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Agent Skill · 开发

performing-cve-prioritization-with-kev-catalog(使用 kev 目录执行 CVE 优先级排序)

performing-cve-prioritization-with-kev-catalog 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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。

performing-cve-prioritization-with-kev-catalog 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 19,191。

总安装 288周安装 12GitHub Stars 5,934下载 96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
unknown

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:performing-cve-prioritization-with-kev-catalog(使用 kev 目录执行 CVE 优先级排序)
来源仓库:https://github.com/mukul975/anthropic-cybersecurity-skills
仓库路径:skills/performing-cve-prioritization-with-kev-catalog
安装命令:
npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill performing-cve-prioritization-with-kev-catalog
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill performing-cve-prioritization-with-kev-catalog

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

用于处理 GitHub 仓库、Issue 和 Pull Request 协作信息。适合在 Codex、Claude、Cursor、Gemini CLI 中整理代码变更或协作事项。可结合来源仓库 README 继续核验具体用法,建议确认维护状态。安装前需注意是否会触发联网、命令执行或文件读写操作。

performing-cve-prioritization-with-kev-catalog 属于开发类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/performing-cve-prioritization-with-kev-catalog

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skil…;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

    它需要需要联网,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

performing-cve-prioritization-with-kev-catalog 怎么安装?

在终端执行 npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill performi…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

performing-cve-prioritization-with-kev-catalog 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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。

performing-cve-prioritization-with-kev-catalog 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

performing-cve-prioritization-with-kev-catalog 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

performing-cve-prioritization-with-kev-catalog 用的人多吗?

公开数据是累计安装 288 次,近一周安装 12 次,下载 96 次,GitHub 5,934 星。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。

performing-cve-prioritization-with-kev-catalog 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

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 5,934 星,维护活跃度相对可查。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
performing-cve-prioritization-with-kev-catalog

使用 kev 目录执行 CVE 优先级排序 · 本页

2885,934需要联网未明确通过

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

Performing CVE Prioritization with KEV Catalog

Overview

The CISA Known Exploited Vulnerabilities (KEV) catalog, established through Binding Operational Directive (BOD) 22-01, is a living list of CVEs that have been actively exploited in the wild and carry significant risk. As of early 2026, the catalog contains over 1,484 entries, growing 20% in 2025 alone with 245 new additions. This skill covers integrating the KEV catalog into vulnerability prioritization workflows alongside EPSS (Exploit Prediction Scoring System) and CVSS to create a risk-based approach that prioritizes vulnerabilities with confirmed exploitation activity over theoretical severity alone.

When to Use

  • When conducting security assessments that involve performing cve prioritization with kev catalog
  • When following incident response procedures for related security events
  • When performing scheduled security testing or auditing activities
  • When validating security controls through hands-on testing

Prerequisites

  • Access to vulnerability scan results (Qualys, Nessus, Rapid7, etc.)
  • Familiarity with CVE identifiers and NVD
  • Understanding of CVSS scoring (v3.1 and v4.0)
  • API access to CISA KEV, EPSS, and NVD endpoints
  • Python 3.8+ with requests and pandas libraries

Core Concepts

CISA KEV Catalog Structure

Each KEV entry contains:

  • CVE ID: The CVE identifier (e.g., CVE-2024-3094)
  • Vendor/Project: Affected vendor and product name
  • Vulnerability Name: Short description of the vulnerability
  • Date Added: When CISA added it to the catalog
  • Short Description: Brief technical description
  • Required Action: Recommended remediation action
  • Due Date: Deadline for federal agencies (FCEB) to remediate
  • Known Ransomware Campaign Use: Whether ransomware groups exploit it

BOD 22-01 Remediation Timelines

CVE Publication DateRemediation Deadline
2021 or later2 weeks from KEV listing
Before 20216 months from KEV listing

Multi-Factor Prioritization Model

FactorWeightData SourceRationale
CISA KEV Listed30%CISA KEV JSON feedConfirmed active exploitation
EPSS Score25%FIRST EPSS APIPredicted exploitation probability
CVSS Base Score20%NVD API v2.0Intrinsic vulnerability severity
Asset Criticality15%CMDB/Asset inventoryBusiness impact context
Network Exposure10%Network architectureAttack surface accessibility

KEV + EPSS Decision Matrix

KEV ListedEPSS > 0.5CVSS >= 9.0PrioritySLA
YesAnyAnyP1-Emergency48 hours
NoYesYesP1-Emergency48 hours
NoYesNoP2-Critical7 days
NoNoYesP2-Critical7 days
NoNoNo (>= 7.0)P3-High14 days
NoNoNo (>= 4.0)P4-Medium30 days
NoNoNo (< 4.0)P5-Low90 days

Workflow

Step 1: Fetch and Parse the KEV Catalog

import requests
import json
from datetime import datetime

KEV_URL = "https://www.cisa.gov/sites/default/files/feeds/known_exploited_vulnerabilities.json"

def fetch_kev_catalog():
    """Download and parse the CISA KEV catalog."""
    response = requests.get(KEV_URL, timeout=30)
    response.raise_for_status()
    data = response.json()

    catalog = {}
    for vuln in data.get("vulnerabilities", []):
        cve_id = vuln["cveID"]
        catalog[cve_id] = {
            "vendor": vuln.get("vendorProject", ""),
            "product": vuln.get("product", ""),
            "name": vuln.get("vulnerabilityName", ""),
            "date_added": vuln.get("dateAdded", ""),
            "description": vuln.get("shortDescription", ""),
            "action": vuln.get("requiredAction", ""),
            "due_date": vuln.get("dueDate", ""),
            "ransomware_use": vuln.get("knownRansomwareCampaignUse", "Unknown"),
        }

    print(f"[+] Loaded {len(catalog)} CVEs from CISA KEV catalog")
    print(f"    Catalog version: {data.get('catalogVersion', 'N/A')}")
    print(f"    Last updated: {data.get('dateReleased', 'N/A')}")
    return catalog

kev = fetch_kev_catalog()

