data-training-manager
Manage AI training data, monitor content freshness, detect repetition, and update training samples for continuous learning. Use when managing training data, checking content quality, updating AI models, or preventing repetitive content.
npx skills add majiayu000/claude-skill-registry --skill data-training-manager --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Data Training Manager Continuous learning system for managing AI training data, monitoring content freshness, and preventing repetitive outputs. ## Overview Maintain high-quality AI outputs through: - **Training Data Management** - Add, update, remove training samples - **Freshness Monitoring** - Detect stale and repetitive content - **Quality Scoring** - Track performance of training samples - **Continuous Learning** - Automatically update based on engagement - **Trend Analysis** - Identify patterns in successful content ## Quick Start ### 1. Check Training Data Freshness ```python from src.freshness_monitor import FreshnessMonitor monitor = FreshnessMonitor() # Check if generated content is fresh score = monitor.check_freshness( generated_text="gm to data contributors who deserve equity...", threshold=0.7 # 70% uniqueness required ) if score < 0.7: print("⚠️ Content too similar to existing samples") else: print("✅ Content is fresh!") ``` ### 2. Add New Training Sample ```python from src.continuous_learning import ContinuousLearningSystem learning = ContinuousLearningSystem() # Add high-performing tweet learning.add_sample( text="gm to everyone building on @base 💙", type="gm",
- Overview
- Quick Start
- 1. Check Training Data Freshness
- 2. Add New Training Sample
- 3. Manage Training Data
- Training Data Structure
- Sample Format
- Training Files
- Freshness Monitoring
- How It Works
- Usage
- Freshness Thresholds
- Continuous Learning System
- Auto-Update from Performance
Check freshness of all samples python scripts/manage_training.py check View statistics python scripts/manage_training.py stats Add new sample python scripts/manage_training.py add \ Checking gm_posts.json... Checking codatta_insights.json... Overall freshness: 93% Training Data Statistics
What does the data-training-manager skill do?
Manage AI training data, monitor content freshness, detect repetition, and update training samples for continuous learning. Use when managing training data, checking content quality, updating AI models, or preventing repetitive content.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill data-training-manager --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.