Extract and classify hard and soft skills from job descriptions into a two-column table.
npx skills add https://github.com/tolgaio/neo --skill prompt-improvement
Please help me install this Agent Skill. Skill: Prompt Improvement Source: https://github.com/tolgaio/neo/tree/main/skills/fabric/improve/prompt Command: npx skills add https://github.com/tolgaio/neo --skill prompt-improvement
This service refines user prompts to be clearer, more structured, and more likely to elicit high-quality model responses, saving time and iterations.
Use the tool to transform a vague input like "Explain quantum computing" into a detailed, testable prompt with defined steps and references.
High-intent search queries and answers about installing and using this skill.
Prompt improvement refines your instructions by adding structure, examples, and clear delimiters to reduce ambiguity. This transforms vague inputs into detailed, testable prompts that consistently elicit higher-quality model outputs with fewer iterations.
Standardized prompt structure ensures repeatable results across different requests. Prompt improvement applies clarity techniques—defined steps, explicit examples, and formatted boundaries—so each prompt follows the same pattern and produces predictable outcomes.
Prompt improvement frames prompts for quick piloting and evaluation by making instructions explicit and measurable. This testing readiness lets you validate prompt performance early, identify gaps, and refine instructions before full deployment.
Delimiters and examples reduce ambiguity by signaling structure to LLMs. Use delimiters to mark input boundaries and section changes; add examples to show expected output format. Both techniques significantly improve clarity and reduce misinterpretation.
Yes. Prompt improvement focuses on universal clarity principles—structured instructions, concrete examples, and explicit delimiters—that work across different LLM platforms. These techniques adapt to any model's input requirements.
getcrew44Convert vague user requests into confirmed, one-sentence problem statements.
hamelsmuCalibrate LLM judges against human labels using TPR/TNR measurement and bias correction.
HermeticOrmusEvaluate and iteratively refine prompts using [0,1]-enriched category quality metrics.
Snowflake-LabsA concise summary of your roleplay character: name,</name> personality, and/or behavior, formatted as a single string sentence. Typically不超过50字，此处省略
