agentmods.dev

analyzing-cloud-storage-access-patterns

analyzing-cloud-storage-access-patterns is a skill for Claude Code, Codex from pinkpixel-dev/skills-collection-1. It costs 79 tokens per session (484 once invoked), scanned A, a copy of analyzing-cloud-storage-access-patterns, Apache-2.0.

A guide to finding unusual activity in AWS S3, Google Cloud Storage, and Azure Blob Storage by examining access logs. It looks for patterns such as bulk downloads, new IP addresses, and unusual API use.

In plain words
What is it for?
It is for building access baselines, detecting anomalies in cloud audit logs, prioritizing findings, and investigating storage-related security incidents.
Why use it?
It helps security teams notice possible data theft or account misuse that may be hidden among normal storage requests.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 7 27d ago A scan 79 tokens copy · 100% Apache-2.0
View the source file pinkpixel-dev/skills-collection-1
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/pinkpixel-dev/skills-collection-1/analyzing-cloud-storage-access-patterns
Any agent
npx skills add pinkpixel-dev/skills-collection-1 --skill analyzing-cloud-storage-access-patterns
Clone the repo
git clone --depth 1 https://github.com/pinkpixel-dev/skills-collection-1

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for analyzing-cloud-storage-access-patterns

README.md
[![agentmods](https://agentmods.dev/badge/skills/pinkpixel-dev/skills-collection-1/analyzing-cloud-storage-access-patterns.svg)](https://agentmods.dev/skills/pinkpixel-dev/skills-collection-1/analyzing-cloud-storage-access-patterns)
Your own site
<a href="https://agentmods.dev/skills/pinkpixel-dev/skills-collection-1/analyzing-cloud-storage-access-patterns"><img src="https://agentmods.dev/badge/skills/pinkpixel-dev/skills-collection-1/analyzing-cloud-storage-access-patterns.svg" alt="Measured on agentmods" height="20"></a>
Per session 79 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 484 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00079 $0.00484
Opus 5 $0.00039 $0.00242
Sonnet 5 $0.00016 $0.00097
Haiku 4.5 $0.00008 $0.00048

Measured 2d ago against content hash e4776c1453bf, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

analyzing-cloud-storage-access-patterns scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/agent.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to analyzing-cloud-storage-access-patterns — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

SKILLS/analyzing-cloud-storage-access-patterns/SKILL.md · 57 lines

What it actually says

Analyzing Cloud Storage Access Patterns

When to Use

  • When investigating security incidents that require analyzing cloud storage access patterns
  • 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

  • Familiarity with cloud security concepts and tools
  • Access to a test or lab environment for safe execution
  • Python 3.8+ with required dependencies installed
  • Appropriate authorization for any testing activities

Instructions

  1. Install dependencies: pip install boto3 requests
  2. Query CloudTrail for S3 Data Events using AWS CLI or boto3.
  3. Build access baselines: hourly request volume, per-user object counts, source IP history.
  4. Detect anomalies:
    • After-hours access (outside 8am-6pm local time)
    • Bulk downloads: >100 GetObject calls from single principal in 1 hour
    • New source IPs not seen in the prior 30 days
    • ListBucket enumeration spikes (reconnaissance indicator)
  5. Generate prioritized findings report.
python scripts/agent.py --bucket my-sensitive-data --hours-back 24 --output s3_access_report.json

Examples

CloudTrail S3 Data Event

{"eventName": "GetObject", "requestParameters": {"bucketName": "sensitive-data", "key": "financials/q4.xlsx"},
 "sourceIPAddress": "203.0.113.50", "userIdentity": {"arn": "arn:aws:iam::123456789012:user/analyst"}}
Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 57 lines · 79 tokens per session scan A e4776c1453bf

Subscribe to this mod's changes

analyzing-cloud-storage-access-patterns is a skill published in the GitHub repository pinkpixel-dev/skills-collection-1 (7 stars, last pushed 27d ago), licensed Apache-2.0. It adds 79 tokens to every session and 484 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to analyzing-cloud-storage-access-patterns, differing in 0 lines, and is treated as a copy.

CloudDevOps and cloudSecurityaccessagent-skillsagentsaianalyzingautomationawesome-liststorage
Related

Other skills, from other repositories

analyzing-cloud-storage-access-patterns

Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines…

xalgorix/xalgorix · 79 tokens

analyzing-cloud-storage-access-patterns

Use when detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical…

oyi77/1ai-skills · 98 tokens

analyzing-cloud-storage-access-patterns

Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines…

marysatasselshaped667/skills-collection-1 · 79 tokens

analyzing-cloud-storage-access-patterns

Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines…

plurigrid/asi · 79 tokens

analyzing-cloud-storage-access-patterns

Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines…

autohandai/community-skills · 79 tokens

analyzing-cloud-storage-access-patterns

Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines…

Mikaru0Mystic/sectinel · 79 tokens