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Check a Kiln branch or pull request through real browser interactions. Multiple agents test separate feature areas in isolated environments, then combine findings into a severity-ranked Markdown report with reproduction steps and expected versus actual behavior.
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Review vulnerability findings before drafting a bug bounty report. Seven validation questions and submission checklists help assess scope, reproducibility, demonstrated impact, duplicate reports, and whether the claimed severity matches the available evidence.
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Turn validated vulnerabilities into clear bug bounty reports for HackerOne, Bugcrowd, Intigriti, or Immunefi. Platform templates help organize demonstrated impact, reproducible steps, supporting evidence, severity reasoning, and concrete fixes in language reviewers can follow.
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Triage a raw bug report into something a team can act on — clean repro steps, a defensible severity/priority, environment, likely area/owner, and duplicate check. Use when asked to triage this bug, set severity and priority, is this a P1, or clean up this bug report for the backlog. Produces the normalized repro, a severity and priority with the reasoning (impact × frequency × workaround), the environment/metadata, a suspected component and owner queue, and a duplicate/related-issue check — flagging when info is missing rather than guessing.
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Production incident response. Use when handling incidents, classifying severity, reconstructing timelines, writing postmortems, generating comms templates, or building response playbooks with severity scoring and RCA frameworks.
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Systematic QA testing for web applications, desktop apps, and workflows. Three tiers: QUICK (critical/high severity only — 5 min), STANDARD (+ medium severity — 15 min), EXHAUSTIVE (+ cosmetic — 30+ min). Tests user flows, finds bugs, reports with severity classification, and optionally auto-fixes with atomic commits. Produces before/after health scores and ship-readiness summary. Use when user says "test this", "QA", "find bugs", "does this work?", or "is it ready to ship?". Voice triggers: "quality check", "run QA", "test the app". For report-only (no fixes), use with --report-only flag.
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从指令、记忆、工具执行和界面显示等十二个层面排查人工智能代理故障，依据代码和日志梳理原因，按严重程度列出问题并提出修复方案。
获取全球机场的延误与取消航班信息，查看延误类型、严重程度、平均延误时间和受影响航班比例，帮助在出发前了解机场整体运行情况。
利用Cloudflare Radar的信息查询国家或地区的互联网故障，结合原因、严重程度、检测时间和结束状态，了解网络中断或封锁情况。
获取洪水、气旋、干旱、热浪等灾害记录，查看所在国家、严重程度及受灾人口，并结合气候异常和相关新闻了解事件背景。
通过三项静态审计检查架构一致性、威胁及MCP权限和依赖，保存带时间戳的记录。可定期检查变化，并在发现达到指定严重程度的问题时使CI任务失败。
Audit a codebase across nine areas, including architecture, tests, security, and documentation. Produces TECH_DEBT_AUDIT.md with source locations, severity ratings, estimated repair effort, and prioritized recommendations to help plan maintenance work.
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Audit a codebase with three vulnerability hunters and two engineers who build small demonstrations of suspected flaws. Verify attack paths, assess severity from demonstrated impact, and report evidence, targeted fixes, and checks to prevent recurrence.
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Integrate harmful-content checks into Python applications using Azure AI Content Safety. Analyze text and images by category and severity, and manage custom text blocklists to help review user submissions or AI-generated content before display.
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Build content moderation features in Java with Azure AI Content Safety. Analyze text and images for harmful content, retrieve severity scores by category, and manage custom blocklists, which are lists of terms to flag.
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Design production alerts, route notifications by severity, and organize on-call schedules. Use configuration examples to define backup contacts, escalation steps when alerts go unanswered, and runbooks that guide responders through incident investigation.
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Review a pull request or code diff for bugs by reading callers, expected behavior, and nearby error handling. Receive findings tied to specific lines, ranked by severity, plus a summary of merge risks and needed tests.
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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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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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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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Build dashboards that show how long vulnerabilities remain unresolved and compare remediation times with severity-based deadlines. Define response policies, automate escalation notices, and report compliance rates and other measures of remediation progress.
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Design a security operations center escalation matrix that defines incident priorities, response deadlines, analyst responsibilities, and notification paths. Use business impact, asset importance, and data sensitivity to decide when incidents need senior or management attention.
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Review a diffusers contribution before opening a pull request. Check the full change against project rules, trace potentially unused code, and receive a severity-based report separating required fixes from points to discuss with reviewers.
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Present results returned by a Codex helper while preserving findings, severity, file references, and uncertainty. This internal guidance also covers reporting edited files, failed runs, and setup requirements, with user confirmation before fixing review findings.
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