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Guide Origin: Official | ArcKit Version: 4.20.1+ | Status: BETA Licence: PROPRIETARY — not open source, not publicly available

/arckit:deepbook generates comprehensive books using DeepBook's large mode workflow with stateful checkpoints and per-topic generation.

Licence and Availability

DeepBook is proprietary software and is not part of the open-source ArcKit distribution.

Unlike every other plugin documented here, arckit-deepbook is not published in the ArcKit marketplace and is not covered by the licence that governs the rest of ArcKit. It lives in a separate, private repository.

  • Licence: Proprietary and confidential. Copyright (c) 2026 Mark Craddock. All rights reserved.
  • Availability: Private. It cannot be installed from the public ArcKit marketplace.
  • Permission: No right is granted to use, copy, modify, distribute, or sublicense the plugin without prior written consent of the copyright holder.
  • Generated output: Books you generate remain your own. The licence governs the software, not its output.

This guide is published for reference and for licensed users. If you want access, contact the copyright holder — installing it is not a self-service step.

Overview

DeepBook is an autonomous book generation agent that executes a 6-stage workflow to create comprehensive non-fiction books. It replicates DeepBook's large mode behavior with faithful prompt replication and ArcKit formatting.

Source: DeepBook repository, which is owned by the same copyright holder and covered by the same proprietary terms.

Inputs

Argument Required Default Description
Book topic Yes - The subject matter for the book
--sector No government Sector context: government, neutral, healthcare, finance, technology
--reference-url No - URL for grounding knowledge
--reference-file No - Local file path for grounding knowledge
--use-external No - Load external knowledge from ArcKit folders
--external-dir No - Additional custom external directory
--resume No - Resume from existing checkpoint
--auto-approve No - Skip structure review, auto-proceed to generation
--output-dir No ./books/ Custom output directory
--max-topics No - Maximum number of topics to generate
--model No Current model LLM model to use

Command

# Basic book generation
/arckit:deepbook <book topic>

# With sector and auto-approval
/arckit:deepbook "Network Effects and GenAI" --sector technology --auto-approve

# With grounding from URL
/arckit:deepbook "AI Governance" --reference-url https://example.com/ai-guidelines

# With grounding from file
/arckit:deepbook "Cloud Architecture" --reference-file ./cloud-strategy.md

# Resume from checkpoint
/arckit:deepbook --resume

# Limited test with 2 topics
/arckit:deepbook "Test Book" --auto-approve --max-topics 2

# With custom output directory
/arckit:deepbook "Enterprise AI" --output-dir ./output/my-book/

Workflow Stages

Step 0: Initialize State and Check Resume

  • Generate session identity (deepbook-{YYYYMMDD}-{HHMMSS}-{random-6-chars})
  • Create safe book title slug
  • Check for existing checkpoint in .arckit/deepbook-checkpoints/
  • Restore state if --resume flag is present

Checkpoint location: .arckit/deepbook-checkpoints/{session_id}.json

Step 1: Grounding and Topic Refinement

  • Assemble external knowledge from URL, file, or external directories
  • Execute refinement prompt (pp-refine-top-78c850)
  • Extract: Refined Topic, Potential Subtitle, Explanation, Key Areas, Differentiation
  • Save checkpoint at step 1

Prompt: plugins/arckit-deepbook/prompts/refine-topic-large.md Temperature: 0.3

Step 2: Book Structure Generation

  • Execute structure prompt (pp-prepare-bo-87a490) with refined topic and key areas
  • Parse JSON output for chapters, sections, subsections
  • Fallback to text parsing if JSON invalid
  • Create default structure (4 chapters x 4 sections x 4 subsections) if parsing fails
  • Save structure to original_book_structure.json
  • Save checkpoint at step 2

Prompt: plugins/arckit-deepbook/prompts/prepare-book-structure-large.md Temperature: 0.3

Step 3: Structure Review

  • Display formatted book structure as markdown
  • Use AskUserQuestion for user interaction
  • Options: Approve, Reject, Refresh
    • Approve: Mark structure as approved, proceed to Step 4
    • Reject: Clear structure, return to Step 1
    • Refresh: Select specific element (chapter/section/subsection) to regenerate

Refresh prompt: plugins/arckit-deepbook/prompts/update-book-structure.md Temperature: 0.5

