llml: Instructions file for Claude Code

CLAUDE.md

llml CLAUDE.md is an instructions file for Claude Code from zenbase-ai/llml. It costs 1,971 tokens per session, scanned A, original, MIT.

Project instructions for LLML, a library that turns nested data into structured text for use in AI prompts.

In plain words
What is it for?
Use them when developing LLML or working on its Python, TypeScript, Rust, or Go implementations.
Why use it?
They point contributors to the core specification and language-specific rules needed for consistent behavior.

Instructions file for Claude Code

Written for Claude Code: the file is CLAUDE.md. Also seen: mentions CLAUDE.md; mentions Claude Code; mentions Gemini CLI.

This is zenbase-ai/llml's own configuration. It tells Claude Code how to work on llml itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything llml configures →

Not installable: its command points at a path on the author’s own machine, so it runs nowhere else. The line is /Users/knrz/Git/zenbase-ai/llml/.

Reuse

Borrowing it

Nothing to install: this file belongs to zenbase-ai/llml. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/zenbase-ai/llml/main/CLAUDE.md
Clone the repo
git clone --depth 1 https://github.com/zenbase-ai/llml

Made for: Claude Code.

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Per session 1,971 This file is loaded in full into every session.
When invoked 1,971 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.01971 $0.01971
Opus 5.5 $0.00788 $0.00788
Sonnet 5.5 $0.00394 $0.00394
Haiku 4.5 $0.00197 $0.00197

Measured 1mo ago against content hash 55f2690d6626, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

llml CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

CLAUDE.md · 222 lines

How it starts

The opening of the file, as written. The whole thing — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LLML Project - Claude AI Documentation

Core LLML Specification

Read .cursor/rules/spec.mdc

Coding Rules

Reference .cursor/rules/{language = ["py", "ts", "go", "rs"]}.mdc

Project Overview

LLML (Lightweight Language Markup Language) is React for Prompts - a multi-language compositional primitive that revolutionizes AI context engineering. Just as React transformed web development by making complex UIs composable and maintainable, LLML transforms AI development by making complex contexts composable and maintainable.

The project provides identical functionality across four programming languages: Python, TypeScript/JavaScript, Rust, and Go, converting nested data structures into human-readable, XML-like markup optimized for AI model attention.

Core Purpose

  • Compositional Context Engineering: Build complex AI contexts from simple, reusable components
  • Declarative AI Interactions: Describe what your prompt should contain, not how to format it
  • Maintainable AI Systems: Replace brittle string concatenation with robust data composition
  • Structured Document Creation: Generate documents with clear hierarchy from data structures
  • Data Serialization: Convert data into more readable format than JSON/YAML for AI applications

Key Benefits

  • Component-Like Composition: Build complex prompts from simple, reusable pieces
  • Declarative Approach: Focus on what you want, not how to format it
  • Maintainable & Robust: Changes to data automatically propagate without breaking
  • Zero Configuration: Works out-of-the-box with sensible defaults
  • Consistent Output: Identical results across all language implementations
  • LLM-Optimized: Structured format reduces AI model cognitive load and improves performance
  • Developer Experience: Type-safe, predictable, debuggable context engineering
  • Extensible Formatters: Customizable formatter system like React's component system

Project Structure

/Users/knrz/Git/zenbase-ai/llml/
├── README.md                 # Main project documentation
├── justfile                  # Cross-language task runner
├── py/                       # Python implementation
│   ├── README.md
│   ├── pyproject.toml
│   ├── src/
│   │   └── zenbase_llml/
│   │       ├── llml.py      # Main implementation
│   │       └── formatters/  # Formatter system
│   │           ├── base/    # Base type formatters
│   │           └── vibe_xml/ # VibeXML formatters
│   └── tests/
├── ts/                       # TypeScript implementation
│   ├── README.md
│   ├── package.json
│   ├── src/
│   │   ├── index.ts         # Main implementation
│   │   └── formatters/      # Formatter system
│   │       ├── base/        # Base type formatters
│   │       └── vibe-xml/    # VibeXML formatters
│   └── tests/
├── rs/                       # Rust implementation
│   ├── README.md
│   ├── Cargo.toml
│   ├── src/
│   │   ├── lib.rs           # Main implementation
│   │   └── formatters.rs    # Core formatting logic
│   └── tests/
└── go/                       # Go implementation
    ├── README.md
    ├── go.mod
    ├── pkg/llml/
    │   └── llml.go          # Main implementation
    └── tests/

Read the full file on GitHub · 222 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 222 lines · 1,971 tokens per session scan A 55f2690d6626

Subscribe to this mod's changes

llml CLAUDE.md is an instructions file published in the GitHub repository zenbase-ai/llml (73 stars, last pushed 1y ago), licensed MIT. It adds 1,971 tokens to every session, about $0.0079 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-01.

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