当 AI 写完测试、要决定信不信这份测试时使用。查三类假信心:断言与实现是否同源复制、有没有边界与异常用例、失败信号够不够强;再规划 2–5 个突变抽样,能执行时再实跑,看把被测代码改坏之后测试会不会变红,没实跑就不声称测试抓得住错。测试全绿不等于代码对,也可能是测试根本抓不住错。要决定整份 diff 能不能合并时走 ai-diff-review-protocol。
日本語の概要は準備中です。原文の説明を表示しています。
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概要と使いどころ
当 AI 写完测试、要决定信不信这份测试时使用。查三类假信心:断言与实现是否同源复制、有没有边界与异常用例、失败信号够不够强;再规划 2–5 个突变抽样,能执行时再实跑,看把被测代码改坏之后测试会不会变红,没实跑就不声称测试抓得住错。测试全绿不等于代码对,也可能是测试根本抓不住错。要决定整份 diff 能不能合并时走 ai-diff-review-protocol。
日本語の概要は準備中です。原文の説明を表示しています。
当拿到一段 AI 生成的代码,还没决定要不要信、要不要跑、要不要往上继续加东西时使用。先做一遍客观体检:行数与嵌套深度、有没有测试、有没有硬编码密钥或遗留 TODO、依赖是不是真实存在、有没有危险调用(eval/exec、shell=True、递归删除、反序列化、提权、外发网络,按正文给的 grep 模式逐类查);再给风险评级和下一步动作。老实写明它查不了算法正确性——体检通过不等于代码是对的。回答里文字、事实和代码混在一起时先用 ai-answer-triage 分级;只审相对已有代码的一份改动用 ai-diff-review-protocol。
日本語の概要は準備中です。原文の説明を表示しています。
当 AI 改完代码、要决定这份 diff 放不放行时使用。不逐行精读,按四步走查高风险点——意图是否匹配、有没有碰到边界条件、副作用面有多大、能不能回滚——每步有明确的放过与打回判据。改动文件数、增删行数、动到的迁移/锁文件/CI/配置/鉴权/测试文件和危险 hunk 先由 scripts/diff_risk.py 统计(粘贴进来的 diff 文本也能读,不需要 Git),越过固定阈值时必须人工逐段过。只有口头描述、没有改动内容时不给结论,只列要补的材料。diff 里新写的测试可不可信交给 ai-generated-test-auditor;没有基线的整段新代码走 ai-code-onboarding-checklist。
日本語の概要は準備中です。原文の説明を表示しています。
当使用者说“建模比赛三个人怎么分工”“帮我们排协作节奏”“模型、代码和论文总对不上”“截止前谁做什么”“怎么交接和复核”时使用。根据真实成员能力、可用时间、交付物依赖与截止约束,按产物和决策分配 owner/reviewer,建立单一事实源、关键路径、九个质量闸门、交接包、冲突与回退规则;不猜成员能力、不平均分任务、不伪造会议、贡献或独立审查,不让未验证结果直接进入论文。
日本語の概要は準備中です。原文の説明を表示しています。
Investigate why a defect or insufficiency was introduced and accepted by tracing code, pull requests, task context, and accessible development sessions. Use for second-order learning after a fix, blameless root-cause analysis, or preventing a recurring class of engineering mistakes.
日本語の概要は準備中です。原文の説明を表示しています。
Design, refine, implement, or review application UI and user journeys with minimal controls, clear actions, shared components, realistic prototypes, and polished behavior. Use for product screens, forms, onboarding, navigation, settings, and chat/task interfaces; not backend-only work or general graphic design.
日本語の概要は準備中です。原文の説明を表示しています。
Rewrite AI-sounding text so it reads like the writer without changing what it says. Use when editing or reviewing prose for AI tells: not-X-but-Y contrasts, one-line closers, staged openers, forced triads, dashes everywhere, inflated claims, sales language, stock AI words, bold labels, or filler. Based on Wikipedia's "Signs of AI writing."
日本語の概要は準備中です。原文の説明を表示しています。
Delegate independent research, implementation, or verification in parallel when it can save time after dispatch and review overhead, or when explicitly requested. Keep decisions and final integration in the main agent.
日本語の概要は準備中です。原文の説明を表示しています。
Manual-only orchestrator for medium-to-large software development. Turns a broad goal into PR-sized and operational nodes with verification gates, then coordinates approved execution across follow-up prompts. Activate only when the user explicitly invokes my-epic; once activated, keep orchestrating that epic for the rest of the session. Do NOT use for an uninvoked planning or implementation request, a single small bug fix, one obvious PR, pure code review, ordinary SDD work that fits in one spec/PR, or implementing an epic during the same first call that created it.
