Agent skill

Performing Cloud Log Forensics With Athena

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Uses AWS Athena to query CloudTrail, VPC Flow Logs, S3 access logs, and ALB logs for forensic investigation.

Apache-2.0Auto-check passedSecurity

Install Performing Cloud Log Forensics With Athena

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-cloud-log-forensics-with-athena -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills performing-cloud-log-forensics-with-athena --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/performing-cloud-log-forensics-with-athena .claude/skills/performing-cloud-log-forensics-with-athena && 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
performing-cloud-log-forensics-with-athena
GitHub stars
34k
Token cost
~3.7k tokens
SKILL.md length
231 words
Files
5 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Uses AWS Athena to query CloudTrail, VPC Flow Logs, S3 access logs, and ALB logs for forensic investigation.

  • Works in 6 steps: Create Athena Database and CloudTrail… → Create VPC Flow Logs Table → Create S3 Access Logs Table → …
  • Investigating AWS security incidents
  • SKILL.md covers When to Use, Prerequisites, Instructions and Examples
  • Runs Python scripts from its folder

What it does

Performing Cloud Log Forensics With Athena is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Uses AWS Athena to query CloudTrail, VPC Flow Logs, S3 access logs, and ALB logs for forensic investigation. Covers CREATE TABLE DDL with partition projection, forensic SQL queries for detecting unauthorized access, data exfiltration, lateral movement, and privilege escalation. Use when investigating AWS security incidents or building cloud-native forensic workflows at scale.

Its SKILL.md is about 3.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/api-reference.md`, `references/athena-forensics-reference.md` and `scripts/agent.py`).

It sits in Security, covering Red teaming and adversary simulation, Digital forensics and File uploads and storage. It works with Amazon Web Services and SQL. 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

  • Investigating AWS security incidents
  • Building cloud-native forensic workflows at scale

Example prompts

  • “Use the performing-cloud-log-forensics-with-athena skill to use AWS Athena to query CloudTrail, VPC Flow Logs, S3 access logs, and ALB logs for…”
  • “/performing-cloud-log-forensics-with-athena”

Requirements

  • Python 3

Workflow steps

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

  1. Create Athena Database and CloudTrail Table
  2. Create VPC Flow Logs Table
  3. Create S3 Access Logs Table
  4. Create ALB Access Logs Table
  5. Forensic Investigation Queries
  6. Cross-Log Correlation

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

Performing Cloud Log Forensics With Athena loads about 3.7k tokens when it runs, and up to ~7.3k if it reads all its reference files. Until then it costs about 105 tokens; SKILL.md has 231 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
~3.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.3k

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). 231 words, ~3,743 tokens.

Download SKILL.mdSave it as .claude/skills/performing-cloud-log-forensics-with-athena/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
performing-cloud-log-forensics-with-athena
description
Uses AWS Athena to query CloudTrail, VPC Flow Logs, S3 access logs, and ALB logs for forensic investigation. Covers CREATE TABLE DDL with partition projection, forensic SQL queries for detecting unauthorized access, data exfiltration, lateral movement, and privilege escalation. Use when investigating AWS security incidents or building cloud-native forensic workflows at scale.
domain
cybersecurity
subdomain
cloud-security
tags
cloud, forensics, athena, aws, cloudtrail, vpc-flow-logs, s3, alb
version
1.0
author
mukul975
license
Apache-2.0
nist_csf
PR.IR-01, ID.AM-08, GV.SC-06, DE.CM-01
mitre_attack
T1078.004, T1530, T1537, T1580, T1021

Performing Cloud Log Forensics with AWS Athena

When to Use

  • When investigating AWS security incidents that require querying massive volumes of cloud logs
  • When performing forensic analysis across CloudTrail, VPC Flow Logs, S3 access logs, and ALB logs
  • When building reusable Athena tables with partition projection for ongoing incident response
  • When hunting for indicators of compromise across multiple AWS log sources simultaneously
  • When creating evidence-grade SQL queries for compliance audits or legal proceedings

