analyzing-cloud-storage-access-patterns

analyzing-cloud-storage-access-patterns is a skill for Claude Code from oyi77/1ai-skills. It costs 98 tokens per session (951 once invoked), scanned A, original, MIT.

A security procedure for finding unusual activity in cloud object storage such as Amazon S3, Google Cloud Storage, and Azure Blob Storage.

In plain words
What is it for?
Use it to examine audit logs, detect unusual API activity, investigate possible data theft, create detection rules, and check monitoring coverage.
Why use it?
It helps identify signs that data may be accessed or copied in suspicious ways, such as bulk downloads or unfamiliar locations.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the 1ai-skills plugin — 209 skills, 4 commands shipped together

Good fit Use it to examine audit logs, detect unusual API activity, investigate possible data theft, create detection rules, and check monitoring coverage.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/oyi77/1ai-skills/analyzing-cloud-storage-access-patterns
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.

Any agent
npx skills add oyi77/1ai-skills --skill analyzing-cloud-storage-access-patterns
Clone the repo
git clone --depth 1 https://github.com/oyi77/1ai-skills

Made for: Claude Code.

Or install 1ai-skills, the plugin that ships this one along with the rest of its 209 skills, 4 commands.

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
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Your own site
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<a href="https://agentmods.dev/skills/oyi77/1ai-skills/analyzing-cloud-storage-access-patterns"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/analyzing-cloud-storage-access-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 98 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 951 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found 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 $0.00098 $0.00951
Opus 5 $0.00049 $0.00476
Sonnet 5 $0.00020 $0.00190
Haiku 4.5 $0.00010 $0.00095

Measured 6d ago against content hash 3e0071a1c13e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, 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 6d ago.

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.

cybersecurity/analyzing-cloud-storage-access-patterns/SKILL.md · 117 lines

How it starts

The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Analyzing Cloud Storage Access Patterns

Overview

Cybersecurity skill for analyzing cloud storage access patterns. Follows industry best practices and security standards.

When to Use

Trigger phrases:

  • "analyzing cloud storage access patterns"

  • "Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyz"

  • 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

When NOT to Use

  • When you lack proper authorization for testing
  • For production systems without change management
  • When the task requires legal or compliance expertise beyond technical scope

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

Workflow

# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}
  1. Scope the Analysis — Define what cloud storage access patterns artifacts or data sources to examine and the investigation timeline.
  2. Preserve Evidence — Create forensic copies of relevant data. Maintain chain of custody documentation.
  3. Extract Key Indicators — Parse and extract relevant cloud storage access patterns data points from collected artifacts.
  4. Correlate Findings — Cross-reference extracted data with other sources (threat intel, logs, timelines).
  5. Build Timeline — Construct a chronological sequence of events related to cloud storage access patterns.
  6. Document Analysis — Write findings report with evidence, conclusions, and recommendations.

Read the full file on GitHub · 117 lines

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. 6d ago First seen · 117 lines · 98 tokens per session scan A 3e0071a1c13e

Subscribe to this mod's changes

analyzing-cloud-storage-access-patterns is a skill published in the GitHub repository oyi77/1ai-skills (12 stars, last pushed today), licensed MIT. It adds 98 tokens to every session and 951 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-04.

Related

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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…

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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…

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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…

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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…

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