danyuchn/asd-ste100-skill

ASD-STE100 Simplified Technical English rules, repurposed as a Claude Code skill for rewriting ambiguous agent-facing English.

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danyuchn/asd-ste100-skill has 5k+ stars and 300+ forks on GitHub, is written in Python, and was last pushed on Oct 14, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 60 minutes ago
Added to GitGenius on October 3rd, 2026
Created on July 20th, 2026
Open Issues & Pull Requests: 3 (+0)
GitHub issues: Enabled
Number of forks: 305
Total Stargazers: 5,155 (+2)
Total Subscribers: 13 (+0)

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Median issue/PR response: 3.2 days
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90th percentile: 9.5 days
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Detailed Description

ASD-STE100 Skill is a Claude Code skill that rewrites English text into Simplified Technical English, a controlled-language standard developed by the aerospace and defense industry to eliminate ambiguity in technical communication.

The skill addresses a specific problem: when one AI agent reads output from another agent, tool description, or error message, there is no human present to resolve ambiguous phrasing. The aerospace industry created ASD-STE100 to prevent misreadings of aircraft maintenance instructions by enforcing one meaning per word, active voice, simple tenses, one instruction per sentence, and short sentences with no dropped words. The skill applies these same rules to inter-agent communication, where a downstream agent cannot ask for clarification if it misinterprets a message. The tool operates in two modes: Strict mode for procedures, error messages, and tool descriptions, and STE-flavored mode for READMEs and explanatory prose. It flags rule violations sentence by sentence, including ambiguous word choice, complex tenses, passive voice with unclear actors, multi-instruction sentences, oversized noun clusters, and other clarity problems, then rewrites each flagged sentence while preserving all facts, conditions, and scope qualifiers from the original.

Adoption makes sense for teams building multi-agent systems where agents must reliably parse each other's output, or for projects where tool descriptions and error messages will be read by language models rather than humans. The skill is particularly suited to systems where miscommunication could cause cascading failures across agent chains. It differs from general style guides by treating ambiguity as a structural problem rather than a preference, and by refusing to simplify if doing so would lose required precision.

Development activity shows consistent engagement with the problem space. The project includes detailed before-and-after examples demonstrating the transformation applied to real-world sentences. The skill outputs rewritten text directly without preamble or change summaries, designed for integration into agent workflows. Documentation covers both the official STE rules themselves and illustrations of how they apply to agent-facing communication.