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This skill acts as a central router for configuring AWS WAF to secure web applications and APIs. It provides structured procedures for creating web ACLs, managing rule sets, implementing bot and fraud control, and configuring logging. The skill emphasizes a secure-by-design approach, requiring logging to be enabled before tuning and enforcing strict header-stripping to prevent spoofing. It covers the full lifecycle of WAF management, from initial setup and association with resources like CloudFront and ALB to advanced mitigation strategies for volumetric abuse and credential stuffing.
What it does
- Creates and associates web ACLs with CloudFront, ALB, API Gateway, and AppSync.
- Configures AWS Managed Rules, rate-based rules, and custom IP or geographic match rules.
- Implements Bot Control and Fraud Control to prevent account takeover and fake-account creation.
- Provides workflows for logging, request sampling, and tuning rules in Count mode.
- Enforces security best practices like header stripping and least-privilege IAM.
- Routes users to specific, self-contained procedures for complex WAF tasks.
When to use it
- When you need to protect a web application or API from layer 7 exploits and HTTP floods.
- When implementing bot detection or fraud prevention for login and signup endpoints.
- When you need to tune managed rules to reduce false positives without blocking legitimate traffic.
- When you need to recover the real client IP or secure forwarded headers behind a CDN.
- When you need to manage AI and LLM crawler traffic.
Advantages
- Reduces risk of misconfiguration by enforcing a logical, step-by-step tuning workflow.
- Prevents accidental blocking of legitimate traffic through mandatory Count-mode tuning.
- Ensures security consistency by mandating header-stripping when forwarding signals.
- Provides clear guidance on WCU budget management and rule group selection.
- Centralizes complex WAF procedures into a single, reliable reference source.
Usage examples
- “How do I set up AWS WAF to block HTTP floods on my Application Load Balancer?”
- The agent will guide you through creating a regional web ACL, associating it with your ALB, and configuring a rate-based rule to throttle requests based on IP or session token.
- “I am seeing false positives from the Core Rule Set. How do I fix this?”
- The agent will instruct you to check your WAF logs to identify the specific rule ID triggering the block, then use the RuleActionOverrides feature to set that specific rule to Count mode.
- “How can I protect my login page from credential stuffing?”
- The agent will guide you through enabling Fraud Control (Account Takeover Prevention) and configuring the necessary logging and signal-forwarding rules.
- “I need to allow traffic only from specific countries. What is the procedure?”
- The agent will provide the steps to create a geographic match rule and configure the web ACL's default action to block traffic outside of your allowed list.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/aws/waf/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip waf.zip -d .agents/skills
Documentation by AWS
AWS WAF
Overview
Domain expertise for configuring AWS WAF, the web application firewall that filters HTTP and HTTPS traffic to CloudFront distributions, Application Load Balancers, API Gateway REST APIs, and AppSync GraphQL APIs. Covers web ACL creation and association, AWS Managed Rules, rate-based rules, match rules (IP set and geographic), Bot Control and the signal-forwarding workflows built on top of it, Fraud Control for logins and signups, AI and LLM crawler management, and the logging that every tuning workflow depends on.
This skill is a router. Each customer task maps to a procedure file under references/. Read the
matching reference in full before acting, then follow its constraints and steps. The reference
files are self-contained: each carries its own decision tables, constraints, procedure, and
troubleshooting.
Execute commands using the AWS MCP server when connected (sandboxed execution, audit logging,
observability). Fall back to the AWS CLI otherwise. A web ACL's scope is fixed at creation: a
CloudFront web ACL must be created in us-east-1 with CLOUDFRONT scope, while a regional web ACL
(Application Load Balancer, API Gateway, AppSync) is created in the resource's Region with
REGIONAL scope.
Which WAF task do you need?
Routing notes
Logging comes before tuning. Every Count-mode tuning workflow assumes logging and request sampling are on. If the customer has not set up logging, run that reference first; otherwise Count-mode tuning has nothing to read.
Web ACL scope is fixed at creation. A CloudFront web ACL is
CLOUDFRONTscope inus-east-1; a regional resource needs aREGIONALweb ACL in its own Region. Scope cannot be changed later, so the creating reference settles it before anything is built.Bot Control is a chain, not one task. Protecting against bots is the on-ramp (turn on, choose Common vs Targeted, observe). Turning labels into a confidence signal, forwarding that signal, and deciding what the application does with it are three separate references that build on it in that order. The header-stripping reference is the mandatory safety companion whenever a signal is forwarded to the origin.
