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Agent skill

Modeling Threats With Opencti

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Deploy OpenCTI (Filigran) via Docker Compose and use the pycti Python client to model threat actors, intrusion sets, campaigns, and indicators as a STIX 2.1 knowledge graph with relationships (uses…

Apache-2.0Auto-check: notesKnowledge Management

Install Modeling Threats With Opencti

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill modeling-threats-with-opencti -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills modeling-threats-with-opencti --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/modeling-threats-with-opencti .claude/skills/modeling-threats-with-opencti && 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
modeling-threats-with-opencti
GitHub stars
34k
Token cost
~2.8k tokens
SKILL.md length
832 words
Files
5 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Deploy OpenCTI (Filigran) via Docker Compose and use the pycti Python client to model threat actors, intrusion sets, campaigns, and indicators as a STIX 2.1 knowledge graph with relationships (uses…

  • Works in 8 steps: Deploy the OpenCTI platform → Authenticate with pycti → Create core STIX domain objects → …
  • Building a centralized threat-intel knowledge base
  • SKILL.md covers Overview, When to Use, Prerequisites and Objectives, plus 5 more sections
  • Runs Python scripts from its folder; calls docker, pip and git; reaches github.com; needs OPENCTI_ADMIN_TOKEN and OPENCTI_ADMIN_PASSWORD

What it does

Modeling Threats With Opencti is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Deploy OpenCTI (Filigran) via Docker Compose and use the pycti Python client to model threat actors, intrusion sets, campaigns, and indicators as a STIX 2.1 knowledge graph with relationships (uses, attributed-to, targets). Use when building a centralized threat-intel knowledge base, correlating IOCs from multiple feeds into one adversary graph, or producing STIX bundles for detection engineering.

Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/api-reference.md`, `references/standards.md` and `scripts/agent.py`).

It sits in Knowledge Management, covering Knowledge graphs, Security operations and Containers. It works with Python and Docker. 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

  • Building a centralized threat-intel knowledge base
  • Correlating IOCs from multiple feeds into one adversary graph
  • Producing STIX bundles for detection engineering

Example prompts

  • “/modeling-threats-with-opencti”

Requirements

  • Python 3
  • Docker
  • A credential in OPENCTI_ADMIN_TOKEN
  • A credential in YOUR_API_TOKEN

Workflow steps

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

  1. Deploy the OpenCTI platform
  2. Authenticate with pycti
  3. Create core STIX domain objects
  4. Build relationships to form the graph
  5. Add indicators and observables
  6. Submit a STIX 2.1 bundle directly
  7. Enable connectors for automated ingestion
  8. Query the graph and export an actor profile

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.

    Shell commands in SKILL.md call:

    • docker
    • pip
    • git
    • openssl

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com

    Also links to:

    • docs.opencti.io
    • oasis-open.github.io

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

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • OPENCTI_ADMIN_TOKEN
    • OPENCTI_ADMIN_PASSWORD
    • MINIO_ROOT_PASSWORD
    • OPENCTI_TOKEN
    • CONNECTOR_MITRE_TOKEN

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

Context cost

Modeling Threats With Opencti loads about 2.8k tokens when it runs, and up to ~3.9k if it reads all its reference files. Until then it costs about 108 tokens; SKILL.md has 832 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~108
When it runs · the whole SKILL.md, loaded when a task matches
~2.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.9k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:78
    cat > .env <<EOF
  • NoteRuns commands with sudoSKILL.md:93
    sudo sysctl -w vm.max_map_count=1048575
  • NoteMentions a .env fileSKILL.md:96
    log in with the admin credentials from `.env`.

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). 832 words, ~2,835 tokens.

Download SKILL.mdSave it as .claude/skills/modeling-threats-with-opencti/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
modeling-threats-with-opencti
description
Deploy OpenCTI (Filigran) via Docker Compose and use the pycti Python client to model threat actors, intrusion sets, campaigns, and indicators as a STIX 2.1 knowledge graph with relationships (uses, attributed-to, targets). Use when building a centralized threat-intel knowledge base, correlating IOCs from multiple feeds into one adversary graph, or producing STIX bundles for detection engineering.
domain
cybersecurity
subdomain
threat-intelligence
tags
opencti, threat-intelligence, stix2, pycti, knowledge-graph, threat-modeling, mitre-attack, cti
version
1.0
author
mahipal
license
Apache-2.0
nist_csf
ID.RA-03
mitre_attack
T1589

Modeling Threats with OpenCTI

Overview

OpenCTI (Open Cyber Threat Intelligence) is an open-source threat-intelligence platform developed by Filigran that lets analysts store, organize, visualize, and share structured cyber threat intelligence as a knowledge graph. Every object — Threat Actors, Intrusion Sets, Campaigns, Attack Patterns, Malware, Indicators, Observables, Vulnerabilities — is modeled on the STIX 2.1 standard, and the relationships between them (uses, attributed-to, targets, indicates) form a graph that reveals how adversaries operate end to end.

