This is a submission for the GitHub Finish-Up-A-Thon Challenge I built the Atomberg Goal Setting & Tracking Portal — a full-featured,
role-based HR performance management system for Atomberg Technologies (a
fast-growing Indian consumer electronics brand).
The portal manages the complete employee performance lifecycle:
Tech Stack: React 18 + Vite · Context API · localStorage (zero-backend
demo) · Recharts · Lucide Icons · Custom dark glassmorphism CSS
🔗 **Live App:** [https://atomberg-goal-tracker.vercel.app/](https://atomberg-goal-tracker.vercel.app/)
📁 **GitHub Repo:** [https://github.com/anonomous29/atomberg-goal-tracker](https://github.com/anonomous29/atomberg-goal-tracker)
| Role | Password | |
|---|---|---|
| Admin | ||
This project started as a hackathon requirement — a Business Requirements
Document (BRD) from Atomberg asking for a digital goal management system. The
initial version had the core structure but was essentially an empty shell:
blank dashboards on login, no check-in data, no analytics, and missing two
bonus modules entirely.
What I finished up:
The biggest unlock was the Escalation Monitor — it transformed the portal
from a passive data entry tool into an active compliance tracker that tells managers exactly who is falling behind and why.
GitHub Copilot was integral to finishing this project quickly. Here's
specifically how it helped:
1. Boilerplate elimination
The score computation engine, localStorage CRUD helpers, and recharts
configurations were tedious to write from scratch. Copilot autocompleted
entire function bodies after seeing the first few lines of pattern.
2. Business logic translation
The BRD had complex UoM scoring rules (Numeric Min, Numeric Max, Timeline,
Zero). Copilot helped translate the prose spec directly into the computeScore
switch statement — getting the inverted formula for "lower is better" metrics correct on the first try.
3. React pattern consistency
Across 10+ components, Copilot kept prop patterns, className conventions, and
inline style objects consistent — no more copy-paste drift between components. 4. Escalation rule engine
The most valuable assist: when I described the escalation rules in a comment
block, Copilot generated the full predicate functions for each rule type,
including edge cases like "employee has goals but hasn't submitted them."
5. Seed data generation
Writing 16 realistic performance goals with correct weightages summing to 100%,
meaningful descriptions, and plausible Q1 achievement numbers would have taken
an hour manually. Copilot drafted the full INITIAL_GOALS
and
INITIAL_CHECKINS
arrays in minutes.
Overall, Copilot cut the "finishing up" time from an estimated 2 days to a
single focused session — letting me ship the escalation module, analytics
overhaul, and rich demo data all in one go.
Atomberg Goal Tracker — Demo Screenshots
Screen 1: Admin Dashboard
Admin Dashboard
Organization-wide overview — team stats, department progress, quick actions
Screen 2: Escalation Monitor (Bonus Feature) Escalation Monitor
Rule-based compliance tracker — Critical / High / Medium / Low severity alerts
Screen 3: QoQ Analytics — Reports & Analytics
QoQ Trends
Quarter-on-Quarter performance trends — all employees compared across Q1–Q4
Screen 4: Employee Dashboard
Employee Dashboard
Personal dashboard with Q1 score donut, approved goals, and cycle timeline
1. Boilerplate elimination
The score computation engine, localStorage CRUD helpers, and recharts
configurations were tedious to write from scratch. Copilot autocompleted
entire function bodies after seeing the first few lines of pattern.
2. Business logic translation
The BRD had complex UoM scoring rules (Numeric Min, Numeric Max, Timeline,
Zero). Copilot helped translate the prose spec directly into the computeScore
switch statement — getting the inverted formula for "lower is better" metrics correct on the first try.
3. React pattern consistency
Across 10+ components, Copilot kept prop patterns, className conventions, and
inline style objects consistent — no more copy-paste drift between components. 4. Escalation rule engine
The most valuable assist: when I described the escalation rules in a comment
block, Copilot generated the full predicate functions for each rule type,
including edge cases like "employee has goals but hasn't submitted them."
5. Seed data generation
Writing 16 realistic performance goals with correct weightages summing to 100%,
meaningful descriptions, and plausible Q1 achievement numbers would have taken
an hour manually. Copilot drafted the full INITIAL_GOALS
and
INITIAL_CHECKINS
arrays in minutes.
Overall, Copilot cut the "finishing up" time from an estimated 2 days to a
single focused session — letting me ship the escalation module, analytics
overhaul, and rich demo data all in one go.g! -->