Common vs Targeted is not a soft choice. Common only catches self-identifying bots and known-bad IPs. For login, checkout, or any high-value endpoint facing evasive bots, Targeted with the application integration SDK is required. The bots reference pushes Targeted for real bot threats rather than presenting it as optional.
Rate limiting vs Fraud Control. Rate-based rules blunt volumetric HTTP floods. Credential stuffing and fake-account creation are account-based abuse that rate limiting misses; those go to the Fraud Control reference (ATP and ACFP), not the rate-based reference.
Forwarded headers need the strip rule. Any time the customer forwards a signal or the client IP to the origin in
x-amzn-waf-*headers, the inbound-header-stripping reference is required to prevent spoofing. The confidence-signal, interpolation, and client-IP references all point at it.What lives in other skills. L3/L4 DDoS protection and Shield cost-protection credits are the shieldadvanced skill. Multi-account WAF rollout is the firewallmanager skill. CloudFront and Application Load Balancer configuration are their own skills. This skill builds the WAF rules; it does not configure the resources it protects.
Security Considerations
AWS WAF is itself a security control, so misconfiguration directly weakens an application's defenses. Apply these across every reference:
Least-privilege IAM. You MUST grant only the specific
wafv2:actions a task needs (for examplewafv2:CreateWebACL,wafv2:GetWebACL,wafv2:UpdateWebACL,wafv2:AssociateWebACL,wafv2:PutLoggingConfiguration) rather thanwafv2:*or theAWSWAFFullAccessmanaged policy.Ephemeral credentials. You MUST use IAM roles with temporary credentials (such as an EC2 instance profile, SSO session, or
aws sts assume-role) rather than long-lived IAM user access keys when running these WAF CLI commands.Monitor configuration changes. You SHOULD enable AWS CloudTrail on
wafv2management events and set CloudWatch alarms on critical web ACL configuration changes (such asDeleteWebACLandUpdateWebACLrule removals) and on the web ACL'sBlockedRequestsandCountedRequestsmetrics, so rule changes and sudden spikes in blocked or counted traffic are detected.Misconfiguration opens access. A web ACL that is created but never associated, or one whose default action is left at
Allowwith no enforcing rules, filters nothing. You MUST confirm the web ACL is associated and that its posture matches the intended default (block vs allow) before reporting setup complete.Protect log destinations. Logs can capture credentials and session data. You MUST redact sensitive fields (such as the
authorizationheader andcookie) and MUST enable encryption at rest on the log destination (CloudWatch Logs, Amazon S3, or Amazon Data Firehose).Header-spoofing risk. Any
x-amzn-waf-*signal forwarded to the origin can be forged inbound. You MUST add the inbound-header-stripping rule whenever a signal or client IP is forwarded (see stripping-inbound-waf-headers-before-trusting-them).
Additional Resources
Files
- ATTRIBUTION.mdAdded by AutoGineer917 B
- LICENSELicence file added by AutoGineer9.9 KiB
- NOTICELicence file added by AutoGineer67 B
- SKILL.md8.6 KiB
- references/adaptive-mitigation-playbook-for-forwarded-signals.md8.9 KiB
- references/adding-managed-rules-and-tuning-with-count-mode.md10.1 KiB
- references/adding-rate-based-rules.md10.5 KiB
- references/creating-a-web-acl-and-associating-it-with-a-resource.md9.3 KiB
- references/forwarding-signals-with-dynamic-label-interpolation.md9.5 KiB
- references/protecting-against-bots-with-bot-control.md9.9 KiB
- references/protecting-logins-and-signups-with-fraud-control.md9.2 KiB
- references/recovering-the-real-client-ip-behind-a-cdn.md9.4 KiB
- references/seeing-and-managing-ai-crawler-traffic.md7.3 KiB
- references/setting-up-logging-and-request-sampling.md9.2 KiB
- references/stripping-inbound-waf-headers-before-trusting-them.md7.5 KiB
- references/turning-bot-control-labels-into-a-confidence-signal.md11.0 KiB
- references/using-ip-sets-and-geographic-match-rules.md8.5 KiB
references/protecting-against-bots-with-bot-control.md
Protecting Against Bots with Bot Control
Overview
Domain expertise for the AWS WAF Bot Control on-ramp: adding the Bot Control managed rule group, choosing Common versus Targeted, and observing in Count mode before enforcing. Covers the sharp difference between Common and Targeted, the application integration SDK as a precondition for Targeted, the machine learning warm-up, the verified-bot Count-override gotcha, and the added cost.
