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概要と使いどころ
Applies the DHDNA framework as an exploratory rubric for reasoning and writing patterns in supplied text. Used for explicit requests for DHDNA, cognitive-style reflection, a thinking-pattern profile, or comparisons of textual reasoning. Scores describe evidence in the sample, not validated psychological traits or personal identity.
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Configure S3 buckets, policies, and lifecycle rules. Implement versioning, replication, and security. Use when managing object storage on AWS.
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Reduce AWS spend with rightsizing, autoscaling, commitment planning, and storage lifecycle policies. Use when running FinOps reviews, lowering cloud bills, or improving cost-per-request metrics.
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Replace the manual commit-by-commit classification step in `breeze release-management prepare-provider-documentation` with AI-driven classification. For each provider with pending changes, analyze every PR (batched into one sub-agent per provider, not one per PR), pay special attention to potentially breaking changes by inspecting the actual diff, scope multi-provider PRs to the current provider's slice, ask the release manager when uncertain, and apply version bumps + changelog entries. Use during the regular provider release cycle as an alternative to the interactive breeze prompts.
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Live launchpad feed + AI hot topics for meme tokens. (1) meme-rush: real-time lifecycle feed on launchpads (Pump.fun, Four.meme) — brand-new launches, currently-finalizing / bonding-curve tokens, and just-migrated-to-DEX tokens; filter by dev behavior, age, market cap. (2) topic-rush: AI-detected hot market narratives with the associated tokens ranked by inflow. Use for: "new pump.fun launches", "what just migrated", "currently bonding", "hot narratives", "what topic is pumping right now", "live launchpad feed", "AI hot topics".
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Build automated billing systems for recurring payments, invoicing, subscription lifecycle, and dunning management. Use when implementing subscription billing, automating invoicing, or managing recurring payment systems.
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Python background job patterns including task queues, workers, and event-driven architecture. Use when implementing async task processing, job queues, long-running operations, or decoupling work from request/response cycles.
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Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use this skill when designing clean architecture for a new microservice, when refactoring a monolith to use bounded contexts, when implementing hexagonal or onion architecture patterns, or when debugging dependency cycles between application layers.
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Use this skill when creating, managing, or working with Conductor tracks - the logical work units for features, bugs, and refactors. Applies to spec.md, plan.md, and track lifecycle operations.
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Turn any concept, lesson, slide deck, or source material into a Feynman learning cycle in which learners explain first, expose the smallest gap, rebuild the explanation through Socratic prompts, strip jargon, stress-test analogies, and transfer the idea to a new context. Use when the user asks for teach-back, learning by explaining, or a Feynman-style classroom. Do not use when the Feynman technique itself is merely the lesson topic or when study strategy should remain a parallel goal.
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Design a coherent assessment-and-practice lesson using zone-of-proximal-development principles: independent diagnosis, targeted review, supported practice, fresh independent checks, and an evidence-based next step. Use for 习题课（最近发展区）, 最近发展区, 测验习题课, diagnostic review lessons, or guided-to-independent practice. Can also run one practice cycle in chat; do not turn a request for a single exercise or theory explanation into a full lesson.
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Infuse social-emotional learning (SEL) into a concept-centered OpenMAIC classroom as a parallel learning goal. Use when the user asks to add emotional awareness, self-management, empathy, collaboration, relationship skills, resilience, or responsible decision-making to a subject lesson. Do not use when SEL itself should be the standalone content of a counseling, advisory, psychology, or homeroom lesson.
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CommunityToolkit.Mvvm Messenger pub/sub for decoupled communication between ViewModels (or any objects). Covers WeakReferenceMessenger vs StrongReferenceMessenger, IRecipient<TMessage>, RequestMessage<T> / AsyncRequestMessage<T> / CollectionRequestMessage<T>, ValueChangedMessage<T>, channels (tokens), and the ObservableRecipient activation lifecycle. Use across WPF, WinUI 3, .NET MAUI, Uno, and Avalonia.
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Strategic account planning and execution for enterprise deals. Use when planning complex sales cycles, managing multiple stakeholders, applying MEDDICC qualification, tracking deal health, or building mutual action plans. Includes the "stale MAP equals dead deal" pattern.
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Launch new products from idea to first customers. Use when launching products, finding early adopters, building launch week playbooks, diagnosing why adoption stalls, or learning that press coverage does not equal growth. Includes the three-layer diagnosis, the 2-week experiment cycle, and the launch that got 50K impressions and 12 signups.
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Smart Money Concepts (ICT) signal engine. Uses the smartmoneyconcepts library to implement institutional-trading-school analysis of BOS, ChoCH, FVG, and order blocks (OB).
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Commodity analysis (oil supply-demand balance / gold pricing / copper as an economic predictor / inventory cycles / futures premium-discount structure / seasonality), generating directional commodity signals.
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Macroeconomic cycle positioning and central-bank policy interpretation, including GDP/CPI/PMI/rates/FX analysis, with output in the form of major-asset allocation tilts.
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Behavioral finance applications: theories of overreaction and underreaction, behavioral explanations for momentum and reversal, investor sentiment cycles, cognitive-bias checklists, and debiasing quantitative strategies.
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Install and run Stratus Red Team to detonate granular, MITRE ATT&CK-mapped AWS, Azure, GCP, and Kubernetes attack techniques through their warmup-detonate-revert-cleanup lifecycle, then verify the telemetry they generate. Use to validate that cloud detections (CloudTrail, GuardDuty, Microsoft Sentinel, GCP SCC, Falco) actually fire, or to run a repeatable purple-team exercise generating realistic attacker telemetry.
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Detects and prevents privilege escalation inside Kubernetes pods by combining admission control (OPA policies), runtime monitoring (Falco), and audit log analysis of security contexts, Linux capabilities, and syscall patterns. Use when investigating a pod running as root or privileged, hardening workloads against in-pod escalation, or hunting for containers exceeding their intended scope. Keywords: allowPrivilegeEscalation, runAsRoot, capabilities, securityContext, OPA, Falco, audit log. Do not use for escalation through RBAC and service-account permissions - use auditing-kubernetes-rbac-privilege-escalation.
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Configures secure OAuth 2.0 authorization flows, including Authorization Code with PKCE, Client Credentials, and Device Authorization Grant, covering flow selection, PKCE implementation, token lifecycle management, and scope design per OAuth 2.1. Use when implementing or hardening OAuth 2.0 authentication/authorization for web, mobile, SPA, or machine-to-machine clients.
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Queries Certificate Transparency logs via crt.sh and pycrtsh to detect phishing domains, unauthorized certificate issuance, and shadow IT. Monitors newly issued certificates for typosquatting and brand impersonation using Levenshtein distance. Use for proactive phishing domain detection and certificate monitoring.
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Runtime iOS app security testing with Objection (Frida): inspect keychain and filesystem data, explore app internals at runtime, and validate/bypass client-side protections during authorized mobile assessments.
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。