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Deploy applications to Kubernetes clusters using kubectl manifests for Deployments, Services, ConfigMaps, Secrets, and Ingress resources. Implement health checks, resource limits, rolling updates, and Helm chart packaging for production deployments. Use when deploying new applications to EKS, GKE, AKS, or self-hosted clusters, migrating from Docker Compose to container orchestration, implementing zero-downtime rolling updates, or setting up multi-environment deployments across dev, staging, and production.

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Derive a theoretical result step-by-step from first principles or established theorems, with every step explicitly justified and special cases checked. Use when deriving a formula or theorem from first principles, proving a mathematical statement by logical deduction, re-deriving a textbook result for verification or adaptation, extending a known result to a more general setting, or producing a self-contained derivation for a paper or thesis.

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Deploy ShinyProxy for hosting multiple containerized Shiny applications. Covers ShinyProxy Docker deployment, application.yml configuration, Shiny app Docker images, authentication, container backends, usage tracking, and scaling. Use when hosting multiple Shiny apps behind a single entry point, needing per-app authentication and access control, deploying Shiny apps as isolated Docker containers, or scaling beyond single-app deployment with usage analytics and audit logging.

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Thoroughness and diligence in execution — systematic checking, completeness verification, follow-through on commitments, and the discipline of finishing well. Maps the personality trait of conscientiousness to AI task execution: not cutting corners, verifying results, and ensuring that what was promised is what was delivered. Use before marking a task as complete, when a response feels "good enough" but deserves better, after a complex multi-step operation where steps may have drifted, or when self-monitoring detects a pattern of cutting corners or rushing.

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Catalog and classify materials using standard library systems. Covers descriptive cataloging, subject headings, call number assignment using Dewey Decimal and Library of Congress Classification, MARC record basics, shelf organization, and authority control for consistent access points. Use when organizing a personal, institutional, or community library from scratch, assigning call numbers and subject headings to new acquisitions, reclassifying a collection that has outgrown its original system, or establishing authority control for authors, series, or subjects.

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Author a new agent-almanac workflow — a self-contained `.mjs` orchestration script run by Claude Code's Workflow tool. Covers choosing a workflow over a team, copying the template, the triple-name discovery contract, the pure-literal meta and sidecar frontmatter, building the body with the injected primitives, the advisory/implementing capability contract, the adversarial-verification fail-safes, validation, and manual installation. Use when you have a repeatable, parameterized procedure that coordinates several agents and want it captured as a reusable, auditable artifact whose control flow is fixed in code.

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Apply collective intelligence coordination patterns — stigmergy, local rules, and quorum sensing — to organize distributed systems, teams, or workflows without centralized control. Covers signal design, agent autonomy boundaries, emergent behavior cultivation, and feedback loop tuning. Use when designing distributed systems without a coordination bottleneck, organizing teams that must self-coordinate, building event-driven architectures with shared state communication, or replacing fragile centralized orchestration with resilient emergent coordination.

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AI dynamic reasoning balance — maintaining grounded reasoning under cognitive pressure, smooth chain-of-thought coordination, and weight-shifting cognitive load across subsystems. Use at the beginning of a complex task requiring multiple coordinated reasoning threads, after a sudden context shift or tool failure, when chain-of-thought feels jerky, or when preparing for sustained focused work that requires all subsystems in alignment.

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AI internal coordination using stigmergic signals — managing information freshness in context and memory, decay rates for assumption staleness, and emergent coherent behavior from simple local protocols. Use during complex tasks where multiple sub-tasks must coordinate, when context has grown long and information freshness is uncertain, after context compression when information may have been lost, or when sub-task outputs need to feed into each other cleanly without degradation.

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AI relational calibration — reading and adapting to the specific person you are working with. Goes beyond user-intent alignment (solving the right problem) to genuine attunement (meeting the person where they are). Maps communication style, expertise depth, emotional register, and implicit preferences from conversational evidence. Use at the start of a new session, when communication feels mismatched, after receiving unexpected feedback, or when transitioning between very different users or contexts.

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AI context assessment — evaluating problem malleability, mapping structural rigidity versus flexibility, analyzing transformation pressure, and estimating capacity to adapt. Use when a complex task feels stuck and it is unclear whether to push through or pivot, before a significant approach change to assess whether the current reasoning structure can support it, when accumulated workarounds suggest the underlying approach may be wrong, or as a periodic structural health check during extended multi-step tasks.