日本語の概要は準備中です。原文の説明を表示しています。
Disciplined diagnosis workflow for hard, intermittent, or multi-component bugs and performance regressions: inspect logs and existing evidence, build a feedback loop, test falsifiable hypotheses, instrument gaps, and verify the outcome. Use when the root cause is unclear, reproduction is unreliable, several causes or components are plausible, or performance has regressed. Do NOT use for obvious single-cause errors, straightforward configuration or syntax fixes, planned feature work, or broad architecture review. Diagnosis-only requests stop after reporting the root cause and evidence; apply a fix only when the user requested one.
日本語の概要は準備中です。原文の説明を表示しています。
Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs. Uses pandas for behavioral analytics and statistical baselines. Use when investigating insider threats or building user behavior analytics for data loss prevention.
日本語の概要は準備中です。原文の説明を表示しています。
Detect and prevent QR code phishing (quishing) attacks that bypass traditional email security by embedding malicious URLs in QR code images within emails.
日本語の概要は準備中です。原文の説明を表示しています。
Detects and prevents code injection attacks targeting serverless functions (AWS Lambda, Azure Functions, Google Cloud Functions) through event source poisoning, malicious layer injection, runtime command execution, and IAM privilege escalation via function modification. The analyst combines static analysis of function code, CloudTrail event correlation, runtime behavior monitoring, and IAM policy auditing to identify injection vectors across the expanded serverless attack surface including API Gateway, S3, SQS, DynamoDB Streams, and CloudWatch event triggers. Activates for requests involving Lambda security assessment, serverless injection detection, function event poisoning analysis, or serverless privilege escalation investigation.
日本語の概要は準備中です。原文の説明を表示しています。
Configures Fail2ban with custom filters and actions to detect port scanning activity, SSH brute force attempts, and network reconnaissance, automatically banning offending IP addresses and alerting security teams to suspicious network probing.
日本語の概要は準備中です。原文の説明を表示しています。
Detect and prevent privilege escalation in Kubernetes pods by monitoring security contexts, capabilities, and syscall patterns with Falco and OPA policies.
日本語の概要は準備中です。原文の説明を表示しています。
Detect and prevent ARP spoofing attacks using ARPWatch, Dynamic ARP Inspection, Wireshark analysis, and custom monitoring scripts to protect against man-in-the-middle interception.
日本語の概要は準備中です。原文の説明を表示しています。
Systematically deobfuscate multi-layer PowerShell malware using AST analysis, dynamic tracing, and tools like PSDecode and PowerDecode to reveal hidden payloads and C2 infrastructure.
日本語の概要は準備中です。原文の説明を表示しています。
Detecting exposed AWS credentials in source code repositories, CI/CD pipelines, and configuration files using TruffleHog, git-secrets, and AWS-native detection mechanisms to prevent credential theft and unauthorized account access.
日本語の概要は準備中です。原文の説明を表示しています。
Detect and prevent API enumeration attacks including BOLA and IDOR exploitation by monitoring sequential identifier access patterns and authorization failures.
日本語の概要は準備中です。原文の説明を表示しています。
Configure microsegmentation policies to enforce least-privilege workload-to-workload access using tools like VMware NSX, Illumio, and Calico, preventing lateral movement in zero trust architectures.
日本語の概要は準備中です。原文の説明を表示しています。
A Certificate Authority (CA) is the trust anchor in a PKI hierarchy, responsible for issuing, signing, and revoking digital certificates. This skill covers building a two-tier CA hierarchy (Root CA +
日本語の概要は準備中です。原文の説明を表示しています。
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.
日本語の概要は準備中です。原文の説明を表示しています。
Identifies and unpacks UPX-packed and other packed malware samples to expose the original executable code for static analysis. Covers both standard UPX unpacking and handling modified UPX headers that prevent automated decompression. Activates for requests involving malware unpacking, UPX decompression, packer removal, or preparing packed samples for analysis.
日本語の概要は準備中です。原文の説明を表示しています。
Analyzes encryption algorithms, key management, and file encryption routines used by ransomware families to assess decryption feasibility, identify implementation weaknesses, and support recovery efforts. Covers AES, RSA, ChaCha20, and hybrid encryption schemes. Activates for requests involving ransomware cryptanalysis, encryption analysis, key recovery assessment, or ransomware decryption feasibility.
日本語の概要は準備中です。原文の説明を表示しています。