Step 4: Topic List and Placeholders

  • Flatten structure into topic list: (subsection, section_title, chapter_title) tuples
  • Initialize state for tracking:
    • all_topics: List of all topic tuples
    • processed_topics: Successfully generated topics
    • failed_topics: Topics that failed generation
    • book_content: Dictionary mapping topics to content
  • Save checkpoint

Step 5: Per-Topic Generation Loop

Main loop processing each topic in order:

  1. Skip if already processed (check processed_topics)
  2. Retry if previously failed (remove from failed_topics)
  3. Check max topics limit (if configured)
  4. Build previous content context:
    • Sort by: same chapter first, most recent first
    • Chapter limit: 50,000 characters
    • Total limit: 200,000 characters
  5. Execute topic expansion (pp-expansion-81fea3)
  6. Validate response (3 attempts):
    • Schema: title (optional), content (required array of 1-200 items)
    • Recovery rules if validation fails
  7. Render markdown from JSON content list
  8. Save topic file: {output_dir}/{session_id}/{safe_book_title}/topics/{slugified_topic}.md
  9. Update state and save checkpoint

Prompt: plugins/arckit-deepbook/prompts/expansion-prompt-large.md Temperature: 0.3

Content types supported:

  • paragraph: Plain text
  • heading: Levels 4 or 5
  • list: Bullet list with items
  • quote: Block quote
  • code: Code block
  • placeholder: Placeholder text

Step 6: Final Book Assembly

Executes when: len(processed_topics) + len(failed_topics) >= len(all_topics)

  1. Combine content:
    • Add title and metadata
    • Generate table of contents with anchor links
    • Concatenate all chapter/section/subsection content
  2. Write outputs:
    • Final book: {output_dir}/{session_id}/{safe_book_title}.md
    • ZIP archive: {output_dir}/{session_id}/{safe_book_title}.zip
    • Topic files: Individual .md files in topics/ directory
    • Structure: original_book_structure.json
  3. Delete checkpoint
  4. Report results: Status, output files, statistics, failed topics

Outputs

File Description Location
{book_title}.md Complete assembled book {output_dir}/{session_id}/
{book_title}.zip ZIP archive with book + topics + structure {output_dir}/{session_id}/
original_book_structure.json Book structure metadata {output_dir}/{session_id}/
topics/*.md Individual topic files {output_dir}/{session_id}/topics/

Sector Contexts

Government

  • Context: Public sector and government digital transformation
  • Example Sources: GOV.UK, Government Digital Service, Cabinet Office, UK Government frameworks
  • Target Audience: Senior civil servants, digital leaders, policy makers, public sector CIOs
  • Guidance: Focus on citizen outcomes, service delivery, policy implementation, and public value

Neutral

  • Context: General business and enterprise
  • Example Sources: Harvard Business Review, McKinsey, BCG, Deloitte Insights
  • Target Audience: Business executives, managers, consultants, entrepreneurs
  • Guidance: Focus on commercial outcomes, ROI, competitive advantage, and market positioning

Healthcare

  • Context: Healthcare and life sciences
  • Example Sources: NHS, WHO, BMJ, medical journals, healthcare providers
  • Target Audience: Clinical leaders, healthcare administrators, health tech professionals, CIOs
  • Guidance: Focus on patient outcomes, clinical effectiveness, regulatory compliance, and care quality

Finance

  • Context: Financial services and fintech
  • Example Sources: Bank of England, FCA, financial institutions, fintech startups
  • Target Audience: CFOs, risk officers, fintech entrepreneurs, regulators, compliance officers
  • Guidance: Focus on risk management, compliance, financial innovation, and market stability

Technology

  • Context: Technology and digital innovation
  • Example Sources: TechCrunch, Gartner, IEEE, major tech companies, CTO publications
  • Target Audience: CTOs, engineers, product managers, innovators, architects
  • Guidance: Focus on technical depth, innovation, practical implementation, and scalability

