Agent skill

Analyzing Apt Group With Mitre Navigator

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Query ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups' TTPs for detection-gap…

Apache-2.0Auto-check passedSecurity

Install Analyzing Apt Group With Mitre Navigator

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-apt-group-with-mitre-navigator -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-apt-group-with-mitre-navigator --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/analyzing-apt-group-with-mitre-navigator .claude/skills/analyzing-apt-group-with-mitre-navigator && 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
analyzing-apt-group-with-mitre-navigator
GitHub stars
34k
Token cost
~2.9k tokens
SKILL.md length
430 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Query ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups' TTPs for detection-gap…

  • Works in 5 steps: Query ATT&CK Data for APT Group → Generate Navigator Layer JSON → Compare Multiple APT Groups → …
  • Compare threat-actor technique coverage
  • SKILL.md covers Overview, When to Use, Prerequisites and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder

What it does

Analyzing Apt Group With Mitre Navigator is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Query ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups' TTPs for detection-gap analysis. Use to compare threat-actor technique coverage, find gaps in detection engineering, or produce Navigator visualizations for threat-intel reporting.

Its SKILL.md is about 2.9k 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. It works with Python. 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

  • Compare threat-actor technique coverage
  • Find gaps in detection engineering
  • Produce Navigator visualizations for threat-intel reporting

Example prompts

  • “/analyzing-apt-group-with-mitre-navigator”

Requirements

  • Python 3

Workflow steps

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

  1. Query ATT&CK Data for APT Group
  2. Generate Navigator Layer JSON
  3. Compare Multiple APT Groups
  4. Detection Gap Analysis with Layer Overlay
  5. Tactic Breakdown Analysis

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

    Links to these hosts (documentation or services it may open):

    • mitre-attack.github.io
    • github.com
    • attack.mitre.org
    • cisa.gov
    • picussecurity.com

    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

Analyzing Apt Group With Mitre Navigator loads about 2.9k tokens when it runs, and up to ~3.5k if it reads all its reference files. Until then it costs about 98 tokens; SKILL.md has 430 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~98
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.5k

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). 430 words, ~2,863 tokens.

Download SKILL.mdSave it as .claude/skills/analyzing-apt-group-with-mitre-navigator/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
analyzing-apt-group-with-mitre-navigator
description
Query ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups' TTPs for detection-gap analysis. Use to compare threat-actor technique coverage, find gaps in detection engineering, or produce Navigator visualizations for threat-intel reporting.
domain
cybersecurity
subdomain
threat-intelligence
tags
mitre-attack, navigator, apt, threat-actor, ttp-analysis, heatmap, detection-gap, threat-intelligence
version
1.0
author
mahipal
license
Apache-2.0
d3fend_techniques
Executable Denylisting, Execution Isolation, File Metadata Consistency Validation, Content Format Conversion, File Content Analysis
nist_csf
ID.RA-01, ID.RA-05, DE.CM-01, DE.AE-02
mitre_attack
T1059.001, T1071.001, T1003.001, T1486, T1547.001

Analyzing APT Group with MITRE ATT&CK Navigator

Overview

MITRE ATT&CK Navigator is a web-based tool for annotating and exploring ATT&CK matrices, enabling analysts to visualize threat actor technique coverage, compare multiple APT groups, identify detection gaps, and build threat-informed defense strategies. This skill covers querying ATT&CK data programmatically, mapping APT group TTPs to Navigator layers, creating multi-layer overlays for gap analysis, and generating actionable intelligence reports for detection engineering teams.