Prerequisites

  • AWS account with Athena, S3, and Glue permissions
  • CloudTrail configured to deliver logs to an S3 bucket
  • VPC Flow Logs enabled and publishing to S3
  • S3 server access logging enabled on target buckets
  • ALB access logging enabled and publishing to S3
  • Python 3.8+ with boto3 installed
  • Appropriate IAM permissions for Athena queries and S3 access

Instructions

Phase 1: Create Athena Database and CloudTrail Table

Create a dedicated forensics database and CloudTrail table using partition projection to automatically discover partitions without manual ALTER TABLE statements.

sql
CREATE DATABASE IF NOT EXISTS cloud_forensics;

CREATE EXTERNAL TABLE cloud_forensics.cloudtrail_logs (
    eventVersion STRING,
    userIdentity STRUCT<
        type: STRING,
        principalId: STRING,
        arn: STRING,
        accountId: STRING,
        invokedBy: STRING,
        accessKeyId: STRING,
        userName: STRING,
        sessionContext: STRUCT<
            attributes: STRUCT<
                mfaAuthenticated: STRING,
                creationDate: STRING>,
            sessionIssuer: STRUCT<
                type: STRING,
                principalId: STRING,
                arn: STRING,
                accountId: STRING,
                userName: STRING>,
            ec2RoleDelivery: STRING,
            webIdFederationData: STRUCT<
                federatedProvider: STRING,
                attributes: MAP<STRING, STRING>>>>,
    eventTime STRING,
    eventSource STRING,
    eventName STRING,
    awsRegion STRING,
    sourceIPAddress STRING,
    userAgent STRING,
    errorCode STRING,
    errorMessage STRING,
    requestParameters STRING,
    responseElements STRING,
    additionalEventData STRING,
    requestId STRING,
    eventId STRING,
    readOnly STRING,
    resources ARRAY<STRUCT<
        arn: STRING,
        accountId: STRING,
        type: STRING>>,
    eventType STRING,
    apiVersion STRING,
    recipientAccountId STRING,
    serviceEventDetails STRING,
    sharedEventID STRING,
    vpcEndpointId STRING,
    tlsDetails STRUCT<
        tlsVersion: STRING,
        cipherSuite: STRING,
        clientProvidedHostHeader: STRING>
)
COMMENT 'CloudTrail logs with partition projection for forensic analysis'
PARTITIONED BY (
    `account` STRING,
    `region` STRING,
    `timestamp` STRING
)
ROW FORMAT SERDE 'org.apache.hive.hcatalog.data.JsonSerDe'
STORED AS INPUTFORMAT 'com.amazon.emr.cloudtrail.CloudTrailInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION 's3://YOUR-CLOUDTRAIL-BUCKET/AWSLogs/'
TBLPROPERTIES (
    'projection.enabled' = 'true',
    'projection.account.type' = 'enum',
    'projection.account.values' = 'YOUR_ACCOUNT_ID',
    'projection.region.type' = 'enum',
    'projection.region.values' = 'us-east-1,us-west-2,eu-west-1',
    'projection.timestamp.type' = 'date',
    'projection.timestamp.format' = 'yyyy/MM/dd',
    'projection.timestamp.range' = '2023/01/01,NOW',
    'projection.timestamp.interval' = '1',
