Format CVs for academic positions with publications, grants, and teaching
日本語の概要は準備中です。原文の説明を表示しています。
Evaluates fresher-specific hiring signals that generic resume tools ignore. Scores 9 dimensions critical for new-grad hiring (CGPA, DSA, project depth, GitHub quality, deployment evidence, etc.) and identifies the weakest signal with improvement recommendations.
日本語の概要は準備中です。原文の説明を表示しています。
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Use this skill when the candidate has ≤2 years of professional experience (internships count as 0.5 years each). Run it as part of Phase 1 alongside evidence extraction.
Trigger phrases: "analyze my profile", "am I ready to apply", "what should I improve", "fresher resume check", "new grad profile review"
Fresher hiring is signal-scarce. Every signal matters more.
When a candidate has 5+ years of experience, the resume writes itself from work history. When a candidate has 0-2 years, recruiters look for different signals: CGPA, DSA ability, project depth, GitHub quality, certifications, hackathons.
Your resume pipeline must know which signals are strong, which are weak, and which are missing — before optimizing the resume.
Score each dimension 1-10. Identify the weakest signal and recommend improvement.
| Score | Criteria |
|---|---|
| 9-10 | CGPA ≥ 9.0 / GPA ≥ 3.8 at a recognized institution |
| 7-8 | CGPA 8.0-8.9 / GPA 3.5-3.7 |
| 5-6 | CGPA 7.0-7.9 / GPA 3.0-3.4 |
| 3-4 | CGPA 6.0-6.9 / GPA 2.5-2.9 |
| 1-2 | CGPA < 6.0 or not reported |
Resume impact: Display CGPA prominently if ≥ 8.0. Hide if < 7.0. For 7.0-7.9, include only if the JD doesn't specify a minimum.
| Score | Criteria |
|---|---|
| 9-10 | 2+ internships at recognized companies, each 3+ months, with measurable technical output |
| 7-8 | 1-2 internships with clear technical contributions and specific technologies |
| 5-6 | 1 internship with basic technical exposure |
| 3-4 | 1 short internship (< 2 months) or non-technical internship |
| 1-2 | No internships |
What to evaluate in each internship:
| Score | Criteria |
|---|---|
| 9-10 | 500+ problems solved, active contest participation, strong rating (e.g., LeetCode top 10%) |
| 7-8 | 200-500 problems, occasional contests, DSA visible in projects |
| 5-6 | 100-200 problems, DSA in coursework, basic competitive programming |
| 3-4 | < 100 problems, DSA only in academic projects |
| 1-2 | No visible DSA evidence |
[!WARNING] For Indian SDE fresher hiring, DSA is often the #1 technical screening criterion. A beautiful resume cannot compensate for DSA weakness when the recruiter's first filter is "can this person solve a medium-level coding problem?"
Resume impact: If DSA evidence exists, include it. If it doesn't, this is a profile improvement priority, not a resume improvement priority.
| Score | Criteria |
|---|---|
| 9-10 | 2+ projects with clear architecture, multiple components, technical decisions documented, tests, deployed |
| 7-8 | 2-3 projects with genuine engineering (not tutorials), clear problem-solution structure |
| 5-6 | 3-4 projects but mostly CRUD/tutorial-level with minor customization |
| 3-4 | Projects exist but appear to be tutorial follow-alongs |
| 1-2 | No projects or only college assignments |
What makes a project "deep" (not just present):
Score EACH of the candidate's top 3 repositories, then average.
| Dimension | 1-3 (Weak) | 4-6 (Adequate) | 7-10 (Strong) |
|---|---|---|---|
| README | Missing/template | Basic setup instructions | Purpose, architecture, screenshots, setup, usage |
| Commit history | 1-3 bulk commits | Regular commits, no message quality | Descriptive commits, development progression visible |
| Code structure | Flat, all in root | Basic folders | Clear architecture, separation of concerns |
| Tests | None | Some test files | Test coverage for core functionality |
| Documentation | None | Inline comments | Docstrings + API docs |
Resume impact: If GitHub quality is ≤ 4, recommend the candidate improve their top 2 repos BEFORE applying. A recruiter who clicks a GitHub link and sees a bare repo with one commit loses confidence in the entire resume.
| Score | Criteria |
|---|---|
| 9-10 | Industry-recognized certifications directly matching target role (e.g., Azure AI for AI roles, AWS SAA for cloud roles) |
| 7-8 | Relevant certifications from recognized issuers (Microsoft, AWS, Google, Oracle) |
| 5-6 | Certifications exist but are tangentially related to target role |
| 3-4 | Only MOOC completion certificates (Coursera, Udemy) |
| 1-2 | No certifications |
Rule: Certifications are supporting evidence, never the center of a fresher profile. 2 relevant > 5 irrelevant.
| Score | Criteria |
|---|---|
| 9-10 | Won/finalist in recognized hackathon (SIH, HackMIT, etc.) or strong competitive programming rating |
| 7-8 | Selected for internal rounds of major hackathon, or regular contest participation |
| 5-6 | Participated in college-level hackathons |
| 3-4 | Participated but no notable outcome |
| 1-2 | No hackathon or competition experience |
| Score | Criteria |
|---|---|
| 9-10 | Multiple projects deployed and live with working demo links |
| 7-8 | At least 2 projects deployed (Netlify, Vercel, Streamlit Cloud, etc.) |
| 5-6 | 1 project deployed, others local only |
| 3-4 | Docker/containerization but no live deployment |
| 1-2 | All projects are local/README only |
Resume impact: Working demo links are extremely high-signal. A recruiter clicking a demo link and seeing a working application is worth more than 5 resume bullets.
