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Agent Skill · 研究检索

threat-intelligence(威胁情报)

threat-intelligence 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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。

threat-intelligence 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,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 45,481。

总安装 847周安装 36GitHub Stars 4下载 297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:threat-intelligence(威胁情报)
来源仓库:https://github.com/sherifeldeeb/agentskills
仓库路径:skills/threat-intelligence
安装命令:
npx skills add https://github.com/sherifeldeeb/agentskills --skill threat-intelligence
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/sherifeldeeb/agentskills --skill threat-intelligence

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。

用于查找、检索和筛选网络安全威胁情报源与分析工具。适合获取 IOC(Indicator of Compromise)数据库、漏洞通告等资源。可按地理区域、攻击组织等维度定制情报订阅,强化预警机制。

安装命令:npx skills add https://github.com/sherifeldeeb/agentskills --skill threat-intelligence。注意情报时效性与可信度验证,避免误判风险。

来源仓库内路径:skills/threat-intelligence

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/sherifeldeeb/agentskills --skill threat-intell…;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

threat-intelligence 怎么安装?

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

threat-intelligence 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。

threat-intelligence 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。

threat-intelligence 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。

threat-intelligence 用的人多吗?

公开数据是累计安装 847 次,近一周安装 36 次,下载 297 次,GitHub 4 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。

threat-intelligence 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。

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。
  • 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。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

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。

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
threat-intelligence

威胁情报 · 本页

8474接触敏感数据未明确通过

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

Threat Intelligence Skill

Gather, analyze, and disseminate cyber threat intelligence with IOC extraction, threat actor profiling, and MITRE ATT&CK mapping.

Capabilities

  • IOC Extraction: Extract indicators from text, logs, and reports
  • IOC Management: Deduplicate, validate, and enrich indicators
  • Threat Profiling: Document threat actors and campaigns
  • ATT&CK Mapping: Map threats to MITRE ATT&CK framework
  • Intelligence Reports: Generate threat bulletins and assessments
  • Feed Processing: Parse and normalize threat feeds

Quick Start

from cti_utils import IOCExtractor, ThreatActor, IntelReport

# Extract IOCs from text
extractor = IOCExtractor()
iocs = extractor.extract_from_text('''
Malware connects to 192.168.1.100 and evil.com.
Hash: d41d8cd98f00b204e9800998ecf8427e
''')
print(iocs)

# Document threat actor
actor = ThreatActor('APT29', aliases=['Cozy Bear', 'The Dukes'])
actor.add_ttp('T1566', 'Phishing')
actor.set_motivation('espionage')

# Generate intel report
report = IntelReport('Emerging Ransomware Campaign')
report.add_ioc('ip', '10.0.0.1', 'C2 server')
print(report.generate())

Usage

IOC Extraction

Extract indicators of compromise from various text sources.

Example:

from cti_utils import IOCExtractor

extractor = IOCExtractor()

# Extract from text
text = '''
The malware was downloaded from hxxp://malware[.]evil[.]com/payload.exe
It connects to C2 server at 192.168.100.50 on port 443.
The file hash is: a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4
Email originated from attacker@phishing.com
'''

iocs = extractor.extract_from_text(text)

print(f"IPs: {iocs['ip']}")
print(f"Domains: {iocs['domain']}")
print(f"URLs: {iocs['url']}")
print(f"Hashes: {iocs['hash']}")
print(f"Emails: {iocs['email']}")

# Defang/refang IOCs
defanged = extractor.defang('http://evil.com')  # hxxp://evil[.]com
refanged = extractor.refang('hxxp://evil[.]com')  # http://evil.com

# Validate IOCs
valid = extractor.validate_ioc('ip', '192.168.1.1')  # True
invalid = extractor.validate_ioc('ip', '999.999.999.999')  # False

IOC Management

Manage collections of indicators with context.

Example:

from cti_utils import IOCCollection

collection = IOCCollection('Campaign-2024-001')

# Add IOCs with context
collection.add_ioc(
    ioc_type='ip',
    value='192.168.1.100',
    context='C2 server',
    confidence='high',
    source='Sandbox analysis'
)

collection.add_ioc(
    ioc_type='domain',
    value='malware.evil.com',
    context='Payload delivery',
    confidence='medium',
    source='Network logs'
)

collection.add_ioc(
    ioc_type='hash',
    value='a1b2c3d4e5f6...',
    context='Ransomware executable',
    confidence='high',
    source='EDR'
)

# Deduplicate
collection.deduplicate()

# Export formats
print(collection.to_csv())
print(collection.to_json())
print(collection.to_stix())  # STIX 2.1 format

Threat Actor Profiling

Document threat actors and their characteristics.