    'projection.timestamp.interval.unit' = 'DAYS',
    'storage.location.template' = 's3://YOUR-CLOUDTRAIL-BUCKET/AWSLogs/${account}/CloudTrail/${region}/${timestamp}'
);
Phase 2: Create VPC Flow Logs Table
sql
CREATE EXTERNAL TABLE cloud_forensics.vpc_flow_logs (
    version INT,
    account_id STRING,
    interface_id STRING,
    srcaddr STRING,
    dstaddr STRING,
    srcport INT,
    dstport INT,
    protocol BIGINT,
    packets BIGINT,
    bytes BIGINT,
    start BIGINT,
    `end` BIGINT,
    action STRING,
    log_status STRING,
    vpc_id STRING,
    subnet_id STRING,
    az_id STRING,
    sublocation_type STRING,
    sublocation_id STRING,
    pkt_srcaddr STRING,
    pkt_dstaddr STRING,
    region STRING,
    pkt_src_aws_service STRING,
    pkt_dst_aws_service STRING,
    flow_direction STRING,
    traffic_path INT
)
PARTITIONED BY (
    `date` STRING
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ' '
LOCATION 's3://YOUR-VPC-FLOW-LOGS-BUCKET/AWSLogs/YOUR_ACCOUNT_ID/vpcflowlogs/'
TBLPROPERTIES (
    'skip.header.line.count' = '1',
    'projection.enabled' = 'true',
    'projection.date.type' = 'date',
    'projection.date.format' = 'yyyy/MM/dd',
    'projection.date.range' = '2023/01/01,NOW',
    'projection.date.interval' = '1',
    'projection.date.interval.unit' = 'DAYS',
    'storage.location.template' = 's3://YOUR-VPC-FLOW-LOGS-BUCKET/AWSLogs/YOUR_ACCOUNT_ID/vpcflowlogs/us-east-1/${date}'
);
Phase 3: Create S3 Access Logs Table
sql
CREATE EXTERNAL TABLE cloud_forensics.s3_access_logs (
    bucket_owner STRING,
    bucket_name STRING,
    request_datetime STRING,
    remote_ip STRING,
    requester STRING,
    request_id STRING,
    operation STRING,
    key STRING,
    request_uri STRING,
    http_status INT,
    error_code STRING,
    bytes_sent BIGINT,
    object_size BIGINT,
    total_time INT,
    turn_around_time INT,
    referrer STRING,
    user_agent STRING,
    version_id STRING,
    host_id STRING,
    signature_version STRING,
    cipher_suite STRING,
    authentication_type STRING,
    host_header STRING,
    tls_version STRING,
    access_point_arn STRING,
    acl_required STRING
)
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.RegexSerDe'
WITH SERDEPROPERTIES (
    'serialization.format' = '1',
    'input.regex' = '([^ ]*) ([^ ]*) \\[(.*?)\\] ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) (\"[^\"]*\"|-) (-|[0-9]*) ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) (\"[^\"]*\"|-) ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*)'
)
STORED AS INPUTFORMAT 'org.apache.hadoop.mapred.TextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION 's3://YOUR-S3-ACCESS-LOGS-BUCKET/logs/';
Phase 4: Create ALB Access Logs Table
sql
CREATE EXTERNAL TABLE cloud_forensics.alb_access_logs (
    type STRING,
    time STRING,
    elb STRING,
    client_ip STRING,
    client_port INT,
    target_ip STRING,
    target_port INT,
    request_processing_time DOUBLE,
    target_processing_time DOUBLE,
