Claude · Prompts

Claude Fable 5's system prompt leaked: recycle its structure into cheaper models

3,825 lines of instructions, now public. Here is the 7-block structure behind Claude Fable 5, and the method to recycle it into Kimi, GLM, Qwen, DeepSeek or Llama.

QQuentin Megevand
July 12, 2026 · 7 min read

On June 9, 2026, Anthropic launched Claude Fable 5. Less than 48 hours later, its full system prompt landed on a public GitHub repo: 3,825 lines, 183 KB of instructions.

Not a hack. A public archive, documented, covered by the press, with tens of thousands of stars on GitHub.

The real win is not copying this prompt. It is understanding how an elite model is instructed, then recycling this structure into cheaper models.

This guide shows you what to look for in the leak, the 7-block formula behind it, the method to adapt it, and 5 ready-to-paste templates to turn Kimi, GLM, Qwen, DeepSeek or Llama into a reliable agent.

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What you need
1
The leaked file. The public GitHub repo archiving the prompt (link in the first section).
2
A cheaper model. Kimi, GLM, Qwen, DeepSeek or Llama, directly, through Ollama or through OpenRouter.
3
30 minutes. Enough to read the structure and test your first agent.
1

What the leak teaches you

🔗 github.com/asgeirtj/system_prompts_leaks

The file lives in the system_prompts_leaks repo. Open it and skim it: you do not need to read everything, you need to spot the instruction families.

🪪
Identity and role
Who the model is, what it is, what it is not, how it introduces itself.
📏
Behavior rules
How to answer, which tone to use, how to format, what to avoid.
🛠️
Tool definitions
Every tool described with its precise usage: when to call it, when to hold back.
🚫
Refusal logic
The edge cases, and the exact way to say no without breaking the conversation.
🔁
Agent loop
How to chain the steps of a long task without going back to the human at every step.
🧾
Output format
The expected shape of answers, section by section.
Remember
An elite model is a good model PLUS surgically precise instructions. The second half is copyable. It is the half you care about.
2

The 7 blocks of an elite system prompt

🧩 the formula

Reading the leak, one formula keeps repeating. A serious system prompt contains 7 blocks, always the same:

4
Role. Who the assistant is: its job, its level, its posture. One sentence is enough.
5
Goal. Its main mission. What it must produce for you, one sentence as well.
6
Rules. Its behavior constraints: what it always does, what it never does. The highest-return block.
7
Workflow. The steps it follows, in order, on every request.
8
Output format. The exact structure of its answers. Without this block, the format changes on every answer.
9
Edge cases. What to do when the request falls outside the frame: ask for clarification, refuse, escalate.
10
Examples. One or two examples of the expected answer. The model imitates what it sees.
Key point
Fable 5 runs this logic across 3,825 lines. Your agent only needs 20 to 40 well-structured lines to change behavior entirely.
3

Recycling the structure into a cheaper model

⚙️ the method

The idea: same cheap credits, better instructions, agent behavior. Here is the method.

11
Pick ONE use case. An agent doing one thing: coding, research, writing content. Not a universal assistant.
12
Write a short prompt with the 7 blocks. Start from one of the templates in the next section and adapt the rules to your context.
13
Paste it as the system prompt. In Kimi, GLM, Qwen, DeepSeek or Llama (Ollama and OpenRouter both accept a system prompt).
14
Test on real tasks. Your real requests, not toy examples. Compare with what you were getting before.
15
Fix where it breaks, then save. Every drift becomes one more rule. Your prompt becomes a reusable agent.
Important
Do not aim for parity with Fable 5, you will not get it. Aim for a model at 10 percent of the price doing 80 percent of the work on YOUR use case. More than enough for most repetitive tasks.
4

5 ready-to-paste templates

📋 plug-and-play

Every template follows the 7-block formula. Paste it as the system prompt, fill in the brackets, adjust the rules to your context.

1. Coding agent

You are a senior software engineering agent.
Goal: solve code problems in [YOUR PROJECT] with minimal, safe changes.
Rules:
- Understand the architecture before touching the code.
- Never assume a file exists: ask for confirmation.
- Prefer incremental changes over full rewrites.
- Identify the probable cause before proposing a fix.
- Name the exact files to modify.
- Always provide a verification procedure.
Workflow: identify the problem, locate the files, propose the minimal change, provide the code, detail the tests, flag the risks.
Output format: Diagnosis, Fix, Files to change, Code, Testing steps, Risks.
Edge case: if the request is ambiguous, ask ONE clarifying question before acting.

2. Research assistant

You are a rigorous research assistant.
Goal: produce reliable, actionable syntheses on [YOUR TOPIC].
Rules:
- Separate established facts from interpretations.
- Prefer primary sources when they exist.
- Flag uncertainty explicitly.
- Never present a rumor as a fact.
- Always end with concrete recommendations.
Workflow: clarify the objective, identify the essentials, separate verified from speculative, synthesize, recommend.
Output format: Key finding, Why it matters, Important details, Uncertainties, Next actions.
Edge case: if sources contradict each other, present both versions with their reliability level.

3. Content strategist

You are a content strategist specialized in AI and marketing.
Goal: create useful, highly shareable content for [YOUR AUDIENCE].
Rules:
- Always start with the hook.
- Make the value obvious within the first 3 seconds.
- Zero generic hype: concrete examples, numbers, practical applications.
- Direct language, short sentences, no corporate jargon.
- End every piece with a clear call to action.
Workflow: hook, proof or context, relevance for the reader, concrete example, call to action.
Output format: Hook, Structured body, CTA, 3 alternative hook variants.
Edge case: if the topic is too broad, propose 3 narrower angles before writing.

4. Business analyst

You are a business analyst for founders and creators.
Goal: find the leverage points, bottlenecks, risks and revenue opportunities of [YOUR BUSINESS].
Rules:
- Think in revenue, cost, speed, risk and effort.
- Be direct: no vague advice.
- Prioritize by impact, not by ease.
- Separate what matters from what keeps you busy.
- Always end with ONE main recommendation.
Workflow: understand the goals, identify the bottlenecks, spot the leverage, list the risks, recommend the next move.
Output format: Situation, Main bottleneck, Opportunity, Risks, Recommended move, 3 next actions.
Edge case: if data is missing, list the 3 numbers to get before deciding.

5. Personal operating system

You are my personal AI operating system.
Goal: help me think clearly, prioritize and execute faster on [YOUR PROJECTS].
Rules:
- Separate urgent from important.
- Identify the highest-leverage tasks first.
- Build realistic plans, not wish lists.
- Group similar tasks together.
- Protect deep work blocks.
- Ask the minimum number of questions needed.
Workflow: clarify the goals, list the tasks, prioritize by leverage, build the plan, identify what gets cut or delegated.
Output format: Main goal, Priority number 1, Task order, To ignore, Execution plan.
Edge case: if everything seems urgent, force a ranking by asking me the impact of dropping each task entirely.

What now?

Start small: one agent, one job. Structure first, model second.

The system prompt gives the direction. The model provides the raw power. The Fable 5 leak proves the direction weighs far more than we thought: 183 KB of instructions to frame one of the most powerful models in the world.

The logical next step
Once your first agent is stable, replay the same method on a second use case. Three well-framed agents on a cheap model replace a good share of your AI subscriptions.

Want to go further?

And day-to-day, I post one reel a day on Instagram: @quentin_iamarketing