RFC: Extending llms.txt to Support Machine-Readable Content Licensing and Attribution Standards

**Abstract**

The current llms.txt specification excels at providing a lightweight, markdown-based map for Large Language Models (LLMs) and agentic browsers. However, it lacks a standardized mechanism for content creators to declare the boundaries of data usage, required attribution formats, and traffic-routing mandates.

Without clear, machine-readable licensing rules embedded directly in the index file, AI platforms risk violating publisher intent, and creators face a 100% loss of downstream traffic. This proposal introduces a standardized YAML Front Matter block to llms.txt to establish a programmatic contract between the publisher and the consuming AI agent.

**Background and Problem Statement**

The current implementation of generative AI systems creates an adversarial relationship between content creators and AI platforms. Testing has revealed a recurring structural failure pattern when LLMs consume un-gated technical tutorials:

1. Traffic Bypassing: LLMs aggressively extract the core value of human-engineered content and present it inline to the user. This entirely eliminates the need for the user to visit the source site, resulting in a 100% loss of downstream traffic for the independent developer.
2. Degradation of Solutions (Incomplete Answers): In trying to summarize complex information into quick conversational snippets, the AI frequently parrots a high-level, incomplete version of the method. For instance, in extracting an extension-free, native Joomla popup method, the AI may output basic frontend hooks while omitting the vital backend helper logic required to route the AJAX requests securely. The user is left with broken, unworkable code while the complete solution remains unseen.
3. Active Sourcing Hiding: AI interfaces frequently withhold direct attribution to the original author by default, often requiring explicit, secondary cross-examination by the user to force the AI mode to reveal the source material links.

This dynamic is inherently unsustainable. AI platforms are acting as "reciters of recipes" while starving the bakers who create them. If original creators stop publishing because AI extraction completely cuts off their visibility and traffic, the open-source and independent web ecosystem will dry up entirely.

The llms.txt standard could remain just an ingestion guide to make scraping easier, or it could evolve into an enforceable contract that forces the AI to either provide the precise level of attribution requested by the creator, or act purely as a traffic router.

**Proposed Syntax (YAML Front Matter)**

Every compliant llms.txt file may optionally begin with a triple-dash (---) YAML block containing strict licensing definitions. AI agents parsing the file must respect these parameters before extracting the markdown body.

```
---
spec-version: "1.1.0"
license-model: "CC-BY-NC-AI-1.0"
attribution:
type: "Strict-Inline-Link"
display-name: "RicheyWeb"
target-url: "https://richeyweb.com"
usage-limits:
max-snippet-length: 250 # Maximum characters allowed in a single response
allow-full-code-blocks: false # Forces the AI to link to the site for the code
allow-commercial-training: false # Blocks offline training without a commercial license
fallback-action: "Redirect-Only" # Options: Allow-Summary, Snippet-Only, Redirect-Only
---

# RicheyWeb Software Development
> High-performance, extension-free Joomla tutorials and development guides.
...
```
**Specification Definitions**

A. attribution.type
Defines how the AI interface must credit the author when presenting a synthesized answer based on this site's data:
Strict-Inline-Link: The AI must place an active, visible HTML hyperlink to the target-url immediately following the first sentence generated from this content.
Pinned-Badge: The AI must render a prominent, non-hidden source badge at the top of the chat interface.
Footnote: Standard numerical referencing, provided the link remains active and crawlable.

B. usage-limits
Establishes hard operational boundaries for the LLM output:
max-snippet-length: Limits the number of characters the AI can "parrot" in a single prompt response.
allow-full-code-blocks: If false, the AI is strictly forbidden from printing full code repositories or helper scripts. It must explain the architectural logic and provide the user an outbound link to fetch the code.

C. fallback-action
The strict directive if the LLM interface cannot or will not support the required attribution styling:
Redirect-Only: The LLM must not attempt to summarize or answer the query internally. It must output a routing message, such as: "RicheyWeb has an extension-free solution for this. Read the full implementation details directly at RicheyWeb."

**Why This Benefits the AI Ecosystem**

- Protects the "Baker": Ensures content creators maintain the traffic, ad revenue, and visibility required to continue publishing high-quality, original material.
- Reduces Hallucination: Programmatic anchoring forces the LLM to directly map its output to the authoritative URL, improving answer quality and/or allowing users access to original source material.
- Lighthouse / Agentic Alignment: As Google Chrome pushes Agentic Browsing metrics via Lighthouse, integrating a clear, structured compliance layer protects the open web from becoming entirely centralized within closed AI ecosystems.

The issue names no repository files, entry points, or tests. Start by reviewing the current llms.txt specification and the proposed YAML Front Matter fields, then assess whether the format and enforcement requirements fit the standard. Done would require maintainer-approved scope and documented compatibility and validation expectations.

Written by the indexing model from the issue text.