菜单
Generates step-by-step solutions for coding problems, numericals, and programming assignments for ANY university worldwide. Produces explanation, algorithm, pseudocode, actual code, complexity analysis, and test cases. Works for ALL programming languages, ALL subjects, ALL exam patterns.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Universal agent normalizer that detects which AI coding agent is running and maps the Exam Prompt skill system to the agent's native configuration format. Supports 20+ coding agents as of June 2026. Ensures every agent can discover, load, and invoke all 30+ skills regardless of its native skill mechanism. Run this skill first whenever you are onboarding a new agent or if skills fail to load. Works for any university worldwide.
Generates exam-ready A+ answers for ANY university worldwide — SPPU, VTU, IITs, NITs, MIT, Oxford, Cambridge, Harvard, NUS, and 50+ more. Covers ALL question types (theory, numerical, MCQ, case study, derivation, diagram, design, etc.), ALL mark levels (1-100+), ALL exam conditions (closed/open book, online, oral, lab, take-home), ALL Bloom's levels, and ALL command words. Pre-configured with SPPU Computer Engineering intelligence. Universally adaptable via syllabus PDF input. Trigger on ANY exam answer request. Never trigger for lab practical reports, project documentation, or coding assignments unless asked.
Generates assignment-ready content for any university worldwide across all assignment types (theory, numerical, case study, research, literature review, project proposals, design, analysis, comparison, critical review, problem-solving, and diagram/illustration). Supports multiple output formats, evaluation depth mapping, marking scheme generation, and citation requirements. University-agnostic.
Generates exam-ready question papers for ANY university worldwide. Supports 11+ university patterns (SPPU, VTU, JNTU, Mumbai, AKTU, RGPV, IIT/NIT, North American, UK/Oxford/Cambridge, Generic), 13 question types, CO/Bloom mapping, marking schemes, and model answer keys.
Generates syllabus-locked, exam-ready academic notes for ANY university worldwide. Supports 12+ note formats with automatic format selection based on subject type, exam conditions, time available, and learning style. Works across all departments, disciplines, and degree programs.
Analyzes Previous Year Question Papers (PYQs) and official syllabus from ANY university worldwide to generate high-probability exam topics, question patterns, unit-wise trends, Bloom's distribution, marks analysis, CO/outcome mapping, and exam strategy. Trigger when user asks to analyze PYQs, find important topics, understand exam patterns, or wants strategic preparation guidance. Requires PYQ PDFs and syllabus as input. Never generates answers or teaches concepts.
| name | universal-code-solution-generator |
| description | Generates step-by-step solutions for coding problems, numericals, and programming assignments for ANY university worldwide. Produces explanation, algorithm, pseudocode, actual code, complexity analysis, and test cases. Works for ALL programming languages, ALL subjects, ALL exam patterns. |
Generates exam-ready coding solutions with proper formatting, explanation, and analysis.
Trigger when user asks for:
Problem: [Original problem statement]
Approach: [Strategy explanation]
Algorithm:
Step 1: ...
Step 2: ...
Pseudocode:
[language-agnostic pseudocode]
Implementation ([Language]):
```[language]
[actual code]
Complexity:
Test Cases: | Input | Expected Output | Actual Output | |-------|----------------|---------------| | ... | ... | ... |
Edge Cases:
Dry Run (for small input): [manual trace with variable states at each step]
## Supported Languages
C, C++, Java, Python, JavaScript, TypeScript, Go, Rust, Kotlin, SQL, R, MATLAB, and any language specified by the user.
---
## Error Handling
| Situation | Action |
|---|---|
| Language not specified | Auto-detect from problem context or ask user; default to Python if ambiguous |
| Code incomplete | Generate complete solution with full implementation; note any assumptions |
| Complexity ambiguous | Analyze both time and space complexity with justification for each |
| Test cases missing | Generate at least 3 test cases including normal, edge, and boundary cases |
## Quality Gate — Check Before Output
- [ ] Code compiles/runs without syntax errors in the specified language
- [ ] Complexity analysis present with O-notation and clear justification
- [ ] Edge cases handled (empty input, boundary values, null/undefined, large inputs)
- [ ] Output matches expected format (problem → approach → algorithm → pseudocode → code → complexity → tests)
- [ ] Test cases include input, expected output, and actual output columns
## Integration with Other Skills
| Skill | Integration |
|---|---|
| **universal-session-config** | Reads language preferences and subject context from session profile |
| **universal-a-plus-answer-writer** | Provides exam-ready answer techniques for code-based exam questions |
| **universal-diagram-generator** | Renders flowcharts and algorithm diagrams from Mermaid code blocks in solutions |