When to Use

  • When investigating security incidents that require analyzing apt group with mitre navigator
  • When building detection rules or threat hunting queries for this domain
  • When SOC analysts need structured procedures for this analysis type
  • When validating security monitoring coverage for related attack techniques

Prerequisites

  • Python 3.9+ with attackcti, mitreattack-python, stix2, requests libraries
  • ATT&CK Navigator (https://mitre-attack.github.io/attack-navigator/) or local deployment
  • Understanding of ATT&CK Enterprise matrix: 14 Tactics, 200+ Techniques, Sub-techniques
  • Access to threat intelligence reports or MISP/OpenCTI for threat actor data
  • Familiarity with STIX 2.1 Intrusion Set and Attack Pattern objects

Key Concepts

ATT&CK Navigator Layers

Navigator layers are JSON files that annotate ATT&CK techniques with scores, colors, comments, and metadata. Each layer can represent a single APT group's technique usage, a detection capability map, or a combined overlay. Layer version 4.5 supports enterprise-attack, mobile-attack, and ics-attack domains with filtering by platform (Windows, Linux, macOS, Cloud, Azure AD, Office 365, SaaS).

APT Group Profiles in ATT&CK

ATT&CK catalogs over 140 threat groups with documented technique usage. Each group profile includes aliases, targeted sectors, associated campaigns, software used, and technique mappings with procedure-level detail. Groups are identified by G-codes (e.g., G0016 for APT29, G0007 for APT28, G0032 for Lazarus Group).

Show full SKILL.md (161 more words)Show less
Multi-Layer Analysis

The Navigator supports loading multiple layers simultaneously, allowing analysts to overlay threat actor TTPs against detection coverage to identify gaps, compare multiple APT groups to find common techniques worth prioritizing, and track technique coverage changes over time.

Workflow

Step 1: Query ATT&CK Data for APT Group
python
from attackcti import attack_client
import json

lift = attack_client()

# Get all threat groups
groups = lift.get_groups()
print(f"Total ATT&CK groups: {len(groups)}")

# Find APT29 (Cozy Bear / Midnight Blizzard)
apt29 = next((g for g in groups if g.get('name') == 'APT29'), None)
if apt29:
    print(f"Group: {apt29['name']}")
    print(f"Aliases: {apt29.get('aliases', [])}")
    print(f"Description: {apt29.get('description', '')[:300]}")

# Get techniques used by APT29 (G0016)
techniques = lift.get_techniques_used_by_group("G0016")
print(f"APT29 uses {len(techniques)} techniques")

technique_map = {}
for tech in techniques:
    tech_id = ""
    for ref in tech.get("external_references", []):
        if ref.get("source_name") == "mitre-attack":
            tech_id = ref.get("external_id", "")
            break
    if tech_id:
        tactics = [p.get("phase_name", "") for p in tech.get("kill_chain_phases", [])]
        technique_map[tech_id] = {
            "name": tech.get("name", ""),
            "tactics": tactics,
            "description": tech.get("description", "")[:500],
            "platforms": tech.get("x_mitre_platforms", []),
            "data_sources": tech.get("x_mitre_data_sources", []),
        }
Step 2: Generate Navigator Layer JSON
python
def create_navigator_layer(group_name, technique_map, color="#ff6666"):
    techniques_list = []
    for tech_id, info in technique_map.items():
        for tactic in info["tactics"]:
            techniques_list.append({
                "techniqueID": tech_id,
                "tactic": tactic,
                "color": color,
                "comment": info["name"],
                "enabled": True,
                "score": 100,
                "metadata": [
                    {"name": "group", "value": group_name},
                    {"name": "platforms", "value": ", ".join(info["platforms"])},
                ],
            })

