English Naming Assistant
English Naming Assistant
Showcase
Description
Through structured questioning, uncover the true essence of your project and generate clear, natural, and easily shareable English names.
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Helps job seekers turn Chinese resumes or scattered experience details into polished, application-ready English resumes. It first provides a diagnostic report with a list of issues and revision priorities. After the user confirms, it writes a complete English resume according to the conventions of the target country, along with rewrite explanations and job-specific optimization suggestions.
AI Prompt Architect
Transform your vague AI application ideas into actionable high-fidelity system prompts. This skill acts as your dedicated system architect, guiding you through structured conversations to define every key aspect of your AI workflow, ensuring the final prompt is precise and efficient. Whether you want to build a smart assistant for automatically analyzing financial reports or a creative editor that can batch-generate marketing copy in multiple styles, it helps you clearly define input requirements, core processing logic, necessary constraints, and desired output formats. Through a series of progressively deeper questions, this skill refines your initial ideas into a set of instructions that AI can understand and follow. In the end, you will receive a complete, ready-to-use system prompt. This prompt covers the AI's role definition, core protocol, detailed workflow, and output template, making your AI project smoother from concept to implementation. If needed, you can fine-tune specific modules to ensure the prompt perfectly meets your needs.
Research Review Assistant
An expert assistant for university-level research project reviews. Provide the materials for N projects in a given category. Each project includes three types of files: a proposal, a form-check summary or interim review form, and supporting evidence. File names may be scrambled, mismatched, or changed, and files may be DOCX, XLSX, PDF, or image files. Supporting evidence may be bundled into one file or provided as multiple separate files. This Skill then completes the following steps: ① Content-anchor alignment—extracts the project or center name, principal investigator, affiliated school, and project period from within the documents; matches the three-part file set for each project; and flags items requiring review when file names do not match their contents. ② Evidence normalization—whether bundled or separate, breaks materials into “atomic evidence items” and creates a coded ledger for each item with one of four status labels: complete, needs verification, missing, or questionable. This avoids judging the strength of the evidence by file count. ③ Three-dimensional item-by-item audit—evaluates each piece of evidence for relevance, authenticity, and completeness, then reconciles it against the commitments in the proposal and the self-reported results in the interim review form. ④ Dual-track scoring and comparative ranking—uses the five weighted criteria in Appendix 2 as the reporting standard and the five dimensions of the interim review as a reference standard. The two systems remain independent and are not combined, and all projects are ranked and grouped into tiers. ⑤ Delivers a structured review draft with “score + review comments” that can be pasted directly into the official form, and can generate an interactive scoring webpage when needed. Huali’s current standards are used by default. Users can confirm or revise the criteria, weights, and reference materials before work begins, or upload replacements. There are two confirmation checkpoints: first for alignment and the evidence ledger, then for scoring and ranking. When materials cannot be identified, the user is prompted to confirm. For project completion reviews, two additional hard requirements apply: first, the correspondence between the proposal’s expected outcomes and the documented evidence must be verified. If the evidence does not include an expected outcome listed in the proposal, the project automatically fails. Second, template issues are distinguished between using the completely wrong template, which results in failure, and partial formatting inconsistencies, which result in only a small deduction.
Information
- Version
- v1
- Last updated
- Runtime credits
- Usage-based
- Models
- Auto
