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Agent skill

Correlating Threat Campaigns

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Correlates disparate security incidents, IOCs, and adversary behaviors across time and organizations to identify unified threat campaigns, attribute them to common threat actors, and extract shared…

Apache-2.0Auto-check passedSecurity

Install Correlating Threat Campaigns

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill correlating-threat-campaigns -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills correlating-threat-campaigns --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/correlating-threat-campaigns .claude/skills/correlating-threat-campaigns && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
correlating-threat-campaigns
GitHub stars
34k
Token cost
~1.8k tokens
SKILL.md length
684 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Correlates disparate security incidents, IOCs, and adversary behaviors across time and organizations to identify unified threat campaigns, attribute them to common threat actors, and extract shared…

  • Works in 5 steps: Collect and Normalize Events → Identify Correlation Pivot Points → Calculate Correlation Confidence → …
  • Multiple incidents exhibit overlapping indicators
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 2 more sections
  • Runs Python scripts from its folder

What it does

Correlating Threat Campaigns is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Correlates disparate security incidents, IOCs, and adversary behaviors across time and organizations to identify unified threat campaigns, attribute them to common threat actors, and extract shared indicators for improved detection. Use when multiple incidents exhibit overlapping indicators, when sector-wide attack campaigns require cross-organizational analysis, or when building campaign-level intelligence products. Activates for requests involving campaign analysis, incident clustering, cross-organizational IOC…

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).

It sits in Security, covering Security operations. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Multiple incidents exhibit overlapping indicators
  • Sector-wide attack campaigns require cross-organizational analysis
  • Building campaign-level intelligence products

Example prompts

  • “Use the correlating-threat-campaigns skill to correlate disparate security incidents, IOCs, and adversary behaviors across time and organizations to…”
  • “/correlating-threat-campaigns”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Collect and Normalize Events
  2. Identify Correlation Pivot Points
  3. Calculate Correlation Confidence
  4. Build Campaign Graph
  5. Produce Campaign Intelligence Report

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Correlating Threat Campaigns loads about 1.8k tokens when it runs, and up to ~2.4k if it reads all its reference files. Until then it costs about 147 tokens; SKILL.md has 684 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~147
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 684 words, ~1,818 tokens.

Download SKILL.mdSave it as .claude/skills/correlating-threat-campaigns/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
correlating-threat-campaigns
description
Correlates disparate security incidents, IOCs, and adversary behaviors across time and organizations to identify unified threat campaigns, attribute them to common threat actors, and extract shared indicators for improved detection. Use when multiple incidents exhibit overlapping indicators, when sector-wide attack campaigns require cross-organizational analysis, or when building campaign-level intelligence products. Activates for requests involving campaign analysis, incident clustering, cross-organizational IOC correlation, or MISP correlation engine.
domain
cybersecurity
subdomain
threat-intelligence
tags
campaign-analysis, correlation, MISP, ATT&CK, threat-actor, intrusion-set, clustering, CTI
version
1.0.0
author
team-cybersecurity
license
Apache-2.0
nist_csf
ID.RA-01, ID.RA-05, DE.CM-01, DE.AE-02
mitre_attack
T1566, T1071.001, T1587.001, T1583.001, T1588.002

Correlating Threat Campaigns

When to Use

Use this skill when:

  • Multiple unrelated-appearing incidents share IOCs (same C2 IP, same malware hash, similar TTPs)
  • An ISAC partner shares indicators from an incident that match your own historical events
  • Building a campaign report linking adversary activity over weeks or months to a single operation

Do not use this skill to force correlation based on weak signals — false campaign attribution misleads defenders and wastes resources on incorrect threat models.

