
ai-automation-workflows
Build automated AI workflows combining multiple models and services. Patterns: batch processing, scheduled tasks, event-driven pipelines, agent loops. Tools: inference.sh CLI, bash scripting, Python SDK, webhook integration. Use for: content automation, data processing, monitoring, scheduled generation. Triggers: ai automation, workflow automation, batch processing, ai pipeline, automated content, scheduled ai, ai cron, ai batch job, automated generation, ai workflow, content at scale, automation script, ai orchestration
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/101-skills/superpowers --skill ai-automation-workflows- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads ai-automation-workflows automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Install the belt CLI skill:
npx skills add belt-sh/cli
AI Automation Workflows
Build automated AI workflows via inference.sh CLI.

Quick Start
Requires inference.sh CLI (
belt). Install instructions
belt login
# Simple automation: Generate daily image
belt app run falai/flux-dev --input '{
"prompt": "Inspirational quote background, minimalist design, date: '"$(date +%Y-%m-%d)"'"
}'
Automation Patterns
Pattern 1: Batch Processing
Process multiple items with the same workflow.
#!/bin/bash
# batch_images.sh - Generate images for multiple prompts
PROMPTS=(
"Mountain landscape at sunrise"
"Ocean waves at sunset"
"Forest path in autumn"
"Desert dunes at night"
)
for prompt in "${PROMPTS[@]}"; do
echo "Generating: $prompt"
belt app run falai/flux-dev --input "{
\"prompt\": \"$prompt, professional photography, 4K\"
}" > "output_${prompt// /_}.json"
sleep 2 # Rate limiting
done
Pattern 2: Sequential Pipeline
Chain multiple AI operations.
#!/bin/bash
# content_pipeline.sh - Full content creation pipeline
TOPIC="AI in healthcare"
# Step 1: Research
echo "Researching..."
RESEARCH=$(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: $ARTICLE\"
}")
echo "Pipeline complete!"
Pattern 3: Parallel Processing
Run multiple operations simultaneously.
#!/bin/bash
# parallel_generation.sh - Generate multiple assets in parallel
# Start all jobs in background
belt app run falai/flux-dev --input '{"prompt": "Hero image..."}' > hero.json &
PID1=$!
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.
#!/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.
#!/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
# 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 "$OUTPUT_DIR"
# Generate daily quote image
belt app run falai/flux-dev --input '{
"prompt": "Motivational quote background, minimalist, morning vibes"
}' > "$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: $DATE"
Monitoring and Logging
Logging Wrapper
#!/bin/bash
# logged_workflow.sh - With comprehensive logging
LOG_FILE="/var/log/ai-workflow-$(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 ${DURATION}s"
Error Alerting
#!/bin/bash
# monitored_workflow.sh - With error alerts
run_with_alert() {
local result
result=$("$@" 2>&1)
local status=$?
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 $status
}
run_with_alert belt app run falai/flux-dev --input '{"prompt": "test"}'
Python SDK Automation
#!/usr/bin/env python3
# automation.py - Python-based workflow
import subprocess
import json
from datetime import datetime
from pathlib import Path
def run_infsh(app_id: str, input_data: dict) -> dict:
"""Run inference.sh app and return result."""
result = subprocess.run(
["belt", "app", "run", app_id, "--input", json.dumps(input_data)],
capture_output=True,
text=True
)
return json.loads(result.stdout) if result.returncode == 0 else None
def daily_content_pipeline():
"""Generate daily content."""
date_str = datetime.now().strftime("%Y-%m-%d")
output_dir = Path(f"output/{date_str}")
output_dir.mkdir(parents=True, exist_ok=True)
# Generate image
image = run_infsh("falai/flux-dev", {
"prompt": f"Daily inspiration for {date_str}, beautiful, uplifting"
})
(output_dir / "image.json").write_text(json.dumps(image))
# Generate caption
caption = run_infsh("openrouter/claude-haiku-45", {
"prompt": "Write an inspiring caption for a daily motivation post. 2-3 sentences."
})
(output_dir / "caption.json").write_text(json.dumps(caption))
print(f"Generated content for {date_str}")
if __name__ == "__main__":
daily_content_pipeline()
Workflow Templates
Content Calendar Automation
#!/bin/bash
# content_calendar.sh - Generate week of content
TOPICS=("productivity" "wellness" "technology" "creativity" "leadership")
DAYS=("Monday" "Tuesday" "Wednesday" "Thursday" "Friday")
for i in "${!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
#!/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
- Rate limiting - Add delays between API calls
- Error handling - Always check return codes
- Logging - Track all operations
- Idempotency - Design for safe re-runs
- Monitoring - Alert on failures
- Backups - Save intermediate results
- 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@infsh-cli
Browse all apps: belt app list
FAQ
What does the ai-automation-workflows skill do?
Build automated AI workflows combining multiple models and services. Patterns: batch processing, scheduled tasks, event-driven pipelines, agent loops. Tools: inference.sh CLI, bash scripting, Python SDK, webhook integration. Use for: content automation, data processing, monitoring, scheduled generation. Triggers: ai automation, workflow automation, batch processing, ai pipeline, automated content, scheduled ai, ai cron, ai batch job, automated generation, ai workflow, content at scale, automation script, ai orchestration
How do I install ai-automation-workflows?
Run npx skills add https://github.com/101-skills/superpowers --skill ai-automation-workflows in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/ai-automation-workflows/ (personal) or .claude/skills/ai-automation-workflows/ (project).
Which AI agents can use ai-automation-workflows?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is ai-automation-workflows safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains ai-automation-workflows?
It is published by 101-skills in 101-skills/superpowers. It has 148 recorded installs.
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