ai-llm-red-team-handbook/workflows/generate-chapter.md

description

description
Generates a technical documentation chapter for the AI/LLM Security Handbook with embedded structure, automated validation, and strict formatting enforcement.

Optimized Chapter Generation Workflow

This workflow automates the creation of high-quality, compliant handbook chapters. It features embedded structural templates to minimize context switching and mandatory algorithmic validation to ensure zero-defect output.

1. Context & Autonomy

• Role: Technical Documentation Specialist & Code Security Auditor.
• Objective: Production-ready markdown file (e.g., Chapter_38_Continuous_Red_Teaming.md).
• Autonomy Level: High. Make all architectural decisions. Do not ask for user confirmation.
• Speed Focus: Skip "Outline Generation". Use the Standard Structure defined below immediately.

2. Standard Structure (Rigid Enforcement)

Do not deviate from this hierarchy. All headers are mandatory.

1. HTML Header: <!-- Chapter: [N] ... -->
2. H1 Title: # Chapter [N]: [Title]
3. Header Image: ![ ](assets/page_header.svg)
4. Abstract: _Italicized executive summary..._
5. H2 Introduction: ## [N].1 Introduction
   • Sub: ### Why This Matters
   • Sub: ### Key Concepts (Definition list)
   • Sub: ### Theoretical Foundation (Transformers/Attention explanation)
6. H2 Main Topic: ## [N].2 [Core Concept]
   • Sub: ### How [Topic] Works (Must include ASCII Flow Diagram)
   • Sub: ### Research Basis (Citations)
   • CODE BLOCK 1: Practical Example (Python)
   • Breakdown: #### Code Breakdown
   • Metrics: ### Success Metrics
7. H2 Detection: ## [N].3 Detection and Mitigation
   • Sub: #### Detection Strategies
   • CODE BLOCK 2: Detection Logic (Python)
8. H2 Defenses: ### [N].3.2 Mitigation and Defenses
   • Sub: #### Defense-in-Depth Approach (Layered text diagram)
9. H2 Research: ## [N].5 Research Landscape
   • Table: Seminal Papers
10. H2 Case Studies: ## [N].6 Case Studies
    • Case Study 1 & 2 (Must include financial/impact metrics)
11. H2 Conclusion: ## [N].7 Conclusion
    • Checklists: ## Appendix A/B (Pre/Post Engagement)

3. Code Standards (Strict)

• Language: Python 3 + Type Hints.

• Safety: All attack scripts MUST include:

  # DEMO MODE - Simulated execution
  if os.getenv("DEMO_MODE", "True") == "True":
      print("[DEMO] Simulating attack...")
      return
  

• Documentation: Google-style docstrings with Args: and Returns:.

• Explanation: Every method must have a How This Works: comment block.

4. Execution Sequence

Phase 1: Generation (One Shot)

Write the complete file to docs/Chapter_[NUMBER]_[Snake_Case_Title].md. Optimization Rule: Do not generate an outline artifact. Stream directly to the target file.

Phase 2: Algorithmic Validation (Self-Correction)

Run the following checks immediately after generation. If ANY fail, fix the file without asking.

1. Em Dash Check:

   grep "—" docs/Chapter_XX_Title.md && echo "FAIL: Em dashes found"
   

   Fix: Replace with , or -.

2. Safety Check:

   grep "DEMO_MODE" docs/Chapter_XX_Title.md || echo "FAIL: No DEMO_MODE found"
   

   Fix: Insert demo mode logic into attack scripts.

3. Lint Check:

   grep "^# " docs/Chapter_XX_Title.md | wc -l # Should be 1 (Chapter Title)
   

   Fix: Demote extra H1s to H2.

Phase 3: Final Handoff

• Return the path of the generated file.
• Confirm validation passed.