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End-to-end ABM program design and orchestration for B2B SaaS mid-market and enterprise motions. Use when building a target account list, designing account tiering criteria, planning ABM channel orchestration, coordinating BDR outreach with marketing campaigns, defining ABM measurement frameworks, or running an ABM program review. Also use when asked to "build an ABM program," "set up account-based marketing," "design a target account strategy," "plan ABM campaigns," or "measure ABM results." Designed for demand gen teams selling to Mid-Market companies with $10K-$50K+ ACV, multi-persona buying committees, and 3-6 month sales cycles.

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Produces and audits externally published marketing copy where factual claims must survive scrutiny — landing pages, product pages, launch emails, one-pagers, search/social metadata. Every statistic, competitor comparison, capability description, and proof point is traced to a fetched source or the project's own documents, labeled, and adversarially verified before it ships. Use when writing or rewriting high-stakes marketing copy; when copy must pass legal, compliance, brand, or claims review; when the audience is skeptical technical or executive buyers; when the company has little or no customer proof yet (pre-launch, zero customers); or when auditing existing copy for unsupported claims, invented capabilities, unverified statistics, or AI-slop language.

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Use when edit_blueprint.yaml and selects_candidates.yaml are ready and the user asks to compile the rough cut, generate timeline.json, or apply a review patch.

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。

Select an evidence-backed platform delivery profile, preserve organic and ads scope boundaries, and produce safe-zone QA input with an explicit human preview gate when evidence is unknown or stale.

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。

Use when the user asks to modify an existing rough cut — shorten, lengthen, remove a clip, reorder, change BGM, adjust pacing. Works by generating a review_patch and re-compiling.

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。

Apply repeatable dialogue MA, Studio-based or automatic portrait reframing, overlay-safe rendering, and loudness/sync QA to an existing interview or talking-head rough cut. Use when the user asks to make speech easier to hear, perform MA, enlarge or rebalance a person in frame, crop or punch in an interview, automatically propose framing, or prepare a finishing preview before final approval.

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当学生说"这周过得挺乱的"、"帮我复盘一下这周"、"下周该怎么调整"时使用。先把这一周实际发生的事读出来（计划了什么、完成了什么、拖了什么），基于事实而不是感受直接写出一版完整复盘，同时用选择题把"卡在哪"一次问清楚，拿到答案后定稿存档并把下周要调整的动作提案进日程。判据只有一条：下周会因此做什么不一样的事。

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。

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。

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.

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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。

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."

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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.

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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.

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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.

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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.

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Detect and prevent privilege escalation in Kubernetes pods by monitoring security contexts, capabilities, and syscall patterns with Falco and OPA policies.

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Detect and prevent QR code phishing (quishing) attacks that bypass traditional email security by embedding malicious URLs in QR code images within emails.

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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.

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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.

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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。

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。