Generate Outline

Course Outline Creator (with File Conversion + Compression)

Generate a comprehensive course outline and research dossier from a topic and reference materials. Automatically converts non-PDF files (PPTX, DOCX, etc.) to PDF using LibreOffice, then compresses large PDFs (>5MB) using Ghostscript before uploading to VectorShift.

Slash Command Usage

/generate-outline "<topic>" --materials <folder> [options]

Examples:

• /generate-outline "Cardiovascular Longevity" --materials "course materials/"
• /generate-outline "Peptide Therapy Fundamentals" --materials "/path/to/materials/" --dry-run
• /generate-outline "Topic" --materials "/materials/" --outline existing_outline.md

Supported File Types

Requirements

Execution Instructions

When this skill is invoked, execute the following steps:

1. Parse arguments from the skill invocation
2. Run dry-run first (unless user explicitly skipped) to show file sizes and processing plan
3. Confirm with user before making API calls (especially for large uploads)
4. Execute the generator:

python3 "/Users/anantvinjamoori/Vectorshift Pipelines/cli/outline_creator_runner.py" \
    "{topic}" \
    --materials "{materials_folder}" \
    {--outline file if specified} \
    {--dry-run if specified} \
    {--output folder if specified}

When to Use This Skill

Use this skill when the user:

• Wants to create a course outline from reference materials
• Has PowerPoint presentations, Word docs, or PDFs to process
• Has large files (>5MB) that need compression before upload
• Says "create an outline", "generate course outline", "build curriculum"
• Has a folder of research papers/protocols/presentations for a new course
• Invokes /generate-outline

Pipeline Information

Architecture

Materials → [4 Parallel Extractors] → [Combiner] → [Perplexity] → [Outline]

Files are automatically split into 4 batches for parallel processing, preventing token limit issues with large material sets.

Required Inputs

1. Topic - The course topic (text string)
2. Materials folder - Folder containing materials (PDF, PPTX, DOCX, etc.)
3. Suggested outline (optional) - Existing outline to revise

Workflow

Step 1: Gather Information

Ask the user for:

• Course topic (required)
• Path to materials folder (required)
• Existing outline file to revise (optional)
• Whether to do a dry run first (recommended for large files)

Step 2: Run Dry Run

Always show a dry run first for large material sets:

python3 "/Users/anantvinjamoori/Vectorshift Pipelines/cli/outline_creator_runner.py" \
    "{topic}" \
    --materials "{materials_folder}" \
    --dry-run

This shows:

• Total number of files found
• File sizes and what will happen to each (convert, compress, or both)
• Tool availability (LibreOffice, Ghostscript)

Step 3: Execute Pipeline

After user confirmation:

python3 "/Users/anantvinjamoori/Vectorshift Pipelines/cli/outline_creator_runner.py" \
    "{topic}" \
    --materials "{materials_folder}"

Options:

• --outline <file> - Existing outline to revise
• --output <folder> - Custom output folder
• --pipeline-id <id> - Override pipeline ID

Step 4: Report Results

After completion, show the user:

• Location of output files
• Duration
• Any errors or warnings

Output Files

Generated files are saved to outputs/outlines/{topic_name}/:

File Processing Pipeline

PPTX/DOCX/etc. ──[LibreOffice]──> PDF ──[Ghostscript]──> Compressed PDF ──> Upload
     PDF ──────────────────────────────[Ghostscript]──> Compressed PDF ──> Upload

Compression Stats

Compression quality: ebook (150dpi) - balances quality and size

Error Handling

Output Retrieval Fallback

If output capture fails locally, the task_id fallback triggers automatically—no manual intervention required.

VectorShift confirmed: The task_id returned when submitting a job can be used directly to query results (task_id == span_id for queries).

Automatic Recovery

The runner script stores the task_id and automatically retries fetching results on timeout.

Manual Recovery (if needed)

If you have a task_id from a previous run:

# Use task_id directly (same as span_id)
python3 "/Users/anantvinjamoori/Vectorshift Pipelines/cli/fetch_by_span_id.py" \
    695f56da724cd19d980e4f1d \
    <TASK_ID> \
    --output-dir ./output

See vectorshift-pipeline-deployment.md for details.

Example Usage

Via slash command:

/generate-outline "Cardiovascular Longevity" --materials "course materials/"
/generate-outline "Topic" --materials "/materials/" --dry-run
/generate-outline "Topic" --materials "/materials/" --outline existing.md

Via natural language:

User: "I have a folder at 'course materials/' with PDFs and PowerPoints. Create an outline for a peptide therapy course."

Claude: First runs dry-run to show file analysis, confirms tools are available, then confirms with user before executing.

User: "Some files are over 100MB"

Claude: Shows compression/conversion analysis, confirms both LibreOffice and Ghostscript are available, proceeds with processing.

Execution Notes

• Pipeline takes 10-20 minutes depending on material size
• Non-PDF files are converted to PDF first (may take 1-2 min per large file)
• Large PDFs are compressed before upload to avoid timeouts
• Progress is displayed during polling
• Output folder is created automatically
• Existing outline file (if provided) will be used as a guide for revision