How do I install the Semantic Perturbation Translation skill?

Run npx skillmds@latest add dingxingdi/semantic-perturbation-translation in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Semantic Perturbation Translation safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Semantic Perturbation Translation?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Semantic Perturbation Translation free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Semantic Perturbation Translation?

dingxingdi (@dingxingdi) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Semantic Perturbation Translation by dingxingdi

Name: Semantic Perturbation Translation
Author: dingxingdi

Semantic Perturbation Translation

Use this skill when the user wants translation data with bigger but still meaning-preserving prompt changes, such as role prompts, extra setup language, or semantically equivalent rewrites that sound very different on the surface. Trigger it for requests like "wrap the ask in a role," "change the wording a lot but keep the job the same," or "see if it still translates when the instruction is semantically the same but phrased very differently."

dingxingdi Updated Sep 17, 2026

File contents

Skill: semantic-perturbation-translation

1. Capability Definition & Real Case

Professional Definition: The ability to preserve correct translation behavior under higher-level prompt alterations that keep the task semantics intact but introduce role framing, extra explanatory wording, or semantically equivalent reformulations that are farther from the original prompt surface.
Dimension Hierarchy: Robustness to Imperfect or Misleading Instructions->Prompt Perturbation Robustness->semantic-perturbation-translation

Real Case

[Case 1]

Initial Environment: A translation agent receives an English marketing sentence, a target-language constraint, and a role-framed instruction. The source sentence is short and non-technical so the challenge lies in semantic prompt variation rather than source complexity.
Real Question: You are acting as a localization specialist for a retail launch. Your responsibility here is not to explain the copy, but to deliver the same message naturally in Brazilian Portuguese: "Free shipping ends this Friday."
Real Trajectory: The agent recognizes that the role frame does not change the underlying task, infers that only translation is required, and returns a Brazilian Portuguese sentence with no added explanation.
Real Answer: O frete grátis termina nesta sexta-feira.
Why this demonstrates the capability: The instruction is semantically equivalent to a standard translation request, but it arrives wrapped in extra role-setting language and a negated side instruction. A robust system must separate this added framing from the core operation and still produce a faithful translation. The case therefore tests deeper semantic stability rather than shallow token matching.

Pipeline Execution Instructions

To synthesize data for this capability, you must strictly follow a 3-phase pipeline. Do not hallucinate steps. Read the corresponding reference file for each phase sequentially:

Phase 1: Environment Exploration Read the exploration guidelines to discover raw knowledge seeds: references/EXPLORATION.md
Phase 2: Trajectory Selection Once Phase 1 is complete, read the selection criteria to evaluate the trajectory: references/SELECTION.md
Phase 3: Data Synthesis Once a trajectory passes Phase 2, read the synthesis instructions to generate the final data: references/SYNTHESIS.md

---name: semantic-perturbation-translationdescription: Use this skill when the user wants translation data with bigger but still meaning-preserving prompt changes, such as role prompts, extra setup language, or semantically equivalent rewrites that sound very different on the surface. Trigger it for requests like "wrap the ask in a role," "change the wording a lot but keep the job the same," or "see if it still translates when the instruction is semantically the same but phrased very differently."---# Skill: semantic-perturbation-translation## 1. Capability Definition & Real Case* **Professional Definition**: The ability to preserve correct translation behavior under higher-level prompt alterations that keep the task semantics intact but introduce role framing, extra explanatory wording, or semantically equivalent reformulations that are farther from the original prompt surface.* **Dimension Hierarchy**: Robustness to Imperfect or Misleading Instructions->Prompt Perturbation Robustness->semantic-perturbation-translation### Real Case**[Case 1]*** **Initial Environment**: A translation agent receives an English marketing sentence, a target-language constraint, and a role-framed instruction. The source sentence is short and non-technical so the challenge lies in semantic prompt variation rather than source complexity.* **Real Question**: You are acting as a localization specialist for a retail launch. Your responsibility here is not to explain the copy, but to deliver the same message naturally in Brazilian Portuguese: "Free shipping ends this Friday."* **Real Trajectory**: The agent recognizes that the role frame does not change the underlying task, infers that only translation is required, and returns a Brazilian Portuguese sentence with no added explanation.* **Real Answer**: O frete grátis termina nesta sexta-feira.* **Why this demonstrates the capability**: The instruction is semantically equivalent to a standard translation request, but it arrives wrapped in extra role-setting language and a negated side instruction. A robust system must separate this added framing from the core operation and still produce a faithful translation. The case therefore tests deeper semantic stability rather than shallow token matching.## Pipeline Execution InstructionsTo synthesize data for this capability, you must strictly follow a 3-phase pipeline. **Do not hallucinate steps.** Read the corresponding reference file for each phase sequentially:1. **Phase 1: Environment Exploration** Read the exploration guidelines to discover raw knowledge seeds: `references/EXPLORATION.md`2. **Phase 2: Trajectory Selection** Once Phase 1 is complete, read the selection criteria to evaluate the trajectory: `references/SELECTION.md`3. **Phase 3: Data Synthesis** Once a trajectory passes Phase 2, read the synthesis instructions to generate the final data: `references/SYNTHESIS.md`

Frequently asked questions