Auto Up Skill Sro 〈Premium〉

new_score = min(100, max(0, raw_update)) # clamp 0–100 return round(new_score, 1)

return ( <div className="skill-card"> <h3>Skill Rating (Auto SRO)</h3> <div className="score">currentScore / 100</div> <label> <input type="checkbox" checked=autoEnabled onChange=(e) => setAutoEnabled(e.target.checked) /> Enable auto up-skilling </label> <button onClick=triggerManualUpgrade>Force Recalculate</button> <small>Auto-updates daily based on recent performance & peer comparison.</small> </div> );

"user_id": 101, "skill_id": 5, "force_recalc": false

# Formula raw_update = ( 0.4 * recent_avg + 0.3 * task_success_rate * 100 + 0.2 * peer_percentile + 0.1 * self.current_score ) * decay_factor auto up skill sro

def apply_time_decay(self): days_since_last_activity = self.get_inactivity_days() if days_since_last_activity > 14: return max(0.7, 1 - (days_since_last_activity - 14) * 0.01) return 1.0

def get_peer_percentile(self): # Compare with all users for same skill all_scores = get_all_sro_scores(self.skill_id) return percentile(all_scores, self.current_score)

I'll help you develop a feature for (likely a Skill Rating/Optimization system, or an auto-upgrading mechanism for a Skill Ranking Object in a game or LMS). new_score = min(100, max(0, raw_update)) # clamp 0–100

Run daily at 02:00 :

"status": "success", "previous_score": 74.2, "new_score": 78.5, "delta": +4.3, "factors": "recent_performance": 82.0, "task_success_rate": 88.5, "peer_percentile": 65.0, "decay_applied": 0.98

from celery import shared_task @shared_task def batch_auto_upgrade_skill_sro(): active_users = get_users_with_recent_activity(days=7) for user in active_users: for skill in user.enrolled_skills: engine = AutoUpSkillSRO(user.id, skill.id) result = engine.trigger_auto_update() if result["updated"]: notify_user_if_needed(user, result) function AutoUpSkillWidget( userId, skillId ) const [currentScore, setCurrentScore] = useState(null); const [autoEnabled, setAutoEnabled] = useState(true); const triggerManualUpgrade = async () => const res = await fetch("/api/v1/sro/auto-upgrade", method: "POST", body: JSON.stringify( user_id: userId, skill_id: skillId, force_recalc: true ) ); const data = await res.json(); setCurrentScore(data.new_score); showToast( Score updated: $data.delta > 0 ? "+" : ""$data.delta ); ; including backend logic

def trigger_auto_update(self): new_score = self.compute_new_score() if abs(new_score - self.current_score) >= 1.0: self.update_sro_score(new_score) self.log_skill_change() return "updated": True, "old": self.current_score, "new": new_score return "updated": False ALTER TABLE skill_rating ADD COLUMN auto_upgrade_enabled BOOLEAN DEFAULT TRUE; ALTER TABLE skill_rating ADD COLUMN last_auto_calc TIMESTAMP; ALTER TABLE skill_rating ADD COLUMN decay_rate DECIMAL(3,2) DEFAULT 0.98; CREATE TABLE skill_auto_log ( id SERIAL PRIMARY KEY, user_id INT, skill_id INT, old_score DECIMAL(5,2), new_score DECIMAL(5,2), reason TEXT, calculated_at TIMESTAMP DEFAULT NOW() ); 3. API Endpoint POST /api/v1/sro/auto-upgrade

Below is a structured feature design, including backend logic, API, database changes, and a simple UI concept. Objective Automatically adjust a user’s skill score/level based on recent performance, task completion, peer comparison, and time decay — without manual intervention. 1. Core Logic (Python-like pseudocode) class AutoUpSkillSRO: def __init__(self, user_id, skill_id): self.user_id = user_id self.skill_id = skill_id self.current_score = self.get_current_sro_score() self.performance_history = self.get_recent_assessments(days=30) def compute_new_score(self): # Factors recent_avg = self.average_last_n_scores(5) task_success_rate = self.get_task_success_rate() peer_percentile = self.get_peer_percentile() decay_factor = self.apply_time_decay()