Analyzing Cloud Storage Access Patterns

What it does

When your agent uses it

• Investigating suspected cloud data exfiltration
• Building related detection rules

Example prompts

• “/analyzing-cloud-storage-access-patterns”

Requirements

• Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1Install dependencies: pip install boto3 requests
2. 2Query CloudTrail for S3 Data Events using AWS CLI or boto3.
3. 3Build access baselines: hourly request volume, per-user object counts, source IP history.
4. 4Detect anomalies
5. 5Generate prioritized findings 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.

  Shell commands in SKILL.md call:

  • python
  • pip

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

• Network

  No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

  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 Cloud Storage Access Patterns loads about 599 tokens when it runs, and up to ~984 if it reads all its reference files. Until then it costs about 105 tokens; SKILL.md has 157 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~105

When it runs · the whole SKILL.md, loaded when a task matches

~599

With references · SKILL.md plus every file in references/, read only if the agent opens them

~984

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

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). 157 words, ~599 tokens.

name

analyzing-cloud-storage-access-patterns

description

Detect abnormal access in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics for after-hours bulk downloads, new-IP access, and API-call spikes (e.g. GetObject) via statistical baselines and time-series anomaly detection. Use when investigating suspected cloud data exfiltration or building related detection rules.

domain

cybersecurity

subdomain

cloud-security

tags

cloud-security, aws-s3, gcs, azure-blob-storage, cloudtrail, data-access-anomaly, exfiltration-detection

version

1.0

author

mahipal

license

Apache-2.0

atlas_techniques

AML.T0024, AML.T0056

nist_ai_rmf

MEASURE-2.7, MAP-5.1, MANAGE-2.4

nist_csf

PR.IR-01, ID.AM-08, GV.SC-06, DE.CM-01

mitre_attack

T1530, T1567.002, T1619, T1078.004, T1048

python scripts/agent.py --bucket my-sensitive-data --hours-back 24 --output s3_access_report.json

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

© 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-cloud-storage-access-patterns of mukul975/Anthropic-Cybersecurity-Skills.

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

Open the folder on GitHubat commit 54a7988