Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add rules/zenbase-ai/llml/specgit clone --depth 1 https://github.com/zenbase-ai/llmlWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.03540 | $0.03540 |
| Opus 5 | $0.01770 | $0.01770 |
| Sonnet 5 | $0.00708 | $0.00708 |
| Haiku 4.5 | $0.00354 | $0.00354 |
Grade A, and why
spec 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 today.
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.
How it starts
The opening of the file, as written. The whole thing — 559 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLML Technical Specification
Version: 0.3.0, July 1st 2025
Overview
LLML (Lightweight Language Markup Language) is a data serialization format that transforms nested data structures into human-readable, XML-like markup. This specification defines the exact transformation rules that must be implemented consistently across all language implementations.
Core Transformation Rules
1. Empty Value Handling
Empty values are transformed to empty strings:
llml() → ""
llml([]) → ""
llml({}) → ""
Special case for empty named arrays:
llml({items: []}) → "" # Empty arrays within objects are omitted entirely
llml([[], [[]]]) → "" # Empty arrays within arrays are omitted entirely
2. Primitive Value Formatting
All primitive values are wrapped in XML-like tags using the key name:
Strings:
llml({message: "Hello"}) → "<message>Hello</message>"
llml({empty: ""}) → "<empty></empty>"
Numbers:
llml({count: 42}) → "<count>42</count>"
llml({temperature: 98.6}) → "<temperature>98.6</temperature>"
llml({zero: 0}) → "<zero>0</zero>"
Booleans:
llml({enabled: true}) → "<enabled>true</enabled>" # TypeScript
llml({enabled: True}) → "<enabled>True</enabled>" # Python
llml({disabled: false}) → "<disabled>false</disabled>" # TypeScript
llml({disabled: False}) → "<disabled>False</disabled>" # Python
Null/None/Undefined:
llml({value: null}) → "<value>null</value>" # TypeScript
llml({value: None}) → "<value>None</value>" # Python
llml({value: undefined}) → "<value>undefined</value>" # TypeScript
3. Key Preservation
Keys are preserved as-is without transformation:
llml({user_name: "Alice"}) → "<user_name>Alice</user_name>"
llml({userName: "Bob"}) → "<userName>Bob</userName>"
llml({"key with spaces": "value"}) → "<key with spaces>value</key with spaces>"
4. Multiple Key-Value Pairs
Multiple key-value pairs are separated by newlines:
llml({name: "Alice", age: 30, active: true})
→
<name>Alice</name>
<age>30</age>
<active>true</active>
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.
- today First seen · 559 lines · 3,540 tokens per session scan A ce8e3e642356
spec is a cursor rule published in the GitHub repository zenbase-ai/llml (72 stars, last pushed 1y ago), licensed MIT. It adds 3,540 tokens to every session, about $0.0177 per session on Opus 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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