Ai Automation Workflows

AI Automation Workflows

Build automated AI workflows via inference.sh CLI.

!AI Automation Workflows

Quick Start

curl -fsSL https://cli.inference.sh | sh && infsh login
Simple automation: Generate daily image
infsh app run falai/flux-dev --input '{
  "prompt": "Inspirational quote background, minimalist design, date: '"$(date +%Y-%m-%d)"'"
}'

> Install note: The install script only detects your OS/architecture, downloads the matching binary from dist.inference.sh, and verifies its SHA-256 checksum. No elevated permissions or background processes. Manual install & verification available.

Automation Patterns

Pattern 1: Batch Processing

Process multiple items with the same workflow.

#!/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: $prompt"
  infsh app run falai/flux-dev --input "{
    \"prompt\": \"$prompt, professional photography, 4K\"
  }" > "output_${prompt// /_}.json"
  sleep 2  # Rate limiting
done

Pattern 2: Sequential Pipeline

Chain multiple AI operations.

#!/bin/bash
content_pipeline.sh - Full content creation pipeline
TOPIC="AI in healthcare"
Step 1: Research
echo "Researching..."
RESEARCH=$(infsh app run tavily/search-assistant --input "{
  \"query\": \"$TOPIC latest developments\"
}")
Step 2: Write article
echo "Writing article..."
ARTICLE=$(infsh app run openrouter/claude-sonnet-45 --input "{
  \"prompt\": \"Write a 500-word blog post about $TOPIC based on: $RESEARCH\"
}")
Step 3: Generate image
echo "Generating image..."
IMAGE=$(infsh app run falai/flux-dev --input "{
  \"prompt\": \"Blog header image for article about $TOPIC, modern, professional\"
}")
Step 4: Generate social post
echo "Creating social post..."
SOCIAL=$(infsh 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.

#!/bin/bash
parallel_generation.sh - Generate multiple assets in parallel
Start all jobs in background
infsh app run falai/flux-dev --input '{"prompt": "Hero image..."}' > hero.json &
PID1=$!
infsh app run falai/flux-dev --input '{"prompt": "Feature image 1..."}' > feature1.json &
PID2=$!
infsh 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.

#!/bin/bash
conditional_workflow.sh - Process based on content analysis
INPUT_TEXT="$1"
Analyze content
ANALYSIS=$(infsh 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..."
    infsh app run falai/flux-dev --input '{"prompt": "Celebration, success, happy"}'
    ;;
  *negative*)
    echo "Generating supportive message..."
    infsh 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.

#!/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 $attempt..."
    result=$(infsh app run falai/flux-dev --input "{\"prompt\": \"$prompt\"}" 2>&1)
    if [ $? -eq 0 ]; then
      echo "$result"
      return 0
    fi
    echo "Failed, retrying..."
    ((attempt++))
    sleep $((attempt * 2))  # Exponential backoff
  done
  # Fallback to different model
  echo "Falling back to alternative model..."
  infsh app run google/imagen-3 --input "{\"prompt\": \"$prompt\"}"
}
generate_with_retry "A beautiful sunset over mountains"

Scheduled Automation

Cron Job Setup

# 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

#!/bin/bash
daily_content.sh - Run daily at 9 AM
DATE=$(date +%Y-%m-%d)
OUTPUT_DIR="/output/$DATE"
mkdir -p "$OUTPUT_DIR"
Generate daily quote image
infsh app run falai/flux-dev --input '{
  "prompt": "Motivational quote background, minimalist, morning vibes"
}' > "$OUTPUT_DIR/quote_image.json"
Generate daily tip
infsh 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)
infsh app run twitter/post-tweet --input "{...}"
echo "Daily content generated: $DATE"

Monitoring and Logging

Logging Wrapper

#!/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=$(date +%s)
Run workflow
log "Generating image..."
RESULT=$(infsh 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

#!/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 infsh app run falai/flux-dev --input '{"prompt": "test"}'

Python SDK Automation

#!/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(
        ["infsh", "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

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

#!/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
  infsh 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

# 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@inference-sh

Browse all apps: infsh app list

curl -fsSL https://cli.inference.sh | sh && infsh login
Simple automation: Generate daily image
infsh app run falai/flux-dev --input '{
  "prompt": "Inspirational quote background, minimalist design, date: '"$(date +%Y-%m-%d)"'"
}'

> Install note: The install script only detects your OS/architecture, downloads the matching binary from dist.inference.sh, and verifies its SHA-256 checksum. No elevated permissions or background processes. Manual install & verification available.

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