    response_processing_time DOUBLE,
    elb_status_code INT,
    target_status_code STRING,
    received_bytes BIGINT,
    sent_bytes BIGINT,
    request_verb STRING,
    request_url STRING,
    request_proto STRING,
    user_agent STRING,
    ssl_cipher STRING,
    ssl_protocol STRING,
    target_group_arn STRING,
    trace_id STRING,
    domain_name STRING,
    chosen_cert_arn STRING,
    matched_rule_priority STRING,
    request_creation_time STRING,
    actions_executed STRING,
    redirect_url STRING,
    lambda_error_reason STRING,
    target_port_list STRING,
    target_status_code_list STRING,
    classification STRING,
    classification_reason STRING,
    conn_trace_id STRING
)
PARTITIONED BY (
    `day` STRING
)
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.RegexSerDe'
WITH SERDEPROPERTIES (
    'serialization.format' = '1',
    'input.regex' = '([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*):([0-9]*) ([^ ]*)[:-]([0-9]*) ([-.0-9]*) ([-.0-9]*) ([-.0-9]*) (|[0-9]*) (-|[0-9]*) ([-0-9]*) ([-0-9]*) \"([^ ]*) (.*) (- |[^ ]*)\" \"([^\"]*)\" ([A-Z0-9-_]+) ([A-Za-z0-9.-]*) ([^ ]*) \"([^\"]*)\" \"([^\"]*)\" \"([^\"]*)\" ([-.0-9]*) ([^ ]*) \"([^\"]*)\" \"([^\"]*)\" \"([^ ]*)\" \"([^\"]*)\" \"([^ ]*)\" \"([^ ]*)\" \"([^ ]*)\"'
)
STORED AS INPUTFORMAT 'org.apache.hadoop.mapred.TextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION 's3://YOUR-ALB-LOGS-BUCKET/AWSLogs/YOUR_ACCOUNT_ID/elasticloadbalancing/us-east-1/'
TBLPROPERTIES (
    'projection.enabled' = 'true',
    'projection.day.type' = 'date',
    'projection.day.format' = 'yyyy/MM/dd',
    'projection.day.range' = '2023/01/01,NOW',
    'projection.day.interval' = '1',
    'projection.day.interval.unit' = 'DAYS',
    'storage.location.template' = 's3://YOUR-ALB-LOGS-BUCKET/AWSLogs/YOUR_ACCOUNT_ID/elasticloadbalancing/us-east-1/${day}'
);
Phase 5: Forensic Investigation Queries
Detect Unauthorized API Calls
sql
SELECT
    eventtime,
    useridentity.arn AS caller_arn,
    useridentity.accountid AS account,
    eventsource,
    eventname,
    errorcode,
    errormessage,
    sourceipaddress,
    useragent
FROM cloud_forensics.cloudtrail_logs
WHERE errorcode IN ('AccessDenied', 'UnauthorizedAccess', 'Client.UnauthorizedAccess')
    AND timestamp BETWEEN '2024/01/01' AND '2024/12/31'
ORDER BY eventtime DESC
LIMIT 1000;
Detect Privilege Escalation Attempts
sql
SELECT
    eventtime,
    useridentity.arn AS actor,
    eventname,
    eventsource,
    json_extract_scalar(requestparameters, '$.policyArn') AS policy_arn,
    json_extract_scalar(requestparameters, '$.roleName') AS role_name,
    json_extract_scalar(requestparameters, '$.userName') AS target_user,
    sourceipaddress
FROM cloud_forensics.cloudtrail_logs
WHERE eventname IN (
    'AttachUserPolicy', 'AttachRolePolicy', 'AttachGroupPolicy',
    'PutUserPolicy', 'PutRolePolicy', 'PutGroupPolicy',
    'CreatePolicyVersion', 'SetDefaultPolicyVersion',
    'AddUserToGroup', 'UpdateAssumeRolePolicy',
    'CreateAccessKey', 'CreateLoginProfile',
