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Agent skill

Detecting AWS Cloudtrail Anomalies

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Detect unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized…

Apache-2.0Auto-check passedSecurity

Install Detecting AWS Cloudtrail Anomalies

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-aws-cloudtrail-anomalies -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-aws-cloudtrail-anomalies --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/detecting-aws-cloudtrail-anomalies .claude/skills/detecting-aws-cloudtrail-anomalies && 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
detecting-aws-cloudtrail-anomalies
GitHub stars
34k
Token cost
~751 tokens
SKILL.md length
234 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Detect unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized…

  • Works in 4 steps: Query CloudTrail Events → Build Activity Baseline → Detect Anomalies → …
  • Tasks that involve Anomaly detection
  • SKILL.md covers Overview, When to Use, Prerequisites and Steps, plus 1 more section
  • Runs Python scripts from its folder

What it does

Detecting AWS Cloudtrail Anomalies is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detect unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized resource access.

Its SKILL.md is about 750 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 Anomaly detection and Red teaming and adversary simulation. It works with Amazon Web Services. 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

  • Tasks that involve Anomaly detection
  • Tasks that involve Red teaming and adversary simulation

Example prompts

  • “/detecting-aws-cloudtrail-anomalies”

Requirements

  • Python 3

Workflow steps

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

  1. Query CloudTrail Events
  2. Build Activity Baseline
  3. Detect Anomalies
  4. Generate Detection 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

Detecting AWS Cloudtrail Anomalies loads about 751 tokens when it runs, and up to ~1.3k if it reads all its reference files. Until then it costs about 61 tokens; SKILL.md has 234 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~61
When it runs · the whole SKILL.md, loaded when a task matches
~751
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.3k

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). 234 words, ~751 tokens.

Download SKILL.mdSave it as .claude/skills/detecting-aws-cloudtrail-anomalies/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
detecting-aws-cloudtrail-anomalies
description
Detect unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized resource access.
domain
cybersecurity
subdomain
cloud-security
tags
cloud-security, aws, cloudtrail, anomaly-detection, threat-detection, boto3
version
1.0
author
mahipal
license
Apache-2.0
nist_csf
PR.IR-01, ID.AM-08, GV.SC-06, DE.CM-01
mitre_attack
T1078.004, T1580, T1538, T1098.001, T1526
mitre_f3.version
1.1
mitre_f3.tactics
initial-access, positioning, defense-impairment

Detecting AWS CloudTrail Anomalies

Overview

AWS CloudTrail records API calls across AWS services. This skill covers querying CloudTrail events with boto3's lookup_events API, building statistical baselines of normal API activity, detecting anomalies such as unusual event sources, geographic anomalies, high-frequency API calls, and first-time API usage patterns that indicate compromised credentials or insider threats.

When to Use

  • When investigating security incidents that require detecting aws cloudtrail anomalies
  • 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 boto3 library
  • AWS credentials with CloudTrail read permissions (cloudtrail:LookupEvents)
  • Understanding of AWS IAM and common API patterns
  • CloudTrail enabled in target AWS account (management events at minimum)

Steps

Step 1: Query CloudTrail Events

Use boto3 CloudTrail client's lookup_events to retrieve recent API activity with pagination.

Step 2: Build Activity Baseline

Aggregate events by user, source IP, event source, and event name to establish normal behavior patterns.

Step 3: Detect Anomalies

Flag unusual patterns: new event sources per user, first-time API calls, geographic IP changes, high error rates, and sensitive API usage (IAM, KMS, S3 policy changes).

Step 4: Generate Detection Report

Produce a JSON report with anomaly scores, top suspicious users, and recommended investigation actions.

Expected Output

JSON report with event statistics, baseline deviations, anomalous users/IPs, sensitive API calls, and error rate analysis.

© 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/detecting-aws-cloudtrail-anomalies 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

Detecting AWS Cloudtrail Anomalies 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.

Detecting AWS Cloudtrail Anomalies compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Detecting AWS Cloudtrail Anomalies this skillmukul975/Anthropic-Cybersecurity-Skills34k—~751Automated safety check: PassApache-2.0
Cloud Defensetransilienceai/communitytools563—~476Automated safety check: PassMIT
Investigating AWS Incidentstrilwu/secskills157—~4.8kAutomated safety check: PassMIT
Rds Db2aws/agent-toolkit-for-aws2.8k—~6.9kAutomated safety check: PassApache-2.0
Amazon Workspaces Agent Accessaws/agent-toolkit-for-aws2.8k—~2.7kAutomated safety check: PassApache-2.0
Dt Obs HostsDynatrace/dynatrace-for-ai163—~5.5kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Detecting AWS Cloudtrail Anomalies

What does Detecting AWS Cloudtrail Anomalies do?

Detect unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized…. Detecting AWS Cloudtrail Anomalies is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detect unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized resource access.

When should I use Detecting AWS Cloudtrail Anomalies?

Detecting AWS Cloudtrail Anomalies fits situations like: tasks that involve Anomaly detection; tasks that involve Red teaming and adversary simulation.

How do I install Detecting AWS Cloudtrail Anomalies in Claude Code?

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

How do I install Detecting AWS Cloudtrail Anomalies in Codex?

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

Can I use Detecting AWS Cloudtrail Anomalies 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 detecting-aws-cloudtrail-anomalies -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/detecting-aws-cloudtrail-anomalies, .gemini/skills/detecting-aws-cloudtrail-anomalies, .github/skills/detecting-aws-cloudtrail-anomalies and .opencode/skills/detecting-aws-cloudtrail-anomalies in your project.

What does Detecting AWS Cloudtrail Anomalies need to run?

Going by SKILL.md and its folder, Detecting AWS Cloudtrail Anomalies needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Detecting AWS Cloudtrail Anomalies 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 Detecting AWS Cloudtrail Anomalies 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 Detecting AWS Cloudtrail Anomalies use?

Detecting AWS Cloudtrail Anomalies 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 Detecting AWS Cloudtrail Anomalies use?

About 751 tokens (SKILL.md is roughly 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 528 tokens, read only when the agent opens those files.

What are the alternatives to Detecting AWS Cloudtrail Anomalies?

Skills that share tags, products or a category with Detecting AWS Cloudtrail Anomalies: Cloud Defense (transilienceai/communitytools, 563 stars), Investigating AWS Incidents (trilwu/secskills, 157 stars), Rds Db2 (aws/agent-toolkit-for-aws, 2.8k stars) and Amazon Workspaces Agent Access (aws/agent-toolkit-for-aws, 2.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Detecting AWS Cloudtrail Anomalies?

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.