    layer = {
        "name": f"{group_name} TTP Coverage",
        "versions": {"attack": "16.1", "navigator": "5.1.0", "layer": "4.5"},
        "domain": "enterprise-attack",
        "description": f"Techniques attributed to {group_name}",
        "filters": {
            "platforms": ["Linux", "macOS", "Windows", "Cloud",
                          "Azure AD", "Office 365", "SaaS", "Google Workspace"]
        },
        "sorting": 0,
        "layout": {
            "layout": "side", "aggregateFunction": "average",
            "showID": True, "showName": True,
            "showAggregateScores": False, "countUnscored": False,
        },
        "hideDisabled": False,
        "techniques": techniques_list,
        "gradient": {"colors": ["#ffffff", color], "minValue": 0, "maxValue": 100},
        "legendItems": [
            {"label": f"Used by {group_name}", "color": color},
            {"label": "Not observed", "color": "#ffffff"},
        ],
        "showTacticRowBackground": True,
        "tacticRowBackground": "#dddddd",
        "selectTechniquesAcrossTactics": True,
        "selectSubtechniquesWithParent": False,
        "selectVisibleTechniques": False,
    }
    return layer

layer = create_navigator_layer("APT29", technique_map)
with open("apt29_layer.json", "w") as f:
    json.dump(layer, f, indent=2)
print("[+] Layer saved: apt29_layer.json")
Step 3: Compare Multiple APT Groups
python
groups_to_compare = {"G0016": "APT29", "G0007": "APT28", "G0032": "Lazarus Group"}
group_techniques = {}

for gid, gname in groups_to_compare.items():
    techs = lift.get_techniques_used_by_group(gid)
    tech_ids = set()
    for t in techs:
        for ref in t.get("external_references", []):
            if ref.get("source_name") == "mitre-attack":
                tech_ids.add(ref.get("external_id", ""))
    group_techniques[gname] = tech_ids

common_to_all = set.intersection(*group_techniques.values())
print(f"Techniques common to all groups: {len(common_to_all)}")
for tid in sorted(common_to_all):
    print(f"  {tid}")

for gname, techs in group_techniques.items():
    others = set.union(*[t for n, t in group_techniques.items() if n != gname])
    unique = techs - others
    print(f"\nUnique to {gname}: {len(unique)} techniques")
Step 4: Detection Gap Analysis with Layer Overlay
python
# Define your current detection capabilities
detected_techniques = {
    "T1059", "T1059.001", "T1071", "T1071.001", "T1566", "T1566.001",
    "T1547", "T1547.001", "T1053", "T1053.005", "T1078", "T1027",
}

actor_techniques = set(technique_map.keys())
covered = actor_techniques.intersection(detected_techniques)
gaps = actor_techniques - detected_techniques

print(f"=== Detection Gap Analysis for APT29 ===")
print(f"Actor techniques: {len(actor_techniques)}")
print(f"Detected: {len(covered)} ({len(covered)/len(actor_techniques)*100:.0f}%)")
print(f"Gaps: {len(gaps)} ({len(gaps)/len(actor_techniques)*100:.0f}%)")

# Create gap layer (red = undetected, green = detected)
gap_techniques = []
for tech_id in actor_techniques:
    info = technique_map.get(tech_id, {})
    for tactic in info.get("tactics", [""]):
        color = "#66ff66" if tech_id in detected_techniques else "#ff3333"
        gap_techniques.append({
            "techniqueID": tech_id,
            "tactic": tactic,
            "color": color,
            "comment": f"{'DETECTED' if tech_id in detected_techniques else 'GAP'}: {info.get('name', '')}",
            "enabled": True,
            "score": 100 if tech_id in detected_techniques else 0,
        })

gap_layer = {
    "name": "APT29 Detection Gap Analysis",
    "versions": {"attack": "16.1", "navigator": "5.1.0", "layer": "4.5"},
    "domain": "enterprise-attack",
    "description": "Green = detected, Red = gap",
    "techniques": gap_techniques,
    "gradient": {"colors": ["#ff3333", "#66ff66"], "minValue": 0, "maxValue": 100},
    "legendItems": [
        {"label": "Detected", "color": "#66ff66"},
        {"label": "Detection Gap", "color": "#ff3333"},
    ],
}
with open("apt29_gap_layer.json", "w") as f:
    json.dump(gap_layer, f, indent=2)
Step 5: Tactic Breakdown Analysis
python
from collections import defaultdict

