Test design, vulnerability assessment, and quality assurance (13.1K skills)
Test app interfaces on an Android emulator using ADB, a command-line device tool. Inspect element positions, perform taps and text entry, then check screenshots and React Native logs to verify behavior and investigate layout or interaction bugs.
Review an API request against the supplied specification without sending it. Check the method, URL, headers, body, authentication format, and query parameters, then receive a brief verdict or a specific correction.
Guide authorized security assessments of REST, SOAP, and GraphQL APIs. Organize endpoint discovery, authentication checks, access control tests, and input testing, then document discovered weaknesses and evidence of unauthorized data access.
Plan security controls for LLM applications and AI agents, covering malicious instructions, data leakage, model tampering, and API access. Includes guidance and code examples for filtering outputs, enforcing permissions, isolating networks, and recording audit events.
Investigate failed Agent QA runs using logs, recorded artifacts, and local source code. Make a focused code or test-definition repair, then verify the affected behavior while keeping product and infrastructure defects visible.
Define security controls for AI coding agents used by a team. Covers command and file permissions, secret scanning, isolated execution environments, code review gates, and audit trails, with configuration examples to adapt.
Investigate failed Agent QA runs using recorded steps, logs, and saved evidence. Classify each failure, explain confidence and supporting findings, and identify the likely area to fix and the next action.
Configure permission checks for unattended Antigravity CLI runs. The setup combines blocking rules, automatic approval for scoped operations, AI classification for ambiguous calls, and single-use approval tokens to control tool access within a verified workspace.
Review GitHub Actions workflows that run AI agents, tracing how external input reaches prompts and tools. Produce findings with evidence, severity, impact, and remediation guidance through static analysis, which examines configuration without running attacks.
Plan structured adversarial tests for AI applications to assess safety bypasses, data leaks, and unauthorized tool use. Organize test prompts, record findings, and prioritize fixes using reporting guidance and an adaptable testing template.
Design layered protections for AI agents against malicious instructions, tool misuse, and data leakage. Provides guidance and code examples for input checks, isolated tool execution, permission limits, and audit logging to help review and strengthen agent security.
Compare prompt revisions on a fixed set of test cases to identify regressions, meaning behavior that gets worse after a change. Record model settings, case-level evidence, missing results, costs, and evaluation limits for a reviewable decision.
Create and edit Agent QA tests, test suites, and hooks, then validate their structure and tool-generated IDs before running them. Use MCP, an agent tool connection, or the CLI within a configured workspace.
Plan reviews of AI-generated code before running or merging it. Define language-specific checks, tests in isolated environments, and evidence requirements, while recording coverage limits and gaps that prevent approval.
Audit web or mobile pages and user journeys against an agreed WCAG accessibility standard. Combine automated scans with manual checks to document barriers, prioritize fixes, and record retest results alongside any remaining gaps.
Build repeatable evaluations for AI agent responses, tool choices, and multi-step tasks. Use reference datasets and scoring criteria to compare changes, detect quality regressions, and set automated checks that must pass before deployment.
Plan periodic access reviews for AWS IAM, GitHub, Okta, and other identity systems. Provides guidance for identifying unused access, assigning reviewers, documenting approval or removal decisions, and organizing evidence for compliance audits.
Check web pages and components for accessibility issues against WCAG 2.2, web accessibility guidelines. Receive a report prioritized by user impact, or request targeted code fixes followed by verification of resolved and newly introduced issues.
Review code and infrastructure for security risks, map potential threats, and plan stronger defenses. The skill covers OWASP checks, attack and defense exercises, and incident response, with validation required for each environment.
Turns supplied AI agent evaluation results into reports that distinguish autonomous success, human-assisted success, failure, timeout, and invalid runs. Makes metric denominators, retries, timing, costs, and comparison conditions explicit to support evidence-based decisions.
Compare accessibility findings between two versions of a web page. Identify newly introduced and fixed issues, count existing findings, and report evidence and suggested fixes without editing application code.
Compare agent, prompt, or tool configuration changes using versioned test cases and explicit outcome checks. Record each attempt, distinguish agent failures from evaluation or infrastructure problems, and report regressions, permission violations, and uncertainty.
Test how agent workflows recover from repeatable simulated failures, including tool timeouts, worker restarts, and lost memory. Produces a fault matrix, event timeline, and verdict showing whether state, safety boundaries, and retry budgets were preserved.
Support authorized security assessments of Microsoft Active Directory with guidance on environment discovery, credential risks, authentication weaknesses, and privilege escalation. The material organizes tools and workflows for penetration testing and controlled training environments.