Prerequisites

  • TIP or SIEM with historical indicator and event data (90+ days recommended)
  • MISP correlation engine enabled with event sharing configured
  • Graph analysis tool (Maltego, Neo4j, or OpenCTI) for relationship visualization
  • Reference to MITRE ATT&CK intrusion set and campaign objects for structuring output

Workflow

Step 1: Collect and Normalize Events

Gather all candidate events for correlation from:

  • Internal SIEM (raw events, alert history)
  • TIP (historical indicators and events)
  • ISAC sharing (partner-submitted events in MISP or TAXII)
  • Commercial intelligence (Recorded Future, Mandiant, CrowdStrike reports)

Normalize all events to STIX 2.1 schema with consistent timestamp (UTC), indicator types, and confidence scores. Ensure all indicators have source attribution and collection date.

Step 2: Identify Correlation Pivot Points

Apply systematic pivot analysis across four dimensions:

Infrastructure pivots:

  • Same IP address or /24 subnet across events
  • Same domain registrant email or WHOIS organization
  • Same ASN or hosting provider with same account fingerprint
  • Same SSL certificate fingerprint or serial number across C2 domains

Capability pivots:

  • Same malware hash or YARA signature match
  • Same C2 communication protocol (Cobalt Strike beacon config, Sliver implant parameters)
  • Same exploit code or weaponized document template
  • Same obfuscation method or packer fingerprint

Temporal pivots:

  • Events occurring within same time window (operational hours suggesting same timezone)
  • Sequential events with logical kill chain progression
  • Malware compilation timestamps clustering in same date range

Victimology pivots:

  • Same target sector (healthcare, energy, financial)
  • Same target geography
  • Same targeted technology (specific ERP vendor, VPN appliance brand)
Step 3: Calculate Correlation Confidence

Apply weighted scoring for campaign attribution:

python
def calculate_campaign_confidence(events: list) -> float:
    scores = []

    # Infrastructure overlap (highest weight — most discriminating)
    infra_overlap = count_shared_infra(events) / len(events)
    scores.append(infra_overlap * 40)

    # Capability overlap (high weight — TTPs are durable)
    capability_overlap = count_shared_ttps(events) / len(events)
    scores.append(capability_overlap * 35)

    # Temporal proximity (moderate weight)
    temporal_score = assess_temporal_clustering(events)
    scores.append(temporal_score * 15)

    # Victimology alignment (lower weight — many actors target same sector)
    victim_score = assess_victim_pattern(events)
    scores.append(victim_score * 10)

    total = sum(scores)
    if total >= 70: return "HIGH"
    elif total >= 45: return "MEDIUM"
    else: return "LOW"
Step 4: Build Campaign Graph

In OpenCTI or Maltego, construct campaign graph:

  • Campaign object (STIX) as central node
  • Intrusion Set → uses → Malware objects
  • Intrusion Set → uses → Infrastructure objects
  • Intrusion Set → targets → Identity objects (victim organizations/sectors)
  • Campaign → attributed-to → Threat Actor (if attribution achieved)
  • Indicators → indicates → Malware (linking technical observables to capabilities)

Label each relationship with evidence reference and confidence.

Show full SKILL.md (301 more words)Show less
Step 5: Produce Campaign Intelligence Report

Structure the campaign report:

  1. Campaign name: Assign descriptive codename based on targeting theme or tooling
  2. Timeline: First/last observed dates with activity phases
  3. Attribution: Suspected threat actor with confidence level
  4. Target profile: Industry verticals, geographies, organization sizes
  5. TTPs summary: ATT&CK Navigator heatmap for campaign-specific techniques
  6. Shared indicators: IOCs that span multiple incidents (highest confidence for blocking)
  7. Detection guidance: Sigma/YARA rules specific to this campaign

Key Concepts

TermDefinition
CampaignSTIX object representing a grouping of adversarial behaviors with common objectives over a defined time period
Intrusion SetSTIX object grouping related intrusion activity by common objectives, even when actor identity is uncertain
PivotUsing a single data point (IOC, infrastructure, TTP) to discover related events or adversary artifacts
ClusteringMachine learning or manual grouping of incidents based on feature similarity to identify campaign boundaries
False CorrelationIncorrect linking of unrelated incidents due to shared infrastructure (CDNs, shared hosting) or common tools