Architecturally, OpenCTI is built from a GraphQL API backed by Elasticsearch/OpenSearch and a graph database, a Redis stream, RabbitMQ message broker, import/export workers, and connectors. Connectors retrieve information from external sources (MITRE ATT&CK, MISP, AlienVault OTX, CISA, abuse.ch, etc.), convert it into STIX 2.1 bundles, and submit those bundles to the platform; workers then ingest the bundles into the graph. The official Python client, pycti (OpenCTIApiClient), is the programmatic interface analysts use to create entities, build relationships, and push STIX bundles.

This skill follows the official OpenCTI documentation (docs.opencti.io) and the OpenCTI-Platform/client-python (pycti) repository. It maps to MITRE ATT&CK T1589 (Gather Victim Identity Information) as part of the broader CTI analysis lifecycle — OpenCTI is where reconnaissance and adversary tradecraft observed across reporting is consolidated, deduplicated, and modeled so detection and response teams can act on it. The threat context is the volume and fragmentation of modern CTI: hundreds of vendor reports, IOC feeds, and ATT&CK updates that are useless until correlated into a single, queryable adversary picture.

When to Use

  • Building a centralized, STIX-native knowledge base of threat actors, campaigns, and TTPs
  • Correlating IOCs and reports from multiple feeds into a single adversary graph
  • Mapping observed activity to MITRE ATT&CK techniques for coverage and gap analysis
  • Producing structured intelligence (STIX bundles) for downstream detection engineering
  • Tracking attribution: which intrusion sets are attributed to which threat actors and campaigns
  • Automating CTI ingestion via connectors and the pycti API

Prerequisites

  • Docker and Docker Compose (OpenCTI is deployed as a container stack)
  • Python 3.8+ for the pycti client:
    bash
    pip install pycti stix2
  • An OpenCTI instance and an API token (Profile > API access in the UI)
  • Familiarity with the STIX 2.1 data model (SDOs, SROs, observables)
  • RabbitMQ, Redis, and Elasticsearch/OpenSearch reachable by the platform (handled by the reference compose)

Objectives

  • Deploy an OpenCTI platform with workers via Docker Compose
  • Authenticate to the GraphQL API with pycti using an API token
  • Create core STIX domain objects: Threat Actor, Intrusion Set, Campaign, Attack Pattern, Malware
  • Build relationships (uses, attributed-to, targets) to form the adversary graph
  • Enable connectors (MITRE ATT&CK, MISP) to auto-ingest intelligence
  • Submit STIX 2.1 bundles via send_stix2_bundle
  • Query the graph and export an actor's full TTP profile

MITRE ATT&CK Mapping

IDNameRelevance
T1589Gather Victim Identity InformationOpenCTI consolidates reconnaissance and victim/target intelligence observed across reporting into a structured, queryable knowledge graph that supports analysis of adversary targeting.

Workflow

Step 1: Deploy the OpenCTI platform

Use the official Docker Compose stack. Generate the required tokens/UUIDs and start the platform, workers, and dependencies.

bash
git clone https://github.com/OpenCTI-Platform/docker.git opencti-docker
cd opencti-docker

# Generate required secrets (UUID v4 for tokens, base64 for app secret)
cat > .env <<EOF
OPENCTI_ADMIN_EMAIL=admin@opencti.local
OPENCTI_ADMIN_PASSWORD=$(openssl rand -hex 16)
OPENCTI_ADMIN_TOKEN=$(cat /proc/sys/kernel/random/uuid)
OPENCTI_BASE_URL=http://localhost:8080
MINIO_ROOT_USER=$(cat /proc/sys/kernel/random/uuid)
MINIO_ROOT_PASSWORD=$(cat /proc/sys/kernel/random/uuid)
RABBITMQ_DEFAULT_USER=guest
RABBITMQ_DEFAULT_PASS=guest
ELASTIC_MEMORY_SIZE=4G
CONNECTOR_HISTORY_ID=$(cat /proc/sys/kernel/random/uuid)
CONNECTOR_EXPORT_FILE_STIX_ID=$(cat /proc/sys/kernel/random/uuid)
EOF

# Increase vm.max_map_count for Elasticsearch, then start the stack
sudo sysctl -w vm.max_map_count=1048575
docker compose up -d

Access the UI at http://localhost:8080 and log in with the admin credentials from .env.

