claude_opus_mythos_5k

High-quality synthetic supervised fine-tuning (SFT) datasets designed to distill the capabilities, reasoning style, and professional behavior of Anthropic's Claude Opus 4.8 and Claude Mythos Preview.

This dataset are intended for training open-weight models to approximate frontier-level performance in software engineering, agentic workflows, complex reasoning, and (in the Mythos set) defensive cybersecurity.

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Dataset Included

WithinUsAI/claude_opus_mythos_5k

• Focus: Maximum-effort structured reasoning, honest analysis, production software engineering, agentic system design, and technical strategy.
• Strengths: High-quality chain-of-thought, clear trade-off analysis, actionable recommendations, strong developer experience focus.
• Focus: Deep cybersecurity, memory safety, autonomous defensive analysis, secure systems design, and responsible technical depth.
• Strengths: Rigorous vulnerability analysis, defensive framing, concrete hardening recommendations, professional security mindset.

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Format

Both datasets use the standard chat format:

{
  "messages": [
    {
      "role": "system",
      "content": "You are Claude Opus 4.8 / Mythos Preview..."
    },
    {
      "role": "user",
      "content": "Real-world professional prompt..."
    },
    {
      "role": "assistant",
      "content": "<think>\nVisible high-quality reasoning...\n</think>\n\n**Structured professional response...**"
    }
  ],
  "metadata": {
    "model_focus": "claude_opus_4_8_max_thinking" | "claude_mythos_preview",
    "category": "...",
    "difficulty": "expert",
    "example_id": "...",
    "generated_at": "..."
  }
}

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Categories

Opus 4.8 MAX THINKING

• Production Software Engineering & Refactoring
• Agentic Workflow Design & Automation
• Complex Technical Strategy & Decision Making
• Code Quality, Testing & Observability
• Enterprise Systems & Scalability

Mythos Preview

• Advanced Cybersecurity & Vulnerability Discovery
• Memory Safety & Secure Systems Design
• Autonomous Technical Analysis & Exploitation (Defensive)
• Complex Systems Security & Hardening
• Secure Software Engineering at Scale

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Key Features

• True <think> tags — Every assistant response contains visible, high-quality chain-of-thought reasoning.
• Professional quality — Real-world scenarios drawn from production engineering, security, and strategy work.
• No placeholders or repetition — Diverse, non-duplicate examples with concrete artifacts.
• Defensive framing — All security-related content is framed responsibly for defenders and builders.
• Honest reasoning — Models explicitly surface uncertainties and trade-offs.

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Intended Use

This dataset are designed for:

• Supervised fine-tuning (SFT) of open-weight LLMs
• Distilling frontier model behavior into smaller or open models
• Research on capability distillation and reasoning transfer
• Creating specialized models strong in software engineering, agentic tasks, or defensive security

Not intended for:

• Replicating offensive cyber capabilities
• Training models for malicious use
• Bypassing safety alignments of the original models

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Quality Notes

These are synthetic datasets generated to emulate the output distribution and professional reasoning style of the target models. They are not real outputs from Claude Opus 4.8 or Mythos Preview.

Every effort has been made to ensure:

• High diversity
• Professional tone
• Visible high-quality reasoning
• No obvious repetition or low-quality filler

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License & Usage

These datasets are provided for research and educational purposes. Users are responsible for ensuring their use complies with applicable laws and the terms of service of any models they fine-tune.

Recommended citation:

 WithinUsAI/claude_opus_mythos_5k (2026)
Synthetic professional SFT data for distilling frontier model capabilities.

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Generated June 2026 — Focused on professional, high-quality distillation for serious model training.