status
Implement — Show experiment dashboard with results, active loops, and progress.
Quellinformationen
- Repository
- thiagofernandes1987-create/APEX
- Letzte Quellaktivität
- 18. April 2026 um 09:35
- Erkannte Sprache von SKILL.md
- Englisch
- Sterne
- 2
- Forks
- 0
Installationsoptionen
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
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SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau- name
- status
- description
- Implement — Show experiment dashboard with results, active loops, and progress.
- command
- /ar:status
- executor
- LLM_BEHAVIOR
- skill_id
- engineering.cs_engineering.autoresearch_agent.status
- status
- ADOPTED
- security
- {"level":"standard","pii":false,"approval_required":false}
- anchors
- ["engineering","visualization","research"]
- tier
- 2
- input_schema
- [{"name":"code_or_task","type":"string","description":"Code snippet, script, or task description to process","required":true}]
- output_schema
- [{"name":"result","type":"string","description":"Primary output from status"}]
# /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 —
```
---
## Why This Skill Exists
Implement — Show experiment dashboard with results, active loops, and progress.
<!-- 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 status 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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