Agent skill · Data & Analytics

alumni-career-tracker

Analyze laboratory alumni career trajectories and outcomes to provide data-driven career guidance for current students and postdocs. Tracks industry vs academia distribution, identifies career pathways, and generates personalized recommendations based on degree level and research interests.

majiayu000github.com/majiayu000GitHub ↗
claude-codecan modify filesMIT
Install
npx skills add majiayu000/claude-skill-registry --skill alumni-career-tracker --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 14 KB
Bundled scripts: none
Allowed tools: ReadWriteBashEdit
Path: skills/analysis/alumni-career-tracker/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Alumni Career Tracker ## Overview Career analytics tool that tracks and analyzes the professional destinations of laboratory alumni, providing evidence-based guidance for trainees navigating career transitions. **Key Capabilities:** - **Career Outcome Tracking**: Monitor alumni destinations across sectors - **Trajectory Analysis**: Map career progression patterns over time - **Skills Gap Identification**: Compare training vs. job requirements - **Salary Benchmarking**: Track compensation trends by degree and sector - **Network Mapping**: Visualize alumni connections and pathways - **Personalized Guidance**: Generate tailored career recommendations ## When to Use **✅ Use this skill when:** - Mentoring new students on career options and trajectories - Training grant applications requiring career outcome data (e.g., NIH T32, F32) - Lab website showcasing successful alumni for recruitment - Departmental reviews demonstrating training effectiveness - Individual career counseling sessions with trainees - Identifying industry partners and collaboration opportunities - Benchmarking your lab's career outcomes against peers **❌ Do NOT use when:** - Job placement services (out of scope) → U

What's inside
Steps it walks through
  1. Overview
  2. When to Use
  3. Core Capabilities
  4. 1. Alumni Database Management
  5. 2. Career Outcome Analysis
  6. 3. Career Pathway Mapping
  7. 4. Personalized Career Recommendations
  8. Common Patterns
  9. Pattern 1: New Student Onboarding
  10. Pattern 2: Training Grant Application
  11. Pattern 3: Industry Partnership Development
  12. Pattern 4: Individual Career Counseling
  13. Complete Workflow Example
  14. Quality Checklist
Ships with 1 file
  • metadata.json
Commands it runs
Generate career landscape overview
python scripts/main.py \
Show specific pathways for their research area
Analyze industry destinations
Identify senior alumni for advisory roles
Step 1: Import existing alumni data
Step 2: Update LinkedIn profiles
Step 3: Generate comprehensive report
Step 4: Create visualization dashboard
More from claude-skill-registry
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About this skill
What does the alumni-career-tracker skill do?

Analyze laboratory alumni career trajectories and outcomes to provide data-driven career guidance for current students and postdocs. Tracks industry vs academia distribution, identifies career pathways, and generates personalized recommendations based on degree level and research interests.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill alumni-career-tracker --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going