    'UpdateLoginProfile', 'AssumeRole'
)
    AND timestamp BETWEEN '2024/01/01' AND '2024/12/31'
ORDER BY eventtime DESC;
Detect Data Exfiltration via S3
sql
SELECT
    eventtime,
    useridentity.arn AS actor,
    eventname,
    json_extract_scalar(requestparameters, '$.bucketName') AS bucket,
    json_extract_scalar(requestparameters, '$.key') AS object_key,
    sourceipaddress,
    useragent
FROM cloud_forensics.cloudtrail_logs
WHERE eventsource = 's3.amazonaws.com'
    AND eventname IN ('GetObject', 'CopyObject', 'PutBucketPolicy',
                      'PutBucketAcl', 'PutObjectAcl', 'SelectObjectContent')
    AND sourceipaddress NOT LIKE '10.%'
    AND sourceipaddress NOT LIKE '172.%'
    AND sourceipaddress NOT LIKE '192.168.%'
    AND timestamp BETWEEN '2024/01/01' AND '2024/12/31'
ORDER BY eventtime DESC;
Detect Lateral Movement via VPC Flow Logs
sql
SELECT
    srcaddr,
    dstaddr,
    dstport,
    protocol,
    SUM(packets) AS total_packets,
    SUM(bytes) AS total_bytes,
    COUNT(*) AS connection_count,
    MIN(from_unixtime(start)) AS first_seen,
    MAX(from_unixtime("end")) AS last_seen
FROM cloud_forensics.vpc_flow_logs
WHERE action = 'ACCEPT'
    AND srcaddr LIKE '10.%'
    AND dstport IN (22, 3389, 5985, 5986, 445, 135, 139)
    AND date BETWEEN '2024/06/01' AND '2024/06/30'
GROUP BY srcaddr, dstaddr, dstport, protocol
HAVING COUNT(*) > 100
ORDER BY connection_count DESC;
Detect Port Scanning Activity
sql
SELECT
    srcaddr,
    COUNT(DISTINCT dstport) AS unique_ports_scanned,
    COUNT(DISTINCT dstaddr) AS unique_targets,
    SUM(packets) AS total_packets,
    MIN(from_unixtime(start)) AS first_seen,
    MAX(from_unixtime("end")) AS last_seen
FROM cloud_forensics.vpc_flow_logs
WHERE action = 'REJECT'
    AND date BETWEEN '2024/06/01' AND '2024/06/30'
GROUP BY srcaddr
HAVING COUNT(DISTINCT dstport) > 25
ORDER BY unique_ports_scanned DESC;
Detect Suspicious S3 Bulk Downloads
sql
SELECT
    remote_ip,
    requester,
    bucket_name,
    COUNT(*) AS request_count,
    SUM(bytes_sent) AS total_bytes_downloaded,
    COUNT(DISTINCT key) AS unique_objects,
    MIN(request_datetime) AS first_request,
    MAX(request_datetime) AS last_request
FROM cloud_forensics.s3_access_logs
WHERE operation LIKE '%GET%'
    AND http_status = 200
GROUP BY remote_ip, requester, bucket_name
HAVING COUNT(*) > 500
ORDER BY total_bytes_downloaded DESC;
Detect ALB-Level Injection Attempts
sql
SELECT
    time,
    client_ip,
    request_verb,
    request_url,
    elb_status_code,
    target_status_code,
    user_agent
FROM cloud_forensics.alb_access_logs
WHERE (
    request_url LIKE '%UNION%SELECT%'
    OR request_url LIKE '%<script%'
    OR request_url LIKE '%../../../%'
    OR request_url LIKE '%/etc/passwd%'
    OR request_url LIKE '%cmd.exe%'
    OR request_url LIKE '%/proc/self%'
    OR request_url LIKE '%SLEEP(%'
    OR request_url LIKE '%WAITFOR%'
)
    AND day BETWEEN '2024/06/01' AND '2024/06/30'
ORDER BY time DESC;
Phase 6: Cross-Log Correlation

