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keyword-velocity-tracker关键词速度跟踪器

Agent Skill

keyword-velocity-tracker 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

3,635

周安装

153

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1,273
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:keyword-velocity-tracker(关键词速度跟踪器)
来源仓库:https://github.com/aipoch-ai/keyword-velocity-tracker
安装命令:
openclaw skills install keyword-velocity-tracker
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

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复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install keyword-velocity-tracker

简介

keyword-velocity-tracker 计算文献增长速率与加速度,评估研究热点趋势。

  • 适用于学术调研、技术方向预测与知识图谱构建。
  • 安装命令:openclaw skills install keyword-velocity-tracker。
  • 依赖外部数据库或 API,需确认数据源更新频率与覆盖范围。
  • 结果受检索策略影响较大,建议交叉验证多个指标。

SKILL.md

name
keyword-velocity-tracker
description
Calculate literature growth velocity and acceleration to assess research.
license
MIT
skill-author
AIPOCH

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

ParameterTypeDescription
keywordstringKeyword to analyze
dataarrayTime series literature data, format: [{"year": 2020, "count": 100}, ...]

Optional Parameters

ParameterTypeDefaultDescription
time_windowint3Time window for calculating growth rate (years)
smoothingbooleantrueWhether to smooth the data
predict_yearsint3Number 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

VariableDescriptionDefault
KVT_SMOOTHING_FACTORSmoothing coefficient0.3
KVT_MIN_CONFIDENCEMinimum confidence threshold0.7

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 IndicatorAssessmentLevel
Code ExecutionPython/R scripts executed locallyMedium
Network AccessNo external API callsLow
File System AccessRead input files, write output filesMedium
Instruction TamperingStandard prompt guidelinesLow
Data ExposureOutput files saved to workspaceLow

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

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.

适合场景

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能力概览

能力 1

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能力 2

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能力 3

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能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

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