Token导航 LogoToken导航

Agent Skill · 研究检索

keyword-velocity-tracker(关键词速度跟踪器)

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。

keyword-velocity-tracker 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 26,113。

总安装 3,635周安装 153GitHub Stars 公开资料未说明下载 1,273核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

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。

命令行安装

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install keyword-velocity-tracker;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

keyword-velocity-tracker 怎么安装?

在终端执行 openclaw skills install keyword-velocity-tracker。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

keyword-velocity-tracker 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

keyword-velocity-tracker 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

keyword-velocity-tracker 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

keyword-velocity-tracker 用的人多吗?

公开数据是累计安装 3,635 次,近一周安装 153 次,下载 1,273 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

keyword-velocity-tracker 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
keyword-velocity-tracker

关键词速度跟踪器 · 本页

3,635—接触敏感数据MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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.

平台分布

OpenClaw

85.92%
按下载量换算1,094

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills