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Implements GDPR Art. 22 automated decision-making and AI Act Art. 14 human oversight requirements for AI systems. Covers identification of solely automated decisions, meaningful human intervention design, logic explanation mechanisms, and contestation procedures. Keywords: Art. 22, automated decision, human oversight, AI Act, profiling, contestation.

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Manages GDPR Article 22 rights related to solely automated decision-making and profiling, including identification of automated decisions, meaningful human oversight implementation, logic explanation requirements, and contestation mechanisms. Activate for automated decision, profiling, Art. 22, algorithmic decision, AI decision queries.

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Add end-to-end tests, which check complete usage flows, and API tests for features that already work. Follow the project's existing test setup to extend automated coverage without introducing a separate testing approach.

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• Adding automated tests to an existing feature
• Testing complete usage flows with E2E tests
• Adding API tests using the existing test setup

Create automated shell script tests with Bats, a Bash testing tool. Check exit codes, output, and failure cases, organize reusable test helpers and data, and run checks across shells or in CI/CD pipelines.

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• Add automated tests to shell scripts
• Check invalid input and missing file errors
• Test behavior in bash, sh, and dash

Conducts a Data Protection Impact Assessment for automated decision-making and profiling systems under GDPR Article 35(3)(a), covering algorithmic transparency, meaningful human oversight, contestation mechanisms, and Art. 22 safeguards. Activate for DPIA automated decision, profiling DPIA, algorithmic impact assessment, Art. 35(3)(a), ADM risk assessment queries.

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Review and add E2E tests, which check complete user journeys such as signing in or checking out. Identify missing coverage, organize reusable tests, and check that automated delivery workflows run them and handle failures.

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• Checking test coverage for sign-in and checkout flows
• Adding tests for form submissions
• Reviewing automated test runs and failure handling in CI/CD

Check website behavior across Chrome, Firefox, Safari, and Edge, and fix browser-specific problems. Review desktop and mobile test coverage alongside CI, the automated checks run during development, to identify gaps that let regressions pass.

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• Checking layouts and features across major browsers
• Reviewing desktop and mobile browser test coverage
• Finding gaps in automated browser checks

Design automated email sequences for new subscribers, prospective buyers, product users, and inactive customers. Get a flow with entry triggers, timing, exit conditions, subject lines, full email copy, clear next steps, and a measurement plan.

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• Create a welcome series for new subscribers
• Plan educational emails for prospective buyers
• Write product onboarding email sequences

Set up automated security checks that inspect source code without running the application. Configure Semgrep, SonarQube, or CodeQL, add project-specific rules, reduce false alarms, and define criteria for blocking problematic changes.

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• Adding automated security scans to CI/CD
• Creating security rules for project-specific code patterns
• Reducing false alarms and scan time

Build automated responses to AWS GuardDuty security findings using EventBridge and Lambda. Configure severity filters, isolate EC2 instances, disable compromised access keys, preserve disk snapshots for investigation, and send notifications to security teams.

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• Triggering responses to high-severity GuardDuty findings
• Isolating EC2 instances and preserving investigation data
• Disabling compromised IAM access keys

Set up and operate Amazon GuardDuty to detect threats in AWS. Configure protection features, centralize findings across accounts, investigate suspicious activity, and build automated responses with EventBridge and Lambda, including containment of potentially compromised resources.

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• Setting up threat detection for AWS accounts
• Investigating credential abuse and compromised instances
• Automating responses to high-severity findings

Set up repeatable security tests for LLM apps with Promptfoo and DeepTeam. Connect them to automated build pipelines, detect renewed vulnerabilities to malicious instructions or data leakage, and compare results across model or prompt changes.

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• Running automated attack tests on every code change
• Blocking merges when security tests fail
• Comparing security across model or prompt versions

Build a GitLab CI/CD pipeline with automated checks for vulnerable code, dependencies, container images, running applications, and exposed secrets. Covers scanner configuration, consolidated findings, and approval policies for serious vulnerabilities before production deployment.

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• Add automated security scans to .gitlab-ci.yml
• Check dependencies and container images for known vulnerabilities
• Detect credentials and API keys committed to source control

Create automated workflows that add threat intelligence to suspicious IP addresses, domains, URLs, and file hashes. Combine findings from multiple services into consistent alert context and confidence scores to support analysts reviewing security alerts.

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• Adding threat context to security alerts
• Processing indicators from phishing email submissions
• Standardizing bulk indicator lookups from threat feeds

Build an automated workflow that collects suspicious files, checks known malware records, and submits unknown samples to isolated analysis environments. Generate verdicts and indicators of compromise, then send results to security monitoring systems.

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• Automating analysis of quarantined email attachments
• Batch-processing files quarantined by endpoint security
• Extracting threat indicators during incident investigations

Connect TestRail test cases with Playwright automated tests. Generate tests from documented steps, send execution results back, create test runs, and check which cases remain unlinked using case IDs recorded in each test.

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• Generating Playwright tests from TestRail cases
• Sending automated test results to TestRail
• Creating a test run for regression testing

Create a game development test plan for a sprint, feature, or individual task. Classify the work and specify automated tests, manual checks, basic functionality checks, and playtest needs from documented requirements.

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• Plan testing before a sprint starts
• Separate automated tests from manual checks
• Define basic checks before a QA handoff

Publish or update a NemoClaw pull request and track automated checks and reviews through completion. It preserves the requested scope, coordinates repairs for valid findings, and reports the latest commit’s results and remaining blockers.

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• Publishing implemented changes as a pull request
• Updating an open pull request after review feedback
• Tracking automated checks and reviews on the latest commit

Design systems that use language models to judge AI responses. Covers scoring criteria, comparisons between two answers, bias reduction, confidence calibration, and checks against human judgments to make automated quality assessments more consistent and traceable.

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• Compare responses from different models
• Run A/B tests for prompt changes
• Create shared scoring rubrics

Recover from failed automated file creation with an explicit shell workflow. The skill guides agents through checking folders, writing and running scripts, and inspecting output files to confirm that deliverables were actually created.

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• Recovering from repeated automated execution failures
• Checking where output files were saved
• Verifying generated PDF and Word files

X (Twitter) DM automated chat end-to-end Skill: scan DM inbox to identify pending-reply conversations, read message history, generate persona-based replies and send; also supports searching users and starting new conversations. Built-in E2E passcode unlock, DM permission filtering, and rate control. Use when user mentions X auto-reply DMs, Twitter DM automated chat, auto-handle unread DMs, reply to X private messages with persona, X DM outreach campaign, batch send DMs to Twitter users, auto-process pending DM replies, Twitter DM bot, automated Twitter outreach, X direct message automation.

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Guides continuous privacy compliance monitoring implementation including automated control testing, evidence collection automation, real-time compliance dashboards, alert-based remediation workflows, regulatory change integration, and deviation management. Covers GRC platform configuration, control framework mapping, and compliance-as-code approaches. Keywords: continuous compliance, automated monitoring, evidence collection, dashboard, regulatory change, compliance-as-code.

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Implements profiling restrictions for children under GDPR Recital 71, Article 22, UK AADC Standard 12, and COPPA. Covers prohibition of behavioural advertising to children, recommendation algorithm limitations, nudge technique prohibition, and automated decision-making safeguards. Keywords: profiling, children, behavioural advertising, recommendation algorithm, AADC, automated decision.

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Implements automated data deletion workflows for GDPR Article 17 right to erasure and retention period expiry. Covers cascading deletion across dependent systems, dependency handling for referential integrity, confirmation logging, and audit trail generation. Activate for automated deletion, erasure automation, data purge, retention expiry queries.

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