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AI multi-path reasoning coherence using bee democracy — independent evaluation of competing approaches, waggle dance as reasoning-out-loud, quorum sensing for confidence thresholds, and deadlock resolution. Use when forage-solutions has identified multiple valid approaches and a selection must be made, when oscillating between options without committing, when justifying an architecture or tool choice with structured reasoning, or before an irreversible action where the cost of the wrong choice is high.

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AI situational awareness — internal threat detection for hallucination risk, scope creep, and context degradation. Maps Cooper color codes to reasoning states and OODA loop to real-time decisions. Use during any task where reasoning quality matters, when operating in unfamiliar territory, after detecting early warning signs such as an uncertain fact or suspicious tool result, or before high-stakes output like irreversible changes or architectural decisions.

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Modern C# idioms for any .NET project (C# 10+, .NET 6+): nullable reference types, records, readonly structs, primary constructors, pattern matching, async/await with CancellationToken, IDisposable/IAsyncDisposable, file-scoped namespaces, var usage, naming conventions (PascalCase members, _camelCase fields, I-prefixed interfaces), and class design rules. Apply whenever the project is a .NET 6+ project. Stack-agnostic — referenced by every .NET plugin in the marketplace. Use this skill to: - Write self-documenting immutable value types with records and readonly structs. - Handle nullable reference types explicitly to eliminate NullReferenceException at compile time. - Implement async/await correctly with CancellationToken propagation and ConfigureAwait(false) in libraries. - Dispose unmanaged resources correctly via IDisposable / IAsyncDisposable and using statements. - Apply C# pattern matching (switch expressions, property patterns, list patterns) for cleaner branching logic. Do NOT use this skill for: - Framework-specific idioms (ASP.NET Core controllers, minimal APIs, EF Core — those live in aspnet-core-plugin skills). - Build tooling (dotnet CLI, NuGet, csproj) — see csharp-foundation:dotnet-tooling. - Testing patterns — see csharp-foundation:dotnet-testing.

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Modern Java idioms for any JVM project (Java 17+): records, sealed types, text blocks, pattern matching, Optional discipline, streams, immutability, null safety, package layout, var usage, and code organisation. Apply when the project is a Java 17+ project. Stack-agnostic — referenced by every Java plugin in the marketplace. Use this skill to: - Write self-documenting, immutable-by-default value types with records. - Model closed type hierarchies with sealed classes and interfaces. - Handle optionality explicitly with Optional instead of returning null. - Write expressive pipelines with Stream API without performance footguns. - Lay out packages consistently (feature-first or layer-first) and keep class responsibilities tight. Do NOT use this skill for: - Framework-specific idioms (Spring annotations, JPA mappings — those live in framework plugin skills). - Build tooling (Maven/Gradle) — see java-foundation:build-tooling. - Testing patterns — see java-foundation:jvm-testing.

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Use when writing Cortex-M NVIC ISRs, configuring priorities, handling HardFault, tail-chaining, or measuring interrupt latency. Not for the vector table itself: use baremetal-startup.

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Use when inspecting, searching, verifying, or parsing DWARF debug info: DIEs, DW_TAG_/DW_AT_ entries, .debug_* sections, line tables, or llvm-dwarfdump/readelf output.

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Use when inspecting .debug_info or .debug_line sections with dwarfdump or readelf, working with split-DWARF .dwo files, setting up debuginfod, or checking how LTO and stripping affect debug info.

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Use when choosing a linker, fixing link order or undefined symbol errors, enabling LTO or ThinLTO, or cutting dead code with --gc-sections. Not for inspecting the linked binary: use elf-inspection.

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Use when writing KUnit tests, adding kselftest cases, fuzzing syscalls with syzkaller and kcov, running LTP, or wiring CI for a kernel patch.

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Use when configuring ADC sampling time, DMA-driven ADC, calibration, or DAC channel setup on bare-metal MCUs. Not for the DMA stream itself: use dma-baremetal.

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Use when the user wants to batch-close resolved or outdated tracker items. The agent never closes tracker items itself. Not for individual closure or items still under active work.

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Run Proto UI's dynamic comprehension assessment and derive an unsigned local task-fit result. Use when an Agent needs review calibration or a fresh autonomous task and review ceiling. Do not use a self-result as identity, permission, or acceptance evidence.

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Self-improve AI Factory skills based on project context, accumulated patches, and codebase patterns. Analyzes what went wrong, what works, and enhances skills to prevent future issues. Use when you want to make AI smarter for your project.

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。