Checkpoint Format

{
  "version": "1.0",
  "session_id": "deepbook-20260704-143022-abc123",
  "initial_topic": "User's original topic",
  "refined_topic": "Refined topic from Step 1",
  "key_areas": ["area 1", "area 2", ...],
  "book_topic": "Final topic used for structure",
  "book_structure": { ... },
  "is_structure_created": false,
  "is_structure_approved": false,
  "all_topics": [["subsection", "section", "chapter"], ...],
  "processed_topics": [["subsection", "section", "chapter"], ...],
  "failed_topics": [["subsection", "section", "chapter"], ...],
  "book_content": {},
  "generation_config": {
    "sector": "technology",
    "model": "claude-3-5-sonnet-latest",
    "detail_level": "large",
    "temperature": 0.3
  },
  "checkpoint_step": 0,
  "cost_tracking": {
    "total_prompt_tokens": 0,
    "total_completion_tokens": 0,
    "api_calls": 0,
    "retry_count": 0
  },
  "settings": {
    "output_dir": "./books/",
    "reference_url": null,
    "reference_file": null,
    "auto_approve": false,
    "max_topics": null
  },
  "created_at": "2026-07-04T14:30:22Z",
  "updated_at": "2026-07-04T14:30:22Z"
}

File Loading Rules

Supported extensions: .md, .txt, .json, .yaml, .yml

Constraints:

  • Skip files > 10MB
  • Skip binary files
  • Truncate individual files to first 50,000 characters
  • Max total external knowledge: 200,000 characters
  • Deduplication: First occurrence only

Error Handling

Scenario Action
No topic provided, no resume Stop with error
Prompt rendering fails Retry 3x, then stop
Model API fails (limit reached) Stop with error
Refined topic parsing fails Stop with error
Structure JSON invalid Use text fallback, warn
Structure text fallback fails Create default, warn
Topic expansion validation fails 3x Mark failed, continue
Topic save fails Mark error, continue
Checkpoint corrupted Start fresh, warn
Resume no checkpoint Start fresh, warn

Prompt Inventory

Stage Prompt File DeepBook ID Temperature Output Format
Topic Refinement refine-topic-large.md pp-refine-top-78c850 0.3 Plain text
Book Structure prepare-book-structure-large.md pp-prepare-bo-87a490 0.3 JSON
Topic Expansion expansion-prompt-large.md pp-expansion-81fea3 0.3 JSON
Structure Refresh update-book-structure.md N/A 0.5 Plain text

Best Practices

  1. Start with clear topic: Be specific about the book subject for best results
  2. Use sector context: Select appropriate sector for relevant examples and guidance
  3. Ground with references: Provide URL or file references for factual accuracy
  4. Review structure: Take time to approve or refine the book structure before generation
  5. Set max-topics for testing: Use --max-topics 2 for quick validation
  6. Use auto-approve for automation: Skip interactive prompts with --auto-approve
  7. Monitor checkpoints: Check .arckit/deepbook-checkpoints/ for progress

Example Workflow

1. Start a new book

/arckit:deepbook "AI Governance in Enterprise"
  • DeepBook will refine the topic
  • Generate book structure
  • Present structure for your approval
  • Upon approval, begin per-topic generation

2. Resume interrupted session

/arckit:deepbook --resume
  • Finds most recent checkpoint
  • Restores all state
  • Continues from where it left off

3. Generate with grounding

/arckit:deepbook "Cloud Migration Strategy" \
  --reference-url https://aws.amazon.com/cloud-migration \
  --sector technology \
  --auto-approve
  • Fetches external knowledge from URL
  • Uses technology sector context
  • Skips structure review
  • Auto-generates all topics
Location Purpose
plugins/arckit-deepbook/ Plugin directory
plugins/arckit-deepbook/commands/deepbook.md Main command implementation
plugins/arckit-deepbook/agents/arckit-deepbook.md Autonomous agent
plugins/arckit-deepbook/agents/arckit-deepbook-writer.md Subagent for topic expansion
plugins/arckit-deepbook/prompts/ DeepBook prompt files
docs/plans/2026-07-04-deepbook-large-plan-book-flow.md Technical specification

Version History

  • v1.0.0: Initial release with 3-stage workflow
  • v1.0.1: Added checkpoint resume capability
  • v1.0.2: Improved error handling and validation
  • v4.20.1+: Current production-ready version

Support

For issues or questions:

  • Check the checkpoint file for state information
  • Review the structure JSON for book organization
  • Verify external knowledge sources are accessible
  • Ensure sufficient API quota for large book generation

Status: Production-ready Plugin Type: Private repository plugin Prompt Source: DeepBook's actual prompts, reworked for ArcKit format