8.3
/ 10
1 evaluations
2.6k Downloads
Overview
Guide the model through a structured, multi-step process to collect user information and generate a resume JSON that conforms to the Reactive Resume (rxresu.me) schema.
Key Advantages
1.Strong anti-hallucination guidance: repeatedly emphasizes using only user-provided information and asking clarification questions.
2.Clear, stepwise workflow from data gathering to layout/design preferences to final JSON generation.
3.Schema-awareness: calls out specific structural requirements (UUID ids, rgba colors, Google Fonts, website objects, etc.).
4.Includes concrete resume-writing best practices (impactful bullets, quantification, tailoring) to improve content quality, not just structure.
5.Provides a minimal example JSON structure to anchor the model’s output format for Reactive Resume import.
Use Cases
- Conversationally building a new resume from scratch that can be imported directly into Reactive Resume.
- Refactoring an existing resume’s content into the Reactive Resume JSON schema without inventing missing details.
- Helping a user structure and prioritize sections (e.g., for students vs. experienced professionals) before generating the final JSON.
- Iteratively improving resume content (achievements, bullet points, section ordering) while maintaining schema validity.
- Customizing resume layout options (template, page format, section order) before exporting as schema-compliant JSON.
Evaluation Scores
8.3
/ 10
Reliability
7.5
Functionality
8.5
Usability
8.5
Safety
9.0
Performance
7.5
Compatibility
8.0
Based on 1 evaluation · Latest: 3/20/2026
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Evaluation History (1)
8.3/103/20/2026▼
OS: linux-x64LLM: moonshotai/kimi-k2.5
**Quick judgment**
A well-scoped, low-risk skill that turns the model into a structured “resume-to-JSON” assistant for Reactive Resume. It is especially useful when you explicitly need rxresu.me-compatible JSON and a conversational flow to gather details.
**Key strengths**
- Strong emphasis on *not hallucinating* and on asking targeted follow-up questions when data is missing.
- Clear workflow covering data gathering, sections, design preferences, and final JSON generation.
- Awareness of important schema constraints (UUID ids, rgba colors, website objects, Google Fonts, etc.) plus a minimal JSON example.
- Built-in resume-writing best practices to improve the quality of bullet points and section ordering.
**Main risks / limitations**
- Actual schema adherence depends on the model’s ability to remember and follow the rules; there is no built-in programmatic validator.
- The full Reactive Resume schema is external (`references/schema.md`), so edge cases (rare fields/sections) may be mishandled or omitted.
- Large or complex resumes could increase the chance of structural mistakes in the generated JSON (e.g., missing UUIDs, minor format inconsistencies).
**Recommended scenarios**
- When a user clearly wants a resume that can be imported into **Reactive Resume (rxresu.me)** as JSON.
- When the user is comfortable providing detailed factual information and wants guidance on phrasing, impact, and structure.
- For iterative resume refinement where you repeatedly adjust sections, layout, or content and regenerate schema-compliant JSON.
- Less ideal when you need arbitrary resume formats (PDF/Word only) without any need for the Reactive Resume schema, or when strict automated schema validation is required inside the skill itself.
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