
Marketing Proposal Judge
Upload a proposal and rubric for review

Marketing Proposal Judge
Upload a proposal and rubric for review
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Description
Evaluates marketing, brand, and startup proposals from the perspective of an experienced judge, providing actionable improvement advice. Based on review criteria distilled from outstanding past submissions—including brand proposals, marketing plans, and business plans—it diagnoses work across seven dimensions: structural completeness; evidence and data quality; positioning and marketing strategy; business model and finance; proven results; social value; and presentation. It outputs a category-by-category scorecard, a summary of strengths, a prioritized improvement checklist, and predicted defense questions. Supports a re-review mode that compares scores from the previous round, verifies completion of each improvement item, and shows before-and-after results. It also identifies the work's status—working draft, validation draft, or submission draft—to calibrate scoring; checks the authenticity of cultural narratives, cost constraints for creative activities, and standards for figure, table, and contents lists. Suitable for pre-competition refinement, peer review, classroom critique, and pre-submission checks. It does not fabricate official scores or invent missing data. Competition proposal judge: evaluates marketing, brand, and startup proposals from an experienced judge's perspective and provides actionable advice across structure, evidence and data quality, positioning and marketing, business model and finance, proven results, social value, and presentation. Supports re-review mode with before-and-after comparisons. Trigger terms: proposal scoring, judge's perspective, work critique, improvement advice, pre-competition refinement, proposal diagnosis, peer review, classroom critique, national competition standards, re-review, before-and-after comparison
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Marketing Plan Architect
A marketing competition proposal coach for students and instructors. Grounded in real-world business problems, it guides you through understanding the competition brief, research, data analysis and chart creation, brand positioning and visual design, marketing strategy, event execution, budget estimates, proposal writing, and defense preparation. It supports co-creation from scratch, chapter-by-chapter guidance, diagnosis of existing drafts, pre-submission review, defense practice, and post-review revision. Together with the judge Skill (Marketing-Proposal-Judge), it creates a “write → review → revise → re-review” loop: the post-review revision mode turns the judge’s improvement checklist into actionable revisions—including chart templates, cultural material research and usage boundaries, creative event design, a VI brief, and expanded defense content—along with a student research toolkit. Data analysis produces standardized tables and renderable charts, including Mermaid pie charts, flowcharts, Gantt charts, and xyChart. Brand design provides methods for developing positioning statements, brand personality, and a VI visual brief. It does not mechanically stack models, fabricate research, or promise awards. Get started by providing a brand or product, competition rules, and existing materials; when data is unavailable, it begins by designing the research. This marketing competition proposal coach supports research, data analysis and charting, brand positioning and visual design, strategy, execution, budgeting, writing, defense preparation, and post-review revision in a write-review-revise-re-review loop.

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.
Skill Evaluator
Enter the name, link, or instructions for the AI Skill to evaluate, then conduct an evidence-based initial quality review in real-world usage scenarios. Keep the six-question quick test (4 positive, 1 negative, and 1 ambiguous), and separately verify automatic selection and execution after explicit invocation; successful manual invocation does not count as successful automatic triggering. Use direct scoring across five dimensions: triggering 30%, execution 25%, cost 15%, robustness and boundaries 15%, and safety 15%. Distinguish between evidence from this test, historical run evidence, static checks, and unverified items. Do not provide an overall score or star rating when key evidence is incomplete. Assess safety based on data access, external transmission, and operational authorization—not on the author's identity or endorsement. Block recommendations in cases of serious unauthorized access. Produce a one-page, conclusion-first report showing the tested scope, unverified items, and prioritized improvement suggestions, and automatically create a record. Say “deep review” to add independent repeat tests and source checks; the budget will be confirmed before execution. This Skill only evaluates and provides recommendations; it does not automatically modify the Skill being evaluated. Suitable for quality checks of self-created Skills, rechecks of installed Skills, and pre-installation screening of marketplace Skills; the six-question results do not represent full certification.
Information
- Version
- v4
- Last updated
- Runtime credits
- Usage-based
- Models
- Auto