Tools & Systems

  • MISP Correlation Engine: Automatic correlation of events sharing attribute values across the MISP instance and federated instances
  • OpenCTI Graph: Interactive relationship graph for visualizing campaign linkages with STIX object types
  • Maltego: Link analysis for infrastructure and capability pivoting across multiple data sources
  • Neo4j: Graph database with Cypher queries for large-scale campaign correlation (millions of events)

Common Pitfalls

  • CDN/Shared hosting false positives: Cloudflare, AWS CloudFront, and bulletproof hosters serve multiple threat actors. Shared IP alone does not establish campaign linkage.
  • Common malware conflation: Multiple threat actors use Cobalt Strike. Shared capability does not indicate same actor without additional corroboration.
  • Premature attribution: Forcing campaign-to-actor attribution before evidence threshold is reached produces incorrect intelligence that persists in reports.
  • Missing temporal analysis: Events from different years may share infrastructure that was recycled by a different actor, not the same campaign.

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/correlating-threat-campaigns of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

Compare with similar skills

Correlating Threat Campaigns next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Correlating Threat Campaigns compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Correlating Threat Campaigns this skillmukul975/Anthropic-Cybersecurity-Skills34k—~1.8kAutomated safety check: PassApache-2.0
Security Alert Triageelastic/agent-skills5921 repos~3.5kAutomated safety check: NotesApache-2.0
Kubernetes Network Security Auditkubeshark/kubeshark12k—~7.3kAutomated safety check: NotesApache-2.0
Security Detection Rule Managementelastic/agent-skills5921 repos~3.9kAutomated safety check: NotesApache-2.0
Chaitin CLIchaitin/chaitin-cli115—~15kAutomated safety check: NotesGPL-3.0
GatesNebulock-Inc/agentic-threat-hunting-framework388—~12kAutomated safety check: PassMIT

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Categories

Questions about Correlating Threat Campaigns

What does Correlating Threat Campaigns do?

Correlates disparate security incidents, IOCs, and adversary behaviors across time and organizations to identify unified threat campaigns, attribute them to common threat actors, and extract shared…. Correlating Threat Campaigns is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Correlates disparate security incidents, IOCs, and adversary behaviors across time and organizations to identify unified threat campaigns, attribute them to common threat actors, and extract shared indicators for improved detection.

When should I use Correlating Threat Campaigns?

Correlating Threat Campaigns fits situations like: multiple incidents exhibit overlapping indicators; sector-wide attack campaigns require cross-organizational analysis; building campaign-level intelligence products.

How do I install Correlating Threat Campaigns in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill correlating-threat-campaigns -a claude-code`. Or copy the skill folder (skills/correlating-threat-campaigns in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/correlating-threat-campaigns in your project. Claude Code loads it when a task matches its description.

How do I install Correlating Threat Campaigns in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill correlating-threat-campaigns -a codex`. Or copy the skill folder (skills/correlating-threat-campaigns in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/correlating-threat-campaigns in your project. Codex loads it when a task matches its description.

Can I use Correlating Threat Campaigns in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill correlating-threat-campaigns -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/correlating-threat-campaigns, .gemini/skills/correlating-threat-campaigns, .github/skills/correlating-threat-campaigns and .opencode/skills/correlating-threat-campaigns in your project.

What does Correlating Threat Campaigns need to run?

Going by SKILL.md and its folder, Correlating Threat Campaigns needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Correlating Threat Campaigns access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Correlating Threat Campaigns safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Correlating Threat Campaigns use?

Correlating Threat Campaigns is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Correlating Threat Campaigns use?

About 1.8k tokens (SKILL.md is roughly 7.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 548 tokens, read only when the agent opens those files.

What are the alternatives to Correlating Threat Campaigns?

Skills that share tags, products or a category with Correlating Threat Campaigns: Security Alert Triage (elastic/agent-skills, 592 stars), Kubernetes Network Security Audit (kubeshark/kubeshark, 12k stars), Security Detection Rule Management (elastic/agent-skills, 592 stars) and Chaitin CLI (chaitin/chaitin-cli, 115 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Correlating Threat Campaigns?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 34,116 GitHub stars. The repository holds 644 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.