New Agent Skill Forces LLMs to Use ASD-STE100 Simplified Technical English for Clearer Documentation

Key Takeaways

• Standardized Precision: The tool enforces ASD-STE100, a controlled language standard used in aerospace since 1983 to ensure technical instructions cannot be misread.
• Elimination of AI Slop: By removing vague adjectives and marketing jargon, the skill forces LLMs to produce concise, fact-based documentation.
• Broad Compatibility: It works with any platform supporting the Agent Skills standard, including Claude Code, Cursor, VS Code Copilot, and Gemini CLI.
• Proven Efficacy: Testing across 96 runs involving six Claude models demonstrated a 72.9% reduction in Simplified Technical English violations.
• Open Source Accessibility: The project is available on GitHub under the MIT license with no external dependencies.

In-Depth Analysis

From Marketing Jargon to Technical Clarity

The primary objective of the "SimpleEnglish" agent skill is to solve the problem of "AI slop"—the tendency of Large Language Models (LLMs) to generate wordy, vague, and overly polite text that mimics social media posts rather than technical manuals. The original news highlights a stark contrast between standard AI output and output filtered through the STE skill.

In one provided example regarding database synchronization, a standard LLM might write about "leveraging robust architecture" and "minimal configuration overhead." When the STE skill is applied, this is stripped down to the functional reality: "sqlpipe copies your Postgres tables to S3. It needs one configuration file." This shift is not merely stylistic; it is functional. By removing abstract concepts like "robust architecture" and replacing them with concrete actions like "copies," the documentation becomes more accessible to users who need to perform specific tasks without navigating through linguistic filler.

Enforcing the Aerospace Standard for Safety

The choice of ASD-STE100 as the target standard is significant. Since 1983, the aerospace industry has relied on this controlled language to ensure that a "tired mechanic cannot misread an instruction." In high-stakes environments, ambiguity can lead to catastrophic errors. The "SimpleEnglish" skill applies this same philosophy to software documentation and error reporting.

For instance, the comparison of error messages shows a standard AI response saying, "Something went wrong while attempting to establish a connection... reach out to your administrator." In contrast, the STE-compliant version provides specific diagnostic information: "Connection to the database failed: the password for user app was not correct. Set DB_PASSWORD to the correct value." This transition from vague apologies to actionable data-driven instructions mirrors the requirements of aerospace maintenance manuals, where clarity is a safety requirement.

Technical Implementation and Performance Metrics

Technically, the tool is designed for ease of integration. It exists as a single folder with no dependencies and is released under the MIT license, making it highly portable for developers. It leverages the "Agent Skills" standard, allowing it to function across approximately 25 different AI harnesses, including major tools like OpenAI Codex and Google's Gemini CLI.

The effectiveness of the skill is backed by measured data. A study of 96 runs—covering six different Claude models across eight distinct tasks—revealed that every model showed improvement when the skill was loaded. The core metric, "STE violations per 100 words," dropped by 72.9%. This suggests that while LLMs are naturally prone to verbose patterns, they are highly capable of adhering to strict linguistic constraints when provided with the proper algorithmic guidance or "skill" framework.

Industry Impact

The introduction of the SimpleEnglish skill represents a shift toward professional-grade AI utility. For the AI industry, this move signifies a transition away from general-purpose conversational agents toward specialized tools that can adhere to rigorous industry standards. By forcing LLMs to write like "Boeing manuals," the tool addresses a major pain point in software engineering and technical writing: the lack of precision in automated content. As AI agents become more integrated into development workflows through tools like Cursor and VS Code Copilot, the ability to enforce standardized, controlled language will be essential for maintaining high-quality, readable, and safe technical documentation.

Frequently Asked Questions

Question: What is ASD-STE100 and why is it used here?

ASD-STE100 is a controlled language standard originally developed for the aerospace industry in 1983. It is designed to make technical documentation as clear and unambiguous as possible so that instructions are not misunderstood, especially in high-pressure or safety-critical situations. This skill uses it to eliminate the vague and wordy "slop" often found in AI-generated text.

Question: Which AI tools are compatible with this agent skill?

The skill is compatible with any environment that supports the Agent Skills standard. This includes popular tools such as Claude Code, Cursor, VS Code Copilot, OpenAI Codex, Gemini CLI, Goose, and OpenCode, along with approximately 25 other platforms.

Question: How much does this skill improve AI writing quality?

According to the project's testing data, which involved 96 runs across six different Claude models, the skill reduced Simplified Technical English (STE) violations by 72.9%. This results in documentation that is significantly more direct, factual, and easier to follow than standard AI output.