Keyword Velocity Tracker

Calculate literature growth velocity and acceleration to assess research.

Security Scan

The skill's code, runtime instructions, and requirements are consistent with a local analysis tool for keyword publication velocity; it requests no credentials or unusual system access.

Purpose & Capability

Name/description (calculate literature growth velocity/acceleration) matches the included Python implementation (scripts/main.py) and the SKILL.md usage. Requested libraries (numpy, scipy) are appropriate for numerical analysis. Minor inconsistency: SKILL.md mentions scipy but requirements.txt omits it and includes stdlib modules (dataclasses, enum) unnecessarily.

Instruction Scope

SKILL.md instructs the agent to validate inputs, run the packaged script, and produce bounded outputs. The instructions only reference running the local script and editing an in-file CONFIG block; they do not ask the agent to read unrelated system files, access external endpoints, or exfiltrate secrets.

Install Mechanism

No install spec is present (instruction-only with an included script). This minimizes install-time risk — execution is local Python. The included files are visible so there is no hidden download-from-URL behavior.

Credentials

No environment variables, credentials, or config paths are required. The skill does not request unrelated secrets or cloud credentials.

Persistence & Privilege

Skill does not request always:true and does not declare modifications to other skills or system-wide configuration. Autonomous invocation is allowed by platform default but is not combined with elevated privileges or credential access.

Assessment

This package appears coherent and local-only: review scripts/main.py yourself (it is included) and run python -m py_compile scripts/main.py to verify. Note the small mismatch between SKILL.md (mentions scipy) and requirements.txt; install numpy (and scipy if you plan to use smoothing/advanced math) in a sandbox environment. Before running on sensitive systems, run the script on sample data to confirm output and check for any unexpected file writes or network calls (there are no obvious network calls in the visible code). If you need stronger assurance, run the script in an isolated container and inspect/grep the source for os, subprocess, socket, requests, or similar imports first.

Skill: Keyword Velocity Tracker

When to Use

• Use this skill when the task needs Calculate literature growth velocity and acceleration to assess research.
• Use this skill for evidence insight tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

• Scope-focused workflow aligned to: Calculate literature growth velocity and acceleration to assess research.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python >= 3.8
• numpy
• scipy

Example Usage

cd "20260318/scientific-skills/Evidence Insight/keyword-velocity-tracker"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

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}
    ]
)

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

Output Requirements

Every final response should make these items explicit when they are relevant:

• Objective or requested deliverable
• Inputs used and assumptions introduced
• Workflow or decision path
• Core result, recommendation, or artifact
• Constraints, risks, caveats, or validation needs
• Unresolved items and next-step checks

Error Handling

• If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
• If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
• If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
• Do not fabricate files, citations, data, search results, or execution outcomes.

Input Validation

This skill accepts requests that match the documented purpose of keyword-velocity-tracker and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

keyword-velocity-tracker only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

References

• references/audit-reference.md - Supported scope, audit commands, and fallback boundaries

Response Template

Use the following fixed structure for non-trivial requests:

1. Objective
2. Inputs Received
3. Assumptions
4. Workflow
5. Deliverable
6. Risks and Limits
7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.