Correlate findings across log sources for comprehensive incident timelines.

sql
-- Correlate suspicious CloudTrail actor with VPC Flow Logs
WITH suspicious_ips AS (
    SELECT DISTINCT sourceipaddress AS ip
    FROM cloud_forensics.cloudtrail_logs
    WHERE errorcode = 'AccessDenied'
        AND timestamp BETWEEN '2024/06/01' AND '2024/06/30'
)
SELECT
    v.srcaddr,
    v.dstaddr,
    v.dstport,
    v.protocol,
    SUM(v.bytes) AS total_bytes,
    COUNT(*) AS flow_count
FROM cloud_forensics.vpc_flow_logs v
JOIN suspicious_ips s ON v.srcaddr = s.ip
WHERE v.date BETWEEN '2024/06/01' AND '2024/06/30'
GROUP BY v.srcaddr, v.dstaddr, v.dstport, v.protocol
ORDER BY total_bytes DESC;

Examples

python
# Quick-start: run the forensics agent for a full investigation
python agent.py \
    --action full_investigation \
    --database cloud_forensics \
    --start-date 2024-06-01 \
    --end-date 2024-06-30 \
    --output forensics_report.json

# Run specific queries only
python agent.py \
    --action privilege_escalation \
    --database cloud_forensics \
    --start-date 2024-06-15 \
    --end-date 2024-06-16

# Create all forensic tables from scratch
python agent.py \
    --action setup_tables \
    --cloudtrail-bucket my-cloudtrail-logs \
    --vpc-flow-bucket my-vpc-flow-logs \
    --s3-access-bucket my-s3-access-logs \
    --alb-bucket my-alb-logs \
    --account-id 123456789012 \
    --regions us-east-1,us-west-2

© 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 4 other files (scripts, references) in skills/performing-cloud-log-forensics-with-athena of mukul975/Anthropic-Cybersecurity-Skills.

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

Open the folder on GitHubat commit 54a7988

Compare with similar skills

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Performing Cloud Log Forensics With Athena compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
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Querying AWS Cloudwatchaws/agent-toolkit-for-aws2.8k—~3.5kAutomated safety check: PassApache-2.0
Redshift Guideaws/agent-toolkit-for-aws2.8k—~2.6kAutomated safety check: PassApache-2.0
Querying AWS Sagemaker Catalogaws/agent-toolkit-for-aws2.8k—~2.6kAutomated safety check: PassApache-2.0
Investigating AWS Incidentstrilwu/secskills157—~4.8kAutomated safety check: PassMIT

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  • NTFS MFT Deleted File Recovery

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Categories

Questions about Performing Cloud Log Forensics With Athena

What does Performing Cloud Log Forensics With Athena do?

Uses AWS Athena to query CloudTrail, VPC Flow Logs, S3 access logs, and ALB logs for forensic investigation. Performing Cloud Log Forensics With Athena is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Uses AWS Athena to query CloudTrail, VPC Flow Logs, S3 access logs, and ALB logs for forensic investigation.

When should I use Performing Cloud Log Forensics With Athena?

Performing Cloud Log Forensics With Athena fits situations like: investigating AWS security incidents; building cloud-native forensic workflows at scale.

How do I install Performing Cloud Log Forensics With Athena in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-cloud-log-forensics-with-athena -a claude-code`. Or copy the skill folder (skills/performing-cloud-log-forensics-with-athena in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/performing-cloud-log-forensics-with-athena in your project. Claude Code loads it when a task matches its description.

How do I install Performing Cloud Log Forensics With Athena in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-cloud-log-forensics-with-athena -a codex`. Or copy the skill folder (skills/performing-cloud-log-forensics-with-athena in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/performing-cloud-log-forensics-with-athena in your project. Codex loads it when a task matches its description.

Can I use Performing Cloud Log Forensics With Athena 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 performing-cloud-log-forensics-with-athena -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/performing-cloud-log-forensics-with-athena, .gemini/skills/performing-cloud-log-forensics-with-athena, .github/skills/performing-cloud-log-forensics-with-athena and .opencode/skills/performing-cloud-log-forensics-with-athena in your project.

What does Performing Cloud Log Forensics With Athena need to run?

Going by SKILL.md and its folder, Performing Cloud Log Forensics With Athena needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Performing Cloud Log Forensics With Athena 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 Performing Cloud Log Forensics With Athena 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 Performing Cloud Log Forensics With Athena use?

Performing Cloud Log Forensics With Athena 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 Performing Cloud Log Forensics With Athena use?

About 3.7k tokens (SKILL.md is roughly 15k 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 3.6k tokens, read only when the agent opens those files.

What are the alternatives to Performing Cloud Log Forensics With Athena?

Skills that share tags, products or a category with Performing Cloud Log Forensics With Athena: Querying Data Lake (aws/agent-toolkit-for-aws, 2.8k stars), Querying AWS Cloudwatch (aws/agent-toolkit-for-aws, 2.8k stars), Redshift Guide (aws/agent-toolkit-for-aws, 2.8k stars) and Querying AWS Sagemaker Catalog (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 Performing Cloud Log Forensics With Athena?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 33,993 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.