101-skills/superpowers

ai-automation-workflows

Build automated AI workflows combining multiple models and services.

Quelltext ansehen

Verwendung

  1. Installationsbefehl kopieren und das Quellpaket vor der Installation in der eigenen Agentenumgebung prüfen.
  2. Benötigte Werkzeuge, Zugangsdaten und Hosts im Original prüfen.
  3. Eingabe und gewünschtes Ergebnis beschreiben und die Ausgabe mit den Quellen abgleichen.
npx skills add https://github.com/101-skills/superpowers --skill ai-automation-workflows

Vor dem Start

  • Die Installation umfasst weder Modellabo noch API-Kontingent oder Dateizugriffsrechte.
  • Sterne und Installationen zeigen Aktivität, keine unabhängige Qualitäts- oder Sicherheitsprüfung.
Originales Skill-Dokument

Aus dem Quell-Repository gerendert; Überschriften, Beispiele, Code, Tabellen, Links und Bilder bleiben erhalten.

Install the belt CLI skill: npx skills add belt-sh/cli

AI Automation Workflows

Build automated AI workflows via inference.sh CLI.

AI Automation Workflows
AI Automation Workflows

Quick Start

Requires inference.sh CLI (belt). Install instructions
bash
belt login

# Simple automation: Generate daily image
belt app run falai/flux-dev --input '{
  "prompt": "Inspirational quote background, minimalist design, date: '"$(date +%Y-%m-%d)"'"
}'

Automation Patterns

Pattern 1: Batch Processing

Process multiple items with the same workflow.

bash
#!/bin/bash
# batch_images.sh - Generate images for multiple prompts

PROMPTS=(
  "Mountain landscape at sunrise"
  "Ocean waves at sunset"
  "Forest path in autumn"
  "Desert dunes at night"
)

for prompt in "${PROMPTS[@]}"; do
  echo "Generating: $prompt"
  belt app run falai/flux-dev --input "{
    \"prompt\": \"$prompt, professional photography, 4K\"
  }" > "output_${prompt// /_}.json"
  sleep 2  # Rate limiting
done

Pattern 2: Sequential Pipeline

Chain multiple AI operations.

bash
#!/bin/bash
# content_pipeline.sh - Full content creation pipeline

TOPIC="AI in healthcare"

# Step 1: Research
echo "Researching..."
RESEARCH=$(belt app run tavily/search-assistant --input "{
  \"query\": \"$TOPIC latest developments\"
}")

# Step 2: Write article
echo "Writing article..."
ARTICLE=$(belt app run openrouter/claude-sonnet-45 --input "{
  \"prompt\": \"Write a 500-word blog post about $TOPIC based on: $RESEARCH\"
}")

# Step 3: Generate image
echo "Generating image..."
IMAGE=$(belt app run falai/flux-dev --input "{
  \"prompt\": \"Blog header image for article about $TOPIC, modern, professional\"
}")

# Step 4: Generate social post
echo "Creating social post..."
SOCIAL=$(belt app run openrouter/claude-haiku-45 --input "{
  \"prompt\": \"Write a Twitter thread (5 tweets) summarizing: $ARTICLE\"
}")

echo "Pipeline complete!"

Pattern 3: Parallel Processing

Run multiple operations simultaneously.

bash
#!/bin/bash
# parallel_generation.sh - Generate multiple assets in parallel

# Start all jobs in background
belt app run falai/flux-dev --input '{"prompt": "Hero image..."}' > hero.json &
PID1=$!

belt app run falai/flux-dev --input '{"prompt": "Feature image 1..."}' > feature1.json &
PID2=$!

belt app run falai/flux-dev --input '{"prompt": "Feature image 2..."}' > feature2.json &
PID3=$!

