测试与安全技能

Guide the design and review of software that handles personal data using GDPR engineering practices. Covers minimal data collection, access controls, logging, encryption, retention, deletion, and checks for code changes and infrastructure.

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• Reviewing APIs and data models that handle personal data
• Checking logs for sensitive information
• Designing retention and user data deletion processes

Review GitHub Actions workflows for unsafe input handling, excessive token permissions, exposed secrets, and changeable action references. Get findings grouped by issue type, with severity ratings, exact YAML locations, and concrete fixes to review.

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• Reviewing CI workflows for security issues
• Checking privileged triggers that run fork code
• Restricting GITHUB_TOKEN permissions

Build an end-to-end evaluation pipeline for Python AI applications using pixie-qa and real language-model calls. Define criteria, prepare realistic test data, score application runs, and analyze the evidence to produce a prioritized improvement plan.

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• Setting up quality checks for Python AI applications
• Creating realistic scenarios for known failure modes
• Scoring full application runs with real language-model calls

Write focused C# unit tests with XUnit, including tests that run against multiple input datasets. Apply consistent naming, setup, assertions, shared fixtures, and mock dependencies to keep tests readable, independent, and organized.

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• Set up an XUnit test project for C#
• Test multiple input cases with Theory and data attributes
• Check exceptions and isolate dependencies with mocks

Write C# unit tests with NUnit using clear naming, focused assertions, and independent test cases. Covers project setup, testing with multiple input values, exception checks, and replacing dependencies with mocks to test components in isolation.

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• Setting up an NUnit test project
• Testing the same behavior with multiple inputs
• Checking exceptions and collection contents

Create integration tests for .NET database access before migrating from Oracle to PostgreSQL. The tests record expected Oracle behavior with controlled test data and portable checks, providing a baseline for later migration validation.

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• Recording Oracle behavior before migration
• Testing repository methods and stored procedure calls
• Checking empty strings, NULL inputs, and datetime precision

Write C# unit tests with TUnit using clear test structure, asynchronous checks, and multiple input cases. Get guidance on setup and cleanup, execution controls, test organization, and converting existing xUnit tests to TUnit.

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• Setting up a TUnit test project
• Testing one behavior with multiple input cases
• Organizing test setup and cleanup

Configure GitHub Dependabot to keep dependencies, the packages a project relies on, updated. Organize update pull requests into groups, set schedules and exclusion rules, and configure security updates across projects with multiple packages or package systems.

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• Create or revise dependabot.yml
• Group dependency updates into fewer pull requests
• Configure updates across a monorepo

Assess potential data breach impact by reviewing sensitive data and its movement through a codebase. Produce an inventory, exposure rankings, financial estimates, and prioritized safeguards, distinguishing legal source figures from planning estimates.

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• Inventory sensitive data before a security review
• Trace where customer data could leak
• Estimate breach impact and costs for planning

Write and improve C# unit tests with MSTest 3.x/4.x. Covers assertions that check expected results, tests with multiple input sets, asynchronous setup, cleanup, and test organization, with guidance on APIs that differ between versions.

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• Create a C# unit test project
• Test multiple input sets with DataRow or DynamicData
• Update exception checks to modern assertion APIs

Turn defects found during Oracle-to-PostgreSQL migration into structured bug reports. Document expected and actual behavior, reproduction steps, severity, root causes, proposed changes, and validation on both databases so teams can act on the findings.

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• Documenting behavioral differences found during migration
• Organizing reproduction steps and test data
• Recording fixes, affected files, and validation steps

Convert feature requirements and implementation plans into a test strategy, task checklist, and quality assurance plan for GitHub projects. Define test coverage, priorities, dependencies, estimates, and quality checkpoints using established testing and software quality frameworks.

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• Plan testing from feature requirements and implementation documents
• Break testing work into tasks for GitHub issue management
• Prioritize performance, security, and accessibility checks

Document bug fixes and incident recovery with a structured receipt covering the problem, cause, change, and verification evidence. Distinguishes verified results from partial or blocked work, identifies missing proof, and specifies the next check needed to close the gap.

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• Record the evidence supporting a bug fix
• Report missing checks after a partial repair
• Document an incident blocked by an external condition

Control and inspect a live Chrome browser through Chrome DevTools MCP. Automate page interactions, capture screenshots, inspect errors and network requests, and record performance data to investigate slow loading and unexpected layout changes.

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• Automating navigation and form entry
• Capturing page screenshots and text snapshots
• Investigating console errors and failed requests

Set up CodeQL security scanning in GitHub Actions or with the local CLI. Get guidance on language settings, build steps, analysis rules, result uploads, and troubleshooting so you can configure scans and interpret their findings.

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• Adding security scans to GitHub Actions
• Running local scans with the CodeQL CLI
• Investigating failed builds or result uploads

Turn incomplete or intermittent bug reports into a minimal reproduction backed by recorded evidence. Capture the environment, expected and actual results, commands, and remaining unknowns in a brief before diagnosis or repair.

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• Clarifying an incomplete bug report
• Recording how often an intermittent failure occurs
• Documenting an environment-specific failure

Review an iOS app’s code, settings, and submission materials for App Store rejection risks. Receive prioritized findings with supporting evidence, concrete fixes, verification steps, and draft notes to help reviewers test the app.

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• Check an iOS app before App Store submission
• Review privacy, permissions, and in-app purchases
• Investigate rejection risks in login and user content flows

Check AI agent plugins and tools for changed, missing, or untracked files using SHA-256 file fingerprints. Generate integrity records, review dependency version settings, and check requirements before promoting a plugin to production.

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• Generate integrity records for plugin files
• Check third-party tools for file changes
• Audit dependency version settings

Review an AI agent codebase against ten OWASP ASI security risk areas. Examine tool permissions, input validation, identity checks, audit logs, and other controls, then summarize coverage, gaps, and recommended improvements in a report.

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• Reviewing agent security before production deployment
• Mapping existing controls to ten security risk areas
• Preparing findings for a security review or audit

Compare C# Web API source code with its TypeScript or JavaScript clients to find mismatched data fields, required inputs, and response handling. Receive an audit report showing client impact, severity, and confidence in each match.

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• Check client impact before merging a DTO or controller change
• Audit API compatibility before a release
• Find stale request fields and missing response fields

Add controls to AI agents that use APIs, databases, and files. Use Python patterns for tool permissions, approval requirements, call limits, suspicious input checks, audit logs, and trust scores that guide delegation between agents.

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• Restrict which tools an agent can call
• Check suspicious inputs before execution
• Require human approval for sensitive operations

Check security audit findings against code evidence and trace how untrusted input moves through a system. Guide a second review, document verification gaps, and assess draft reports against a minimum quality score before delivery.

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• Verifying evidence behind security review findings
• Rechecking input flows after an initial analysis
• Assessing report quality before delivery

Review AI prompts for harmful output risks, bias, privacy concerns, security weaknesses, and clarity. Receive an assessment, a revised prompt, and suggested tests with explanations of the principles behind each improvement.

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• Review a prompt before putting it into use
• Check wording for stereotypes and bias
• Assess data exposure and prompt injection risks

Check whether a feature, fix, or refactor actually works using existing tests, build checks, direct commands, or manual validation. Get a concise account of the evidence, including what passed, what failed, and what remains unverified.

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• Checking that a bug fix works
• Verifying a newly added feature
• Checking behavior after a refactor