tactic_breakdown = defaultdict(list)
for tech_id, info in technique_map.items():
    for tactic in info["tactics"]:
        tactic_breakdown[tactic].append({"id": tech_id, "name": info["name"]})

tactic_order = [
    "reconnaissance", "resource-development", "initial-access",
    "execution", "persistence", "privilege-escalation",
    "defense-evasion", "credential-access", "discovery",
    "lateral-movement", "collection", "command-and-control",
    "exfiltration", "impact",
]

print("\n=== APT29 Tactic Breakdown ===")
for tactic in tactic_order:
    techs = tactic_breakdown.get(tactic, [])
    if techs:
        print(f"\n{tactic.upper()} ({len(techs)} techniques):")
        for t in techs:
            print(f"  {t['id']}: {t['name']}")

Validation Criteria

  • ATT&CK data queried successfully via TAXII server
  • APT group mapped to all documented techniques with procedure examples
  • Navigator layer JSON validates and renders correctly in ATT&CK Navigator
  • Multi-layer overlay shows threat actor vs. detection coverage
  • Detection gap analysis identifies unmonitored techniques with data source recommendations
  • Cross-group comparison reveals shared and unique TTPs
  • Output is actionable for detection engineering prioritization

References

© 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/analyzing-apt-group-with-mitre-navigator 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

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C To AstNarwhal-Lab/MagicSkills316—~1.1kAutomated safety check: PassMIT
Security Detection Rule Managementelastic/agent-skills5921 repos~3.9kAutomated safety check: NotesApache-2.0
Security AuditTheDecipherist/claude-code-mastery551—~1.3kAutomated safety check: NotesMIT

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Works with

Categories

Questions about Analyzing Apt Group With Mitre Navigator

What does Analyzing Apt Group With Mitre Navigator do?

Query ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups' TTPs for detection-gap…. Analyzing Apt Group With Mitre Navigator is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Query ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups' TTPs for detection-gap analysis.

When should I use Analyzing Apt Group With Mitre Navigator?

Analyzing Apt Group With Mitre Navigator fits situations like: compare threat-actor technique coverage; find gaps in detection engineering; produce Navigator visualizations for threat-intel reporting.

How do I install Analyzing Apt Group With Mitre Navigator in Claude Code?

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

How do I install Analyzing Apt Group With Mitre Navigator in Codex?

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

Can I use Analyzing Apt Group With Mitre Navigator 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 analyzing-apt-group-with-mitre-navigator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analyzing-apt-group-with-mitre-navigator, .gemini/skills/analyzing-apt-group-with-mitre-navigator, .github/skills/analyzing-apt-group-with-mitre-navigator and .opencode/skills/analyzing-apt-group-with-mitre-navigator in your project.

What does Analyzing Apt Group With Mitre Navigator need to run?

Going by SKILL.md and its folder, Analyzing Apt Group With Mitre Navigator needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Analyzing Apt Group With Mitre Navigator access the network?

SKILL.md names 5 domains. As links in the text: mitre-attack.github.io, github.com, attack.mitre.org, cisa.gov and picussecurity.com. This is read from the text; nothing was executed.

Is Analyzing Apt Group With Mitre Navigator 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 Analyzing Apt Group With Mitre Navigator use?

Analyzing Apt Group With Mitre Navigator 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 Analyzing Apt Group With Mitre Navigator use?

About 2.9k 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 644 tokens, read only when the agent opens those files.

What are the alternatives to Analyzing Apt Group With Mitre Navigator?

Skills that share tags, products or a category with Analyzing Apt Group With Mitre Navigator: Security Alert Triage (elastic/agent-skills, 592 stars), Kubernetes Network Security Audit (kubeshark/kubeshark, 12k stars), C To Ast (Narwhal-Lab/MagicSkills, 316 stars) and Security Detection Rule Management (elastic/agent-skills, 592 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Analyzing Apt Group With Mitre Navigator?

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.