# Wait for all to complete
wait $PID1 $PID2 $PID3
echo "All images generated!"

Pattern 4: Conditional Workflow

Branch based on results.

bash
#!/bin/bash
# conditional_workflow.sh - Process based on content analysis

INPUT_TEXT="$1"

# Analyze content
ANALYSIS=$(belt app run openrouter/claude-haiku-45 --input "{
  \"prompt\": \"Classify this text as: positive, negative, or neutral. Return only the classification.\n\n$INPUT_TEXT\"
}")

# Branch based on result
case "$ANALYSIS" in
  *positive*)
    echo "Generating celebration image..."
    belt app run falai/flux-dev --input '{"prompt": "Celebration, success, happy"}'
    ;;
  *negative*)
    echo "Generating supportive message..."
    belt app run openrouter/claude-sonnet-45 --input "{
      \"prompt\": \"Write a supportive, encouraging response to: $INPUT_TEXT\"
    }"
    ;;
  *)
    echo "Generating neutral acknowledgment..."
    ;;
esac

Pattern 5: Retry with Fallback

Handle failures gracefully.

bash
#!/bin/bash
# retry_workflow.sh - Retry failed operations

generate_with_retry() {
  local prompt="$1"
  local max_attempts=3
  local attempt=1

  while [ $attempt -le $max_attempts ]; do
    echo "Attempt $attempt..."

    result=$(belt app run falai/flux-dev --input "{\"prompt\": \"$prompt\"}" 2>&1)

    if [ $? -eq 0 ]; then
      echo "$result"
      return 0
    fi

    echo "Failed, retrying..."
    ((attempt++))
    sleep $((attempt * 2))  # Exponential backoff
  done

  # Fallback to different model
  echo "Falling back to alternative model..."
  belt app run google/imagen-3 --input "{\"prompt\": \"$prompt\"}"
}

generate_with_retry "A beautiful sunset over mountains"

Scheduled Automation

Cron Job Setup

bash
# Edit crontab
crontab -e

# Daily content generation at 9 AM
0 9 * * * /path/to/daily_content.sh >> /var/log/ai-automation.log 2>&1

# Weekly report every Monday at 8 AM
0 8 * * 1 /path/to/weekly_report.sh >> /var/log/ai-automation.log 2>&1

# Every 6 hours: social media content
0 */6 * * * /path/to/social_content.sh >> /var/log/ai-automation.log 2>&1

Daily Content Script

bash
#!/bin/bash
# daily_content.sh - Run daily at 9 AM

DATE=$(date +%Y-%m-%d)
OUTPUT_DIR="/output/$DATE"
mkdir -p "$OUTPUT_DIR"

# Generate daily quote image
belt app run falai/flux-dev --input '{
  "prompt": "Motivational quote background, minimalist, morning vibes"
}' > "$OUTPUT_DIR/quote_image.json"

# Generate daily tip
belt app run openrouter/claude-haiku-45 --input '{
  "prompt": "Give me one actionable productivity tip for today. Be concise."
}' > "$OUTPUT_DIR/daily_tip.json"

# Post to social (optional)
# belt app run twitter/post-tweet --input "{...}"

echo "Daily content generated: $DATE"

Monitoring and Logging

Logging Wrapper

bash
#!/bin/bash
# logged_workflow.sh - With comprehensive logging

LOG_FILE="/var/log/ai-workflow-$(date +%Y%m%d).log"

log() {
  echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1" | tee -a "$LOG_FILE"
}

log "Starting workflow"

# Track execution time
START_TIME=$(date +%s)

# Run workflow
log "Generating image..."
RESULT=$(belt app run falai/flux-dev --input '{"prompt": "test"}' 2>&1)
STATUS=$?

if [ $STATUS -eq 0 ]; then
  log "Success: Image generated"
else
  log "Error: $RESULT"
fi

END_TIME=$(date +%s)
DURATION=$((END_TIME - START_TIME))
log "Completed in ${DURATION}s"

Error Alerting

bash
#!/bin/bash
# monitored_workflow.sh - With error alerts

run_with_alert() {
  local result
  result=$("$@" 2>&1)
  local status=$?