Show full SKILL.md (329 more words)Show less
Step 2: Authenticate with pycti

Create an OpenCTIApiClient instance using your platform URL and API token.

python
from pycti import OpenCTIApiClient

opencti = OpenCTIApiClient(
    "http://localhost:8080",
    "YOUR_API_TOKEN",  # from Profile > API access, or OPENCTI_ADMIN_TOKEN
)
Step 3: Create core STIX domain objects

Create a Threat Actor, an Intrusion Set, a Campaign, and an Attack Pattern. pycti create() calls act as upserts when update=True.

python
# Threat Actor (group)
actor = opencti.threat_actor_group.create(
    name="APT-EXAMPLE",
    description="Financially motivated intrusion group tracked in this case.",
    threat_actor_types=["crime-syndicate"],
)

# Intrusion Set
intrusion_set = opencti.intrusion_set.create(
    name="EXAMPLE-SET",
    description="Cluster of activity sharing infrastructure and TTPs.",
)

# Campaign
campaign = opencti.campaign.create(
    name="Operation Example 2026",
    description="Spearphishing campaign targeting the finance sector.",
)

# Attack Pattern linked to MITRE ATT&CK (x_mitre_id maps to the technique)
technique = opencti.attack_pattern.create(
    name="Spearphishing Attachment",
    x_mitre_id="T1566.001",
)
Step 4: Build relationships to form the graph

Connect the objects with STIX relationships so the graph reflects how the adversary operates.

python
# Intrusion set attributed to the threat actor
opencti.stix_core_relationship.create(
    fromId=intrusion_set["id"],
    toId=actor["id"],
    relationship_type="attributed-to",
)

# Campaign attributed to the intrusion set
opencti.stix_core_relationship.create(
    fromId=campaign["id"],
    toId=intrusion_set["id"],
    relationship_type="attributed-to",
)

# Intrusion set uses the technique
opencti.stix_core_relationship.create(
    fromId=intrusion_set["id"],
    toId=technique["id"],
    relationship_type="uses",
)
Step 5: Add indicators and observables

Create an indicator with a STIX pattern and tie it to the intrusion set via an indicates relationship.

python
from dateutil.parser import parse

date = parse("2026-06-01").strftime("%Y-%m-%dT%H:%M:%SZ")

indicator = opencti.indicator.create(
    name="C2 domain for Operation Example",
    pattern_type="stix",
    pattern="[domain-name:value = 'malicious-c2.example']",
    x_opencti_main_observable_type="Domain-Name",
    valid_from=date,
)

opencti.stix_core_relationship.create(
    fromId=indicator["id"],
    toId=intrusion_set["id"],
    relationship_type="indicates",
)
Step 6: Submit a STIX 2.1 bundle directly

For bulk ingestion, build a STIX bundle and submit it with send_stix2_bundle — the recommended bulk-ingest path.

python
import json

with open("threat_report_bundle.json") as f:
    bundle = json.load(f)

opencti.stix2.import_bundle_from_json(
    json.dumps(bundle),
    update=True,
)
Step 7: Enable connectors for automated ingestion

Add connectors to the compose stack so external intelligence (MITRE ATT&CK, MISP) is ingested continuously. Each connector needs its own token.

yaml
# Append to docker-compose.yml under services:
  connector-mitre:
    image: opencti/connector-mitre:latest
    environment:
      - OPENCTI_URL=http://opencti:8080
      - OPENCTI_TOKEN=${CONNECTOR_MITRE_TOKEN}
      - CONNECTOR_ID=${CONNECTOR_MITRE_ID}
      - CONNECTOR_TYPE=EXTERNAL_IMPORT
      - CONNECTOR_NAME=MITRE ATT&CK
      - CONNECTOR_SCOPE=tool,report,malware,identity,attack-pattern,intrusion-set,campaign
      - MITRE_INTERVAL=7   # days
    restart: always
bash
docker compose up -d connector-mitre
Step 8: Query the graph and export an actor profile

Read back the adversary's full picture for reporting and detection engineering.

python
# Resolve all techniques an intrusion set uses
iset = opencti.intrusion_set.read(filters={
    "mode": "and",
    "filters": [{"key": "name", "values": ["EXAMPLE-SET"]}],
    "filterGroups": [],
})

rels = opencti.stix_core_relationship.list(
    fromId=iset["id"],
    relationship_type="uses",
)
for r in rels:
    print(r["to"]["name"], r["to"].get("x_mitre_id"))