Does not cover what to do with the labels Bot Control produces; turning labels into a confidence signal, forwarding that signal, and the application's response are three separate references that build on this one.
Execute commands using the AWS MCP server when connected (sandboxed execution, audit logging, observability). Fall back to the AWS CLI otherwise.
Table of Contents
Overview
Workflow
Decision: Common vs Targeted
The SDK is a precondition for Targeted
Machine learning warm-up
Verified-bot Count-override gotcha
Added cost
Troubleshooting
Procedure
Security Considerations
Additional Resources
Workflow
To turn on Bot Control end to end, follow the procedure exactly. See the Procedure section below.
The procedure covers:
Adding the Bot Control rule group and choosing the inspection level
Running in Count mode to see how traffic is labeled
Keeping verified bots passing while tuning
Deciding which categories to block, challenge, or allow, then enforcing
This reference is the on-ramp. Turning the labels into an application decision is the turning-bot-control-labels-into-a-confidence-signal reference and the ones after it.
Decision: Common vs Targeted
| Level | Detects | SDK | Best for |
|---|---|---|---|
| Common | Self-identifying bots (user-agent such as curl, python-requests, declared crawlers) and known-bad IPs | Not required | Basic filtering of honest bots |
| Targeted | Adds behavioral machine learning, browser interrogation, and token session tracking | Strongly required | Login, checkout, any high-value endpoint facing evasive bots |
Constraints:
You MUST push Targeted for any real or evasive bot threat; Common alone is not meaningful protection against bots that impersonate a real browser (headless Chrome, Puppeteer, Selenium, residential proxies)
You MUST NOT present Targeted as an optional upgrade when the customer faces credential stuffing or inventory hoarding on a high-value endpoint
The SDK is a precondition for Targeted
Targeted's behavioral machine learning, browser interrogation, and token session tracking are largely blind without the application integration SDK or its JavaScript token.
Constraints:
You MUST treat the application integration SDK as a precondition for Targeted, not an optional add-on
You SHOULD confirm the SDK is integrated before relying on Targeted detection
Machine learning warm-up
The TGT_ML_* rules need up to roughly 24 hours to establish a traffic baseline. Customers enable
Targeted, see nothing fire immediately, and assume it is broken.
Constraints:
You MUST set the expectation that
TGT_ML_*rules need up to roughly 24 hours of warm-up before they actYou SHOULD advise against disabling Targeted during the warm-up window
Verified-bot Count-override gotcha
Bot Control does not block verified bots; it labels them. Overriding the whole rule group to Count while tuning also overrides the implicit Allow for verified bots, so they fall through to the customer's other rules.
Constraints:
You MUST add an explicit Allow rule on the
awswaf:managed:aws:bot-control:bot:verifiedlabel when tuning the group in Count, so verified bots keep passingYou MUST place that Allow rule at a higher priority number than the Bot Control group, so the verified-bot label exists when the Allow rule runs
You SHOULD rely on the verified-bot labeling rather than blanket-blocking all automated traffic
Added cost
Bot Control incurs additional fees beyond the basic AWS WAF charges.
Constraints:
You MUST state the additional cost before the customer adds the rule group, not after it appears on the invoice
Troubleshooting
Evasive bots still get through on Common
Common only catches self-identifying bots and known-bad IPs. Move to Targeted with the SDK (Decision: Common vs Targeted).
Targeted fires nothing right after enabling
The TGT_ML_* rules are still warming up. Wait up to roughly 24 hours (Machine learning warm-up).
Verified bots get blocked while tuning
Overriding the whole group to Count canceled the verified-bot Allow. Add an explicit Allow on the verified label (Verified-bot Count-override gotcha).
Procedure
Overview
This procedure adds Bot Control at the chosen level in Count mode, keeps verified bots passing, then enforces, and surfaces the console link.
Parameters
web_acl_name, web_acl_id, scope (required): Identify the web ACL.
inspection_level (required):
COMMONorTARGETED.