  if [ $status -ne 0 ]; then
    # Send alert (webhook, email, etc.)
    curl -X POST "https://your-webhook.com/alert" \
      -H "Content-Type: application/json" \
      -d "{\"error\": \"$result\", \"command\": \"$*\"}"
  fi

  echo "$result"
  return $status
}

run_with_alert belt app run falai/flux-dev --input '{"prompt": "test"}'

Python SDK Automation

python
#!/usr/bin/env python3
# automation.py - Python-based workflow

import subprocess
import json
from datetime import datetime
from pathlib import Path

def run_infsh(app_id: str, input_data: dict) -> dict:
    """Run inference.sh app and return result."""
    result = subprocess.run(
        ["belt", "app", "run", app_id, "--input", json.dumps(input_data)],
        capture_output=True,
        text=True
    )
    return json.loads(result.stdout) if result.returncode == 0 else None

def daily_content_pipeline():
    """Generate daily content."""
    date_str = datetime.now().strftime("%Y-%m-%d")
    output_dir = Path(f"output/{date_str}")
    output_dir.mkdir(parents=True, exist_ok=True)

    # Generate image
    image = run_infsh("falai/flux-dev", {
        "prompt": f"Daily inspiration for {date_str}, beautiful, uplifting"
    })
    (output_dir / "image.json").write_text(json.dumps(image))

    # Generate caption
    caption = run_infsh("openrouter/claude-haiku-45", {
        "prompt": "Write an inspiring caption for a daily motivation post. 2-3 sentences."
    })
    (output_dir / "caption.json").write_text(json.dumps(caption))

    print(f"Generated content for {date_str}")

if __name__ == "__main__":
    daily_content_pipeline()

Workflow Templates

Content Calendar Automation

bash
#!/bin/bash
# content_calendar.sh - Generate week of content

TOPICS=("productivity" "wellness" "technology" "creativity" "leadership")
DAYS=("Monday" "Tuesday" "Wednesday" "Thursday" "Friday")

for i in "${!DAYS[@]}"; do
  DAY=${DAYS[$i]}
  TOPIC=${TOPICS[$i]}

  echo "Generating $DAY content about $TOPIC..."

  # Image
  belt app run falai/flux-dev --input "{
    \"prompt\": \"$TOPIC theme, $DAY motivation, social media style\"
  }" > "content/${DAY}_image.json"

  # Caption
  belt app run openrouter/claude-haiku-45 --input "{
    \"prompt\": \"Write a $DAY motivation post about $TOPIC. Include hashtags.\"
  }" > "content/${DAY}_caption.json"
done

Data Processing Pipeline

bash
#!/bin/bash
# data_processing.sh - Process and analyze data files

INPUT_DIR="./data/raw"
OUTPUT_DIR="./data/processed"

for file in "$INPUT_DIR"/*.txt; do
  filename=$(basename "$file" .txt)

  # Analyze content
  belt app run openrouter/claude-haiku-45 --input "{
    \"prompt\": \"Analyze this data and provide key insights in JSON format: $(cat $file)\"
  }" > "$OUTPUT_DIR/${filename}_analysis.json"

done

Best Practices

  1. Rate limiting - Add delays between API calls
  2. Error handling - Always check return codes
  3. Logging - Track all operations
  4. Idempotency - Design for safe re-runs
  5. Monitoring - Alert on failures
  6. Backups - Save intermediate results
  7. Timeouts - Set reasonable limits

Related Skills

bash
# Content pipelines
npx skills add inference-sh/skills@ai-content-pipeline

# RAG pipelines
npx skills add inference-sh/skills@ai-rag-pipeline

# Social media automation
npx skills add inference-sh/skills@ai-social-media-content

# Full platform skill
npx skills add inference-sh/skills@infsh-cli

Browse all apps: belt app list

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