_required":false}
- anchors
- ["engineering","research"]
- tier
- 2
- input_schema
- [{"name":"code_or_task","type":"string","description":"Code snippet, script, or task description to process","required":true},{"name":"context","type":"string","description":"Additional context or background information","required":false}]
- output_schema
- [{"name":"result","type":"string","description":"Primary output from 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`.
---
## Why This Skill Exists
Implement — Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating.
<!-- SR_40: auto-generated from frontmatter `purpose`/`description` (OPP-Phase3). Expand with domain-specific rationale. -->
## When to Use
Use this skill when the task requires resume capabilities.
<!-- SR_40: auto-generated from frontmatter `when`/`description` (OPP-Phase3). -->
## What If Fails
If this skill fails to produce the expected output: (1) verify input completeness, (2) retry with more specific context, (3) fall back to the parent workflow without this skill.
<!-- SR_40: auto-generated from frontmatter `what_if_fails` (OPP-Phase3). -->
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