test assignment reviewer
by callitmagic
Deep structured review of technical test assignments with scoring, gap analysis, and interview questions.
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Works with the AI tools you already use
See it in action
You say
Review this candidate's repo at https://github.com/user/task-app against the requirements in this PDF: https://company.com/frontend-test.pdf. Provide a full evaluation.
Your agent does
VERDICT
[HIRE] The candidate implemented the full React/Redux stack as requested. The custom hook for data fetching shows advanced state management.
Overall Score: 52 / 60
| Signal | Status | |---|---| | Task completion | ✅ Full | | Stack compliance | ✅ Full | | Architecture | ✅ Full | | Code quality | ✅ Strong | | Error handling | ⚠️ Partial | | Tests | ✅ Present |
... [Detailed scores and interview questions follow]
What you get
About this skill
The problem
Reviewing technical test assignments is a time-consuming bottleneck for engineering teams. Manually cross-referencing a candidate's repository against a multi-page task description often leads to inconsistent scoring and missed red flags.
What it does
- Parses task descriptions from GitHub, PDFs, or Notion to extract mandatory features and constraints.
- Audits candidate implementation code for stack compliance, architecture patterns, and code quality.
- Generates a structured verdict (HIRE/NO HIRE) with a 60-point scoring matrix across six key signals.
- Produces a line-by-line gap analysis comparing specific requirements to actual implementation.
- Drafts 5-7 targeted interview questions based on identified deviations or suspicious code choices.
Frameworks & tools
Works with GitHub repositories, deployed applications, and document formats including PDF, Notion, and Google Docs. Uses Bash and WebFetch for deep repository inspection.
Why this beats prompting it yourself
This skill enforces a strict, evidence-based evaluation framework that prevents hallucinated praise or vague feedback. It automates the tedious task of requirement mapping, ensuring no mandatory feature is overlooked, and provides export-ready artifacts for your ATS or team wiki.
Use cases
- Reviewing full-stack take-home assignments for mid-to-senior level roles.
- Screening initial repository submissions before scheduling technical interviews.
- Generating standardized hiring reports for engineering managers and stakeholders.
- Audit candidate decision-making by identifying intentional deviations from the task prompt.
Known limitations
Evaluation of deployed apps without source code is limited to UX and feature completeness. ZIP archives must be described or pasted inline as direct file uploads are not supported.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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Creator
6 skills on Agensi
◦ Prompt Engineer for Claude, ChatGPT & visual AI systems ◦ Building Claude automations, custom skills & AI-assisted workflows ◦ Generating code, interfaces, prompts, visuals & long-form content with AI ◦ IBM Certified Prompt Engineer ◦ Books author using Manus AI (some fun) Open to DMs and projects
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