Example:

from cti_utils import ThreatActor

actor = ThreatActor(
    name='APT29',
    aliases=['Cozy Bear', 'The Dukes', 'YTTRIUM']
)

# Set attributes
actor.set_motivation('espionage')
actor.set_sophistication('advanced')
actor.set_origin('Russia')

# Add TTPs (MITRE ATT&CK)
actor.add_ttp('T1566.001', 'Spearphishing Attachment')
actor.add_ttp('T1059.001', 'PowerShell')
actor.add_ttp('T1071.001', 'Web Protocols')
actor.add_ttp('T1486', 'Data Encrypted for Impact')

# Add targeting
actor.add_target_sector('Government')
actor.add_target_sector('Healthcare')
actor.add_target_region('North America')
actor.add_target_region('Europe')

# Add tools
actor.add_tool('Cobalt Strike')
actor.add_tool('Mimikatz')

# Add infrastructure
actor.add_infrastructure('ip', '192.168.1.100', 'C2 server')
actor.add_infrastructure('domain', 'actor-c2.com', 'Primary C2')

# Generate profile
print(actor.generate_profile())

Campaign Tracking

Track threat campaigns over time.

Example:

from cti_utils import Campaign

campaign = Campaign(
    name='Operation DarkSide',
    first_seen='2024-01-01',
    threat_actor='APT29'
)

# Add campaign details
campaign.set_description('''
Targeted campaign against financial institutions using
spearphishing emails with malicious Excel attachments.
''')

campaign.set_objective('Financial theft and espionage')

# Add IOCs
campaign.add_ioc('domain', 'campaign-c2.evil.com')
campaign.add_ioc('hash', 'abc123...', 'Excel dropper')

# Add TTPs
campaign.add_ttp('T1566.001', 'Initial access via phishing')
campaign.add_ttp('T1059.005', 'VBA macro execution')

# Add targets
campaign.add_target('Financial Services', 'North America')

# Timeline events
campaign.add_event('2024-01-01', 'First phishing emails observed')
campaign.add_event('2024-01-05', 'New C2 infrastructure identified')
campaign.add_event('2024-01-10', 'Malware variant updated')

# Generate report
print(campaign.generate_report())

MITRE ATT&CK Mapping

Map threats to the ATT&CK framework.

Example:

from cti_utils import ATTACKMapper

mapper = ATTACKMapper()

# Map techniques
mapper.add_technique('T1566.001', 'Spearphishing used for initial access')
mapper.add_technique('T1059.001', 'PowerShell scripts executed')
mapper.add_technique('T1055', 'Process injection observed')
mapper.add_technique('T1486', 'Files encrypted with ransomware')

# Generate matrix view
print(mapper.generate_matrix())

# Get technique details
print(mapper.get_technique_info('T1566.001'))

# Export for ATT&CK Navigator
mapper.export_navigator('attack_layer.json')

Intelligence Reports

Generate threat intelligence reports.

Example:

from cti_utils import IntelReport

report = IntelReport(
    title='Emerging Ransomware Campaign Targeting Healthcare',
    classification='TLP:AMBER'
)

# Executive summary
report.set_summary('''
A new ransomware campaign has been identified targeting healthcare
organizations in North America. The campaign uses phishing emails
with malicious attachments to gain initial access.
''')

# Key findings
report.add_finding('New ransomware variant identified: "MedLocker"')
report.add_finding('Campaign active since January 2024')
report.add_finding('At least 5 healthcare organizations targeted')

# Add IOCs
report.add_ioc('hash', 'abc123...', 'Ransomware executable')
report.add_ioc('domain', 'medlocker-payment.onion', 'Payment portal')
report.add_ioc('ip', '192.168.1.100', 'C2 server')

# Add TTPs
report.add_ttp('T1566.001', 'Phishing with malicious attachments')
report.add_ttp('T1486', 'Data encryption')

# Recommendations
report.add_recommendation('Block IOCs at perimeter')
report.add_recommendation('Update endpoint detection signatures')
report.add_recommendation('Conduct phishing awareness training')

# Generate outputs
print(report.generate())
print(report.generate_executive_brief())

Configuration

Environment Variables

VariableDescriptionRequiredDefault
CTI_FEED_API_KEYAPI key for threat feedsNoNone
CTI_OUTPUT_DIROutput directory for reportsNo./output

Supported IOC Types

  • ip - IPv4 and IPv6 addresses
  • domain - Domain names
  • url - Full URLs
  • hash - MD5, SHA1, SHA256 hashes
  • email - Email addresses
  • cve - CVE identifiers

Limitations

  • No Live Feeds: Feed fetching requires manual configuration
  • Offline ATT&CK: Uses embedded technique data
  • No Enrichment APIs: External enrichment not included

Troubleshooting

Invalid IOC Format

IOC validation uses standard regex patterns:

# Valid
extractor.validate_ioc('ip', '192.168.1.1')  # True

# Invalid
extractor.validate_ioc('ip', '192.168.1.256')  # False

Defanging Issues

Use consistent defanging format:

# Standard defanging
extractor.defang('http://evil.com')
# Returns: hxxp://evil[.]com

Related Skills

References

平台分布

Codex

36.38%
按下载量换算108

Claude

28.69%
按下载量换算85

Cursor

18.91%
按下载量换算56

Gemini CLI

8.15%
按下载量换算24

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

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。

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