Step 2: Enrich with EPSS Scores

EPSS_API = "https://api.first.org/data/v1/epss"

def get_epss_scores(cve_list):
    """Fetch EPSS scores for a batch of CVEs."""
    scores = {}
    batch_size = 100
    for i in range(0, len(cve_list), batch_size):
        batch = cve_list[i:i + batch_size]
        cve_param = ",".join(batch)
        response = requests.get(EPSS_API, params={"cve": cve_param}, timeout=30)
        if response.status_code == 200:
            for entry in response.json().get("data", []):
                scores[entry["cve"]] = {
                    "epss": float(entry.get("epss", 0)),
                    "percentile": float(entry.get("percentile", 0)),
                }
    return scores

Step 3: Build the Prioritization Engine

import pandas as pd

def prioritize_vulnerabilities(scan_results, kev_catalog, epss_scores):
    """Apply multi-factor prioritization to scan results."""
    prioritized = []

    for vuln in scan_results:
        cve_id = vuln.get("cve_id", "")
        cvss_score = float(vuln.get("cvss_score", 0))
        asset_criticality = float(vuln.get("asset_criticality", 3))
        exposure = float(vuln.get("network_exposure", 3))

        in_kev = cve_id in kev_catalog
        kev_data = kev_catalog.get(cve_id, {})
        epss_data = epss_scores.get(cve_id, {"epss": 0, "percentile": 0})
        epss_score = epss_data["epss"]

        # Composite risk score calculation
        risk_score = (
            (1.0 if in_kev else 0.0) * 10 * 0.30 +
            epss_score * 10 * 0.25 +
            cvss_score * 0.20 +
            (asset_criticality / 5.0) * 10 * 0.15 +
            (exposure / 5.0) * 10 * 0.10
        )

        # Assign priority level
        if in_kev or (epss_score > 0.5 and cvss_score >= 9.0):
            priority = "P1-Emergency"
            sla_days = 2
        elif epss_score > 0.5 or cvss_score >= 9.0:
            priority = "P2-Critical"
            sla_days = 7
        elif cvss_score >= 7.0:
            priority = "P3-High"
            sla_days = 14
        elif cvss_score >= 4.0:
            priority = "P4-Medium"
            sla_days = 30
        else:
            priority = "P5-Low"
            sla_days = 90

        prioritized.append({
            "cve_id": cve_id,
            "cvss_score": cvss_score,
            "epss_score": round(epss_score, 4),
            "epss_percentile": round(epss_data["percentile"], 4),
            "in_cisa_kev": in_kev,
            "ransomware_use": kev_data.get("ransomware_use", "N/A"),
            "kev_due_date": kev_data.get("due_date", "N/A"),
            "risk_score": round(risk_score, 2),
            "priority": priority,
            "sla_days": sla_days,
            "asset": vuln.get("asset", ""),
            "asset_criticality": asset_criticality,
        })

    df = pd.DataFrame(prioritized)
    df = df.sort_values("risk_score", ascending=False)
    return df

Step 4: Generate Prioritization Report

def generate_report(df, output_file="kev_prioritized_report.csv"):
    """Generate summary report from prioritized vulnerabilities."""
    print("\n" + "=" * 70)
    print("VULNERABILITY PRIORITIZATION REPORT - KEV + EPSS + CVSS")
    print("=" * 70)

    print(f"\nTotal vulnerabilities analyzed: {len(df)}")
    print(f"KEV-listed vulnerabilities:    {df['in_cisa_kev'].sum()}")
    print(f"Ransomware-associated:         {(df['ransomware_use'] == 'Known').sum()}")

    print("\nPriority Distribution:")
    print(df["priority"].value_counts().to_string())

    print("\nTop 15 Highest Risk Vulnerabilities:")
    top = df.head(15)[["cve_id", "cvss_score", "epss_score", "in_cisa_kev",
                        "risk_score", "priority"]]
    print(top.to_string(index=False))

    df.to_csv(output_file, index=False)
    print(f"\n[+] Full report saved to: {output_file}")

Best Practices

  1. Update the KEV catalog daily since CISA adds new entries multiple times per week
  2. Always cross-reference KEV with EPSS; a CVE may have high EPSS but not yet be in KEV
  3. Treat all KEV-listed CVEs as P1-Emergency regardless of CVSS score
  4. Pay special attention to KEV entries flagged with "Known Ransomware Campaign Use"
  5. Automate KEV comparison against your vulnerability scan results in CI/CD pipelines
  6. Track KEV due dates separately for FCEB compliance requirements
  7. Use KEV as a leading indicator for threat hunting; if a CVE is added, check for prior exploitation in your environment

Common Pitfalls

  • Relying solely on CVSS scores without checking KEV or EPSS data
  • Not updating the KEV catalog frequently enough (CISA updates multiple times weekly)
  • Treating non-KEV CVEs as safe; they may be exploited but not yet cataloged
  • Ignoring the "ransomware use" field which indicates highest-urgency threats
  • Using KEV only for compliance instead of integrating into overall risk management

Related Skills

  • prioritizing-vulnerabilities-with-cvss-scoring
  • building-vulnerability-data-pipeline-with-api
  • implementing-threat-intelligence-scoring
  • implementing-vulnerability-remediation-sla

平台分布

Codex

34.11%
按下载量换算33

Claude

33.34%
按下载量换算32

Cursor

19.89%
按下载量换算19

Gemini CLI

9.03%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。

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