Skill: Keyword Velocity Tracker

Metadata

• ID: 201
• Name: Keyword Velocity Tracker
• Type: Analysis Tool
• Version: 1.0.0

Description

Calculate the literature growth rate and acceleration of specific keywords to determine the development stage of academic research fields. By analyzing changes in literature volume over different time periods, provide field popularity trends and lifecycle analysis.

Functions

Core Functions

1. Literature Growth Rate Calculation - Calculate keyword literature growth rate over different time periods
2. Growth Acceleration Analysis - Identify trends of literature growth acceleration or deceleration
3. Field Development Stage Judgment - Determine field stage based on growth curve characteristics
4. Trend Prediction - Predict future development trends based on historical data

Stage Judgment Criteria

• Embryonic Stage: Low base, slow growth
• Growth Stage: Growth rate continues to rise (acceleration is positive)
• Mature Stage: Growth rate is stable or declining
• Decline Stage: Growth rate is negative

Input

Required Parameters

Parameter	Type	Description
keyword	string	Keyword to analyze
data	array	Time series literature data, format: [{"year": 2020, "count": 100}, ...]

Optional Parameters

Parameter	Type	Default	Description
time_window	int	3	Time window for calculating growth rate (years)
smoothing	boolean	true	Whether to smooth the data
predict_years	int	3	Number of future years to predict

Output

Return Value

{
  "keyword": "artificial intelligence",
  "analysis_period": {"start": 2015, "end": 2023},
  "current_velocity": 0.35,
  "current_acceleration": -0.05,
  "stage": "mature",
  "stage_confidence": 0.85,
  "trend": "stable",
  "velocity_series": [
    {"year": 2016, "velocity": 0.20, "acceleration": null},
    {"year": 2017, "velocity": 0.25, "acceleration": 0.05},
    ...
  ],
  "prediction": {
    "2024": {"estimated_count": 1850, "confidence": 0.80},
    "2025": {"estimated_count": 1980, "confidence": 0.70},
    "2026": {"estimated_count": 2100, "confidence": 0.60}
  },
  "insights": [
    "Field has entered mature stage, growth slowing",
    "Recent slight deceleration trend, needs attention"
  ]
}

Stage Definitions

• current_velocity: Current annual growth rate (0-1)
• current_acceleration: Current acceleration (growth rate change rate)
• stage: Field development stage (embryonic/growth/mature/decline)
• stage_confidence: Stage judgment confidence (0-1)
• trend: Trend direction (growth/stable/decline)

Usage Examples

Command Line

python scripts/main.py --keyword "artificial intelligence" --data-file data.json

Python API

from skills.keyword_velocity_tracker.scripts.main import KeywordVelocityTracker

tracker = KeywordVelocityTracker()
result = tracker.analyze(
    keyword="artificial intelligence",
    data=[
        {"year": 2019, "count": 500},
        {"year": 2020, "count": 650},
        {"year": 2021, "count": 900},
        {"year": 2022, "count": 1100},
        {"year": 2023, "count": 1250}
    ]
)

Dependencies

• Python >= 3.8
• numpy
• scipy

Configuration

Environment Variables

Variable	Description	Default
KVT_SMOOTHING_FACTOR	Smoothing coefficient	0.3
KVT_MIN_CONFIDENCE	Minimum confidence threshold	0.7

Algorithm Description

Growth Rate Calculation

velocity(t) = (count(t) - count(t-1)) / count(t-1)

Acceleration Calculation

acceleration(t) = velocity(t) - velocity(t-1)

Stage Judgment Logic

1. Average growth rate in last 3 years < 0.1 → Embryonic/Decline stage
2. Acceleration > 0 and growth rate > 0.2 → Growth stage
3. Growth rate stable (fluctuation < 0.1) → Mature stage
4. Growth rate < 0 → Decline stage

Version History

• 1.0.0 (2024-02-06): Initial version, basic growth rate and acceleration calculation

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python/R scripts executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output files	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output files saved to workspace	Low

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support