This is a meta-signal that evaluates whether the candidate has depth in a few areas or surface knowledge across many.
| Score | Criteria |
|---|---|
| 9-10 | Deep expertise in 2-3 technology areas, each supported by multiple projects + work experience |
| 7-8 | Clear primary technology area with supporting breadth |
| 5-6 | Moderate depth across 3-4 areas |
| 3-4 | Surface-level exposure to 5+ areas, no clear depth |
| 1-2 | Technology list looks like a keyword dump with no evidence of depth |
How to measure depth in a technology:
## Fresher Signal Analysis Report
**Candidate:** [Name]
**Experience Level:** [0-2 years + internship count]
**Date:** [Date]
### Signal Scores
| # | Signal | Score | Assessment |
|---|---|---|---|
| 1 | Academic Performance | X/10 | [Brief assessment] |
| 2 | Internship Quality | X/10 | [Brief assessment] |
| 3 | DSA Evidence | X/10 | [Brief assessment] |
| 4 | Project Depth | X/10 | [Brief assessment] |
| 5 | GitHub Quality | X/10 | [Brief assessment] |
| 6 | Certification Relevance | X/10 | [Brief assessment] |
| 7 | Hackathon/Competition | X/10 | [Brief assessment] |
| 8 | Deployment Evidence | X/10 | [Brief assessment] |
| 9 | Depth vs. Breadth | X/10 | [Brief assessment] |
**Overall Signal Strength:** X/10 (average)
### Weakest Signal: [Signal Name] (X/10)
**Why this matters:** [Explanation of why this signal is important for the target role]
**Improvement Plan:**
1. [Specific action to improve this signal]
2. [Specific action]
3. [Timeline estimate]
### Strongest Signals (Lead with these)
1. [Signal] — [How to leverage this on the resume]
2. [Signal] — [How to leverage]
### Signal Gap by Role Family
| Signal | SDE Impact | AI/ML Impact | Full Stack Impact |
|---|---|---|---|
| DSA | CRITICAL | Medium | Medium |
| Project Depth | High | CRITICAL | High |
| Certifications | Low | High | Low |
| Deployment | Medium | Medium | CRITICAL |
### Resume Optimization Priority
Based on signal analysis, the resume should:
1. [Most important emphasis]
2. [Second priority]
3. [What to de-emphasize]
### Profile Improvement Priority (Outside the Resume)
These improvements would have MORE impact than resume optimization:
1. [Improvement 1 — e.g., "Solve 200+ DSA problems on LeetCode"]
2. [Improvement 2 — e.g., "Add README, screenshots, and tests to top 2 GitHub repos"]
3. [Improvement 3]
After scoring all 9 signals, apply this test:
If ≥3 signals score ≤4/10, output this warning:
> [!CAUTION]
> ### Your resume is NOT your primary bottleneck.
>
> With [N] weak signals, improving your resume from 70/100 to 90/100 will have
> minimal impact on your callback rate. Your effort should be split:
>
> - 30% Resume + application strategy
> - 30% [Weakest signal improvement]
> - 20% [Second weakest signal improvement]
> - 20% Interview preparation
>
> NOT:
> - 80% Resume optimization
> - 20% Everything else
>
> The resume can get you into the funnel. It cannot compensate for [weak signal].
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Format CVs for academic positions with publications, grants, and teaching
日本語の概要は準備中です。原文の説明を表示しています。
Decides whether to apply to a specific role, with what level of effort, and with which resume variant. Prevents wasted applications on roles with fundamentally poor fit and prioritizes high-probability opportunities.
日本語の概要は準備中です。原文の説明を表示しています。
Decide how to prioritize job applications using hard eligibility, evidence coverage, role alignment, and real application outcomes rather than arbitrary fit scores.
日本語の概要は準備中です。原文の説明を表示しています。
Define a clear primary role identity and supporting evidence so a resume reads coherently for the target job.
日本語の概要は準備中です。原文の説明を表示しています。
Determines the candidate's primary role identity before any JD analysis. Classifies into role families, selects core tech identity, ranks projects by role relevance, and outputs a Candidate Position Brief consumed by all downstream stages. This is Phase -1 of the pipeline.
日本語の概要は準備中です。原文の説明を表示しています。
Translate skills from one industry to another, identify transferable skills
日本語の概要は準備中です。原文の説明を表示しています。