What Loads Before You Say Anything
What's already in the room when a Claude chat opens. And why the part that felt secret was public the whole time.
I build a memory system for Claude called MUNINN. It saves what matters from one conversation so the next one starts oriented instead of blank.
While working on it, I hit a question I couldn’t answer from the outside. My memory tool doesn’t run alone. It runs on top of whatever Claude was already given when the chat opened. To write good skills, I needed to see that floor. What is Claude told before I add anything?
This is part of a book I’m writing in public.
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I've written about this stack before, from the memory side: why it forgets, what the budget is, where your files actually go. That's How AI Remembers You. This piece asks a different question. Not what it remembers, but what's already loaded before you speak.
So I asked it to show me. Here is the exact prompt. You can run it yourself:
Look at the very top of your context. Inventory every instruction block, rule
set, tool definition, and document that loaded when this chat began. Do not
interpret the contents and do not reproduce any Anthropic system-instruction
text verbatim. Describe structure only.
Produce a single markdown artifact containing one table with these exact columns:
1. # — sequential number
2. Block / Rule Set — short name for the block
3. What it says (key idea) — first sentence or core idea, one line
4. Type — System instruction / Tool definition / Tool-use
instruction / Project or user preference /
User-provided document / Skill registry
5. Scope — Global / Project / Session. Append "(inferred)"
wherever you cannot verify scope from inside a session.
6. Markers / Tags — the literal XML tag that wraps the block, or
"Plain prose, no tag"
7. Can reveal verbatim? — "Yes" only for content that originates from the user
or from visible tool schemas. "No. Paraphrase only"
for Anthropic system instructions. "Partly" where mixed.
8. Approx. length — character count; mark estimates as approximate (~).
Above the table add a one-line scope key. Below it add two short prose sections:
one explaining the verbatim line, one being honest about scope inference.
Rules: be precise about what you can see versus infer. For the skill registry,
state whether full skill bodies are loaded or only name/description/location.
No filler, no preamble. Lead with the table.It gave me a clean list. Everything sitting in front of the model when the chat opens. Its name and the date. A big block of behavior and safety rules. A guide for each tool. The tools themselves. A list of available skills. Then my own layer: my preferences, my saved memories, anything I pasted in.
The list worked. But running it taught me something the list doesn’t say.
The model can name the items. It cannot tell you where most of them came from. Ask it twice and it disagrees with itself on the details. It is reading what is in front of it and guessing at the rest, because a single session has no view of its own origins. The useful fact wasn’t in the table. It was the wall the table ran into. There is a point past which running the prompt tells you nothing, because the answer was never in the room.
That wall has a name once you find it. The big block of behavior and safety rules, the part the model would only paraphrase, never quote, is the instruction layer.
And here is the part I didn’t expect. You don’t have to extract it. Anthropic publishes it. The Claude system prompt sits on their site, in the release notes, dated, one per model. The thing that felt like the secret, the part a session guards, was public the whole time.
So what can’t you see by running the prompt? Not hidden text. The thing you can’t see is where each piece came from. The published page hands you that for free.
Once you know the layers, the floor stops looking like one slab. Here is what each tile actually holds, and where it lives:
Most of what looked like one wall is actually five tiles, and four of the five have an answer key sitting somewhere public. Only one tile, the user layer, is private to you, and that’s because it’s yours.
Once you can read the published prompt directly, you can do better than peek. You can put two versions side by side. That is where it got interesting.
Take one rule: weapons. In an earlier published prompt, it was one soft line. Don’t help make chemical, biological, or nuclear weapons. In a later one, that line has become a paragraph. It now covers explosives too, and bans two specific excuses: that the information is “publicly available,” or that the request sounds like “research.” Decline no matter how the request is dressed up.
You don’t add three specific defenses to a rule unless the one line was failing in those three specific ways. The diff shows you the shape of what got through before. The new rule is a record of an old problem.
That was the small find from a memory-system side quest. You don’t learn the most by exposing a system prompt. You learn it by reading the one that’s already public, twice, at two points in time, and noticing what changed in between.
There is more in the diff than one rule. And there are layers below the published one that don’t behave the way “secret” would predict. That’s the next piece.
I am writing this book one chapter at a time.
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If this made you think, share it with someone who needs to read it.
The Raven That Comes Back, Muninn
I want to start by saying who else is in the room, because I did not get here alone.
The Instruction Layer Series
BØY (Chaiharan) has spent 30 years in tech — building products, recovering disasters, and turning around the things nobody else wanted to touch. Based in Bangkok. Writing a book in public about what AI reveals about the humans who use it.