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."
ソース情報
- リポジトリ
- Dingxingdi/paper_fast_search_backup
- ソースの最終更新活動
- 2026年4月10日 01:27
- 検出された SKILL.md の言語
- 英語
- スター
- 0
- フォーク
- 0
インストール方法
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
ソースファイルを確認
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
ファイルエクスプローラー
4 ファイルSKILL.md を表示中
SKILL.md
ソースの指示 · 読み取り専用プレビュー- name
- semantic-perturbation-translation
- description
- 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 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:
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`
GitHubで見る