Constraints for parameter acquisition:
You MUST ask for all required parameters upfront in a single prompt
You MUST confirm logging is enabled and, for Targeted, that the application integration SDK is integrated
Steps
1. Verify dependencies
Constraints:
You MUST confirm credentials with
aws sts get-caller-identityYou MUST confirm logging and sampling are on
For Targeted, you MUST confirm the application integration SDK is in place
2. Add Bot Control in Count mode
Constraints:
You MUST add the Bot Control rule group at the chosen inspection level with the group in Count while observing
You MUST add an explicit Allow rule on
awswaf:managed:aws:bot-control:bot:verifiedso verified bots keep passing during tuningYou MUST fetch the current
LockTokenbeforeupdate-web-acland pass the full rule setYou MUST preserve the web ACL's existing
DefaultActionfrom theget-web-aclresponse and pass it back as{default_action}; do not assumeAllow={}, since that would silently open all unmatched traffic on a web ACL whose default action isBlock. For example, the Bot Control group at the chosen inspection level plus the verified-bot Allow rule:aws wafv2 get-web-acl --name {web_acl_name} --scope {scope} --id {web_acl_id} --region {region} aws wafv2 update-web-acl --name {web_acl_name} --scope {scope} --id {web_acl_id} \ --lock-token {lock_token} --default-action {default_action} \ --rules '[{"Name":"AWS-BotControl","Priority":1,"Statement":{"ManagedRuleGroupStatement":{"VendorName":"AWS","Name":"AWSManagedRulesBotControlRuleSet","ManagedRuleGroupConfigs":[{"AWSManagedRulesBotControlRuleSet":{"InspectionLevel":"{inspection_level}"}}]}},"OverrideAction":{"Count":{}},"VisibilityConfig":{"SampledRequestsEnabled":true,"CloudWatchMetricsEnabled":true,"MetricName":"AWS-BotControl"}},{"Name":"AllowVerifiedBots","Priority":2,"Action":{"Allow":{}},"Statement":{"LabelMatchStatement":{"Scope":"LABEL","Key":"awswaf:managed:aws:bot-control:bot:verified"}},"VisibilityConfig":{"SampledRequestsEnabled":true,"CloudWatchMetricsEnabled":true,"MetricName":"AllowVerifiedBots"}}]' \ --visibility-config SampledRequestsEnabled=true,CloudWatchMetricsEnabled=true,MetricName={web_acl_name} \ --region {region}
3. Review and enforce
Constraints:
You MUST review the labeled traffic (allowing for the Targeted warm-up) before enforcing
You MUST decide per category whether to block, challenge, or allow, then enforce
4. Surface the console link
Constraints:
You MUST present the web ACL console link and tell the customer to open the Bot Control rule and confirm its level and actions:
https://us-east-1.console.aws.amazon.com/wafv2/homev2/web-acls?region={region}
Example
Example input
{
"web_acl_name": "example-webacl",
"web_acl_id": "abc",
"scope": "REGIONAL",
"inspection_level": "TARGETED"
}Example output
Confirmed the SDK is integrated. Added Bot Control TARGETED in Count, with an explicit Allow on the verified-bot label.
TGT_ML rules need up to ~24h to warm up before acting.
Open the web ACL and confirm the Bot Control level and actions:
https://us-east-1.console.aws.amazon.com/wafv2/homev2/web-acls?region=us-east-1Troubleshooting
Evasive bots get through on Common
Move to Targeted with the SDK (Step 1).
Nothing fires right after enabling Targeted
The machine learning is warming up; wait up to roughly 24 hours (Step 3).
Verified bots blocked while tuning
Add the explicit Allow on the verified label (Step 2).
Security Considerations
This procedure modifies a security control, so misconfiguration directly weakens the application's defenses.
Least-privilege IAM. You MUST grant only the specific
wafv2:actions a task needs (for examplewafv2:GetWebACLandwafv2:UpdateWebACL) rather thanwafv2:*or theAWSWAFFullAccessmanaged policy.Ephemeral credentials. You MUST use IAM roles with temporary credentials (such as an EC2 instance profile, SSO session, or
aws sts assume-role) rather than long-lived IAM user access keys when running these WAF CLI commands.Monitor configuration changes. You SHOULD enable AWS CloudTrail on
wafv2management events and set CloudWatch alarms on critical web ACL configuration changes (such asDeleteWebACLandUpdateWebACLrule removals) and on the web ACL'sBlockedRequestsandCountedRequestsmetrics, so rule changes and sudden spikes in blocked or counted traffic are detected.
Additional Resources
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs an MCP server
Security
No issues found by our static scan· No issues found
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Attribution and licence
- Published by
- AWS
- Publisher
- AWS (Official)
- Source
- aws/agent-toolkit-for-aws · skills/specialized-skills/networking-and-content-delivery-skills/waf · commit 0a218f8
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- NOTICE (added by AutoGineer from NOTICE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by AWS.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download waf.zip