Tools and Resources

ToolPurposeSource
OpenCTI PlatformSTIX 2.1 threat-intel knowledge graphhttps://github.com/OpenCTI-Platform/opencti
OpenCTI DockerReference compose stackhttps://github.com/OpenCTI-Platform/docker
pyctiOfficial Python client for the GraphQL APIhttps://github.com/OpenCTI-Platform/client-python
OpenCTI ConnectorsImporters (MITRE, MISP, OTX, CISA, abuse.ch)https://github.com/OpenCTI-Platform/connectors
OpenCTI docsOfficial documentationhttps://docs.opencti.io/latest/
STIX 2.1 specUnderlying data modelhttps://oasis-open.github.io/cti-documentation/

STIX Object Cheat Sheet (pycti entities)

pycti entitySTIX typeUse
threat_actor_groupthreat-actorNamed adversary group
intrusion_setintrusion-setClustered activity / tracked set
campaigncampaignTime-bounded operation
attack_patternattack-patternMITRE ATT&CK technique
malwaremalwareTooling/implant
indicatorindicatorDetection pattern (STIX/Sigma/YARA)
vulnerabilityvulnerabilityCVE
stix_core_relationshiprelationshipuses, attributed-to, targets, indicates

Validation Criteria

  • OpenCTI platform and workers deployed and reachable at the base URL
  • pycti authenticates with a valid API token
  • Threat Actor, Intrusion Set, Campaign, and Attack Pattern objects created
  • Relationships (attributed-to, uses, indicates) built between objects
  • At least one indicator created and linked to an intrusion set
  • A STIX 2.1 bundle ingested via import_bundle_from_json
  • MITRE ATT&CK connector enabled and importing techniques
  • Intrusion-set TTP profile queryable and exportable

© 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 4 other files (scripts, references) in skills/modeling-threats-with-opencti of mukul975/Anthropic-Cybersecurity-Skills.

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

Open the folder on GitHubat commit 54a7988

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Works with

Categories

Questions about Modeling Threats With Opencti

What does Modeling Threats With Opencti do?

Deploy OpenCTI (Filigran) via Docker Compose and use the pycti Python client to model threat actors, intrusion sets, campaigns, and indicators as a STIX 2.1 knowledge graph with relationships (uses…. Modeling Threats With Opencti is an agent skill from mukul975/Anthropic-Cybersecurity-Skills.1 knowledge graph with relationships (uses, attributed-to, targets).

When should I use Modeling Threats With Opencti?

Modeling Threats With Opencti fits situations like: building a centralized threat-intel knowledge base; correlating IOCs from multiple feeds into one adversary graph; producing STIX bundles for detection engineering.

How do I install Modeling Threats With Opencti in Claude Code?

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

How do I install Modeling Threats With Opencti in Codex?

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

Can I use Modeling Threats With Opencti 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 modeling-threats-with-opencti -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/modeling-threats-with-opencti, .gemini/skills/modeling-threats-with-opencti, .github/skills/modeling-threats-with-opencti and .opencode/skills/modeling-threats-with-opencti in your project.

What does Modeling Threats With Opencti need to run?

Going by SKILL.md and its folder, Modeling Threats With Opencti needs Python for the scripts in its folder, the command-line tools its instructions call (docker, pip, git and openssl) and credentials named OPENCTI_ADMIN_TOKEN, OPENCTI_ADMIN_PASSWORD, MINIO_ROOT_PASSWORD and OPENCTI_TOKEN. Our summary lists: Python 3; Docker; A credential in OPENCTI_ADMIN_TOKEN; A credential in YOUR_API_TOKEN.

Does Modeling Threats With Opencti access the network?

SKILL.md names 3 domains. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. As links in the text: docs.opencti.io and oasis-open.github.io. This is read from the text; nothing was executed.

Is Modeling Threats With Opencti safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file; runs commands with sudo), nothing it rates as a warning. 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 Modeling Threats With Opencti use?

Modeling Threats With Opencti 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 Modeling Threats With Opencti use?

About 2.8k tokens (SKILL.md is roughly 11k 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 1.1k tokens, read only when the agent opens those files.

What are the alternatives to Modeling Threats With Opencti?

Skills that share tags, products or a category with Modeling Threats With Opencti: LLM Wiki Knowledge Graph (Egonex-AI/Understand-Anything, 86k stars), Alibabacloud Ecs Sec Userspace (aliyun/alibabacloud-ecs-troubleshoot-skills, 148 stars), Mini Context Graph (github/awesome-copilot, 40k stars) and Sca Trivy (AgentSecOps/SecOpsAgentKit, 220 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Modeling Threats With Opencti?

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.