ai-automation-workflows

name	ai-automation-workflows
description	AI workflow design. Chaining skills, batch processing, error handling in multi-step pipelines.
allowed-tools	[]
disable-model-invocation	false

AI Automation Workflow Guide

This guide covers how to chain multiple Pocket-Knife skills together into reliable multi-step pipelines.

Core Principles

1. Each skill produces a file or structured output — the output of one skill becomes the input of the next.

2. Use absolute paths throughout — skills that write files need absolute paths; skills that read images need public URLs (upload intermediate files to a public host when required).

3. Fail fast at each step — verify each output before passing it to the next skill. An empty or corrupt file passed forward causes confusing errors later.

4. Name outputs descriptively — use timestamps or meaningful names to avoid overwriting files during batch runs.

Common Pipeline Patterns

Pattern 1: Image → Animated Video

Use case: Turn a product photo or portrait into a short video clip.

Step 1: /pocket-knife:background-removal
  Input:  IMAGE_URL = public URL of source image
  Output: ~/Downloads/bg_removed_[timestamp].png

Step 2: Upload the PNG to a public host → get new public URL

Step 3: /pocket-knife:image-to-video
  Input:  IMAGE_URL = public URL from step 2
          PROMPT    = motion description
  Output: ~/Downloads/i2v_[timestamp].mp4

Verify at each step: Check that the PNG is not empty before uploading; check the MP4 exists and has nonzero size before considering the pipeline complete.

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Pattern 2: Script → Voiced Audio + Music

Use case: Produce a narrated audio piece with background music.

Step 1: /pocket-knife:elevenlabs-tts
  Input:  TEXT = narration script
          VOICE = george
  Output: ~/Downloads/tts_narration.mp3

Step 2: /pocket-knife:elevenlabs-music
  Input:  PROMPT   = background music description
          DURATION = same duration as narration + 5 seconds
  Output: ~/Downloads/music_background.mp3

Step 3: Layer in audio editor (Audacity, DaVinci Resolve)
  - Import both MP3 files
  - Lower music volume to -18dB under voice
  - Export as final mix

---

Pattern 3: Image Generation → Product Photography

Use case: Generate e-commerce product images at scale.

Step 1: /pocket-knife:ai-image-generation
  Input:  PROMPT = product description + background
  Output: ~/Downloads/product_raw_[timestamp].png

Step 2: /pocket-knife:background-removal
  Input:  IMAGE_URL = public URL of generated image
  Output: ~/Downloads/product_nobg_[timestamp].png

Step 3: /pocket-knife:ai-image-generation (composite prompt)
  Input:  PROMPT = product + new studio background description
  Output: ~/Downloads/product_final_[timestamp].png

---

Pattern 4: Audio Transcription → Dialogue Re-voice

Use case: Transcribe an existing recording and re-produce it with different voices.

Step 1: /pocket-knife:elevenlabs-stt
  Input:  AUDIO_FILE = ~/recordings/interview.mp3
          DIARIZE    = true
  Output: Transcript with speaker labels

Step 2: Edit transcript (remove fillers, assign voice names to speaker labels)

Step 3: /pocket-knife:elevenlabs-dialogue
  Input:  SEGMENTS = formatted dialogue lines
  Output: ~/Downloads/dialogue_revoiced.mp3

Batch Processing

To process multiple files with the same skill, loop and vary the output filenames:

# Example: transcribe all MP3s in a folder
for FILE in ~/recordings/*.mp3; do
  BASENAME=$(basename "$FILE" .mp3)
  # Run /pocket-knife:elevenlabs-stt with AUDIO_FILE=$FILE
  # Save transcript to ~/transcripts/${BASENAME}.txt
done

Batch tips:

• Add a 2–3 second delay between API calls to avoid rate limiting
• Log each step's output file path to a manifest file for later processing
• Process in groups of 5–10 items; do not queue hundreds simultaneously

Error Handling in Pipelines

Error scenario	Detection method	Recovery
Empty output file	[ ! -s "$OUTPUT_FILE" ]	Stop pipeline; log the step that failed
Missing API key	Check $? after skill run	Run /pocket-knife:setup and restart
Public URL expired	HTTP 403/404 on image-to-video	Re-upload to a fresh public URL
API rate limit	HTTP 429 response	Wait 30–60 seconds; retry once
Corrupt output	File size far below expected	Delete file; re-run that step only

Pipeline Design Checklist

Before running a multi-step pipeline:

• All required API keys are set (ELEVENLABS_API_KEY, FAL_KEY, GOOGLE_API_KEY)
• Intermediate output directories exist and are writable
• Images that will be passed to video skills have public HTTPS URLs
• Output filenames include timestamps to prevent collisions
• Each step's success condition is verified before proceeding to the next

Tips for Reliable Pipelines

• Test each skill individually before chaining — confirm each one works end-to-end alone first
• Use descriptive filenames — product_bg_removed_20260327.png is easier to debug than output2.png
• Keep intermediate files during development — delete them only after the full pipeline succeeds
• Log everything — print step name, input, output path, and file size at each stage