「domain」の検索結果

Owns repositories, data sources and mapping for Compose and Compose Multiplatform apps: DTO to domain to UiModel boundaries, Ktor clients and bearer auth, WebSocket and SSE, Room, DataStore, Paging 3, offline-first and data-layer tests. Use when writing or reviewing a repository, data source, DTO, mapper, HttpClient, bearer token, WebSocket, SSE, Room DAO, migration, DataStore, PagingSource, RemoteMediator, offline cache or MockEngine test. Do NOT use for ViewModel or UI wiring (compose-feature), composables (compose-ui), the MVI contract or error tiers (compose-architecture), Gradle modules (compose-project), or commonMain vs platform splits (compose-platform).

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Run a comprehensive code review on local source files. Use this skill when the user asks to review, audit, inspect, evaluate, or check code, even if they never use the word "review." Does not post comments to GitHub pull requests — use post-code-review-to-pr for that. Does not analyze architectural structure or module boundaries — use architectural-analysis for that. Does not explain code or a PR to build understanding before reviewing — use code-overview for a written overview, or code-walkthrough to be paced through it one step at a time. Does not map bounded contexts or domain boundaries — use ddd-analysis for that. Does not capture feedback on Han's own skills — use han-feedback for that.

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。

Evidence-based investigation of issues, bugs, API calls, integrations, and other aspects of software development that need a deep dive to find the root cause and solutions. Use when you need to debug, troubleshoot, diagnose, or figure out why something is broken. Does not review code for quality or style — use code-review for auditing changes or post-code-review-to-pr for posting review feedback to GitHub. Does not assess architectural health or structural risk — use architectural-analysis for architectural concerns. Does not research open-ended options, prior art, or how something works when nothing is broken — use research for that. Does not plan the structural change a root cause calls for — use plan-a-change. Does not map bounded contexts or domain boundaries — use ddd-analysis. Does not capture feedback on Han's own skills — use han-feedback for that.

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。

Builds a new Claude Code agent (subagent) from scratch through a relentless, evidence-based interview that walks the agent's design tree decision-by-decision — entity fit, domain focus and vocabulary, role identity, anti-patterns, description, model tier, tools, and self-containment — then reviews the finished agent against the plugin-building guidance and applies every fix it finds. Use when creating, authoring, scaffolding, designing, or drafting a new agent or subagent. Does not build a skill or slash command — use skill-builder. Does not serve, vendor, or refresh the authoring guidance itself — use guidance.

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。

Restructure existing code without changing its behavior, through a test-gated refactoring loop: a named target, a green suite over that target before any edit, a planned sequence of small named refactorings, and the full suite re-run after every step. Use when the user wants to refactor, restructure, clean up, simplify, or improve the design of existing code, or to apply refactoring recommendations from a code-review or architectural-analysis report. This skill changes code; it does not review code (use code-review), assess architecture (use architectural-analysis), plan a multi-module restructure before executing it (use plan-a-change), map domain boundaries before restructuring along them (use ddd-analysis), or build new behavior test-first (use tdd). Do not use it on code inside an active tdd loop; the refactor step of tdd owns that cleanup. Runs its planned sequence to completion without pausing for review; to review each named refactoring as it lands, use pairing.

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。

Performs deep architectural analysis of a specified module, directory, or feature area by examining structural coupling, data flow, concurrency patterns, risk, and SOLID alignment. Use when the user wants to assess, evaluate, or review the architecture, design quality, dependency structure, coupling, cohesion, or technical debt of an existing part of the codebase. Not for investigating specific bugs, runtime errors, or failures — use investigate. Not for test planning — use automated-test-planning. Not for file-level code review — use code-review. Not for researching open-ended options, prior art, or how something works — use research. Not for designing a new interface or contract — use design-an-api. Not for planning the change its findings imply — use plan-a-change. Not for discovering bounded contexts, ubiquitous language, or where code boundaries diverge from domain boundaries — use ddd-analysis. Not for writing documentation or architectural decision records.

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。

Applies the reasoning of Pieter Abbeel, robotics and reinforcement learning expert, UC Berkeley professor, and co-founder of Covariant. Use this skill whenever you are designing AI systems, tackling Sim2Real transfer, deploying machine learning in the physical world, or evaluating reinforcement learning architectures. Trigger this skill for questions about domain randomization, reward design, bootstrapping real-world AI, robotics hardware assumptions, or shifting from hard-coded rules to data-driven deep learning. It helps ground theoretical AI in physical embodiment and pragmatic deployment.

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。

Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.

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。

Applies the reasoning style of Kaiming He, computer vision pioneer and creator of ResNet. Use this skill whenever you are designing deep learning architectures, debugging neural network optimization, formulating generative AI problems, or bridging AI with other scientific domains. Trigger this skill for discussions on network depth, weight initialization, residual learning, flow matching, or when reframing discriminative tasks as conditional generation. It emphasizes simplicity in complex visual problems, end-to-end optimization, and viewing AI as a universal language for science.

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。

Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate leverage.

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。

Debug "API returns data that doesn't exist in database" when multiple Kubernetes clusters exist (production, staging, POC). Use when: (1) REST API returns records that direct database queries can't find, (2) Data counts match approximately but specific records don't overlap, (3) kubectl context points to a different cluster than the one serving the public domain, (4) ClickHouse/Postgres queries return stale or different data than the API, (5) "completely different datasets" despite same table names and schemas. Root cause: querying the wrong cluster's database.

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。

Fix iOS AVPlayer "CoreMediaErrorDomain error -12939 - byte range length mismatch" or similar video streaming failures when a proxy/edge/CDN service sits between the client and object storage (GCS, S3, R2). Use when: (1) iOS video fails with -12939 "byte range length mismatch - should be length 2 is length N", (2) curl with Range: bytes=0-1 returns full file instead of 2 bytes, (3) one URL path works with Range but another path to the same storage doesn't, (4) proxy advertises Accept-Ranges: bytes but returns 200 with full file for range requests. The root cause is typically a proxy handler that constructs a new request to the backend without forwarding the client's Range header.

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。

Fix cert-manager DNS01 ACME challenges stuck in "pending" state with "DNS record not yet propagated" inside GKE private clusters, even when TXT records exist in Cloudflare DNS. Use when: (1) cert-manager challenges show "pending" for hours with propagation check failures, (2) dig from outside cluster shows correct TXT records but cert-manager can't verify them, (3) Using Cloudflare DNS01 solver in a GKE private cluster with Cloud NAT, (4) Google Cloud intercepts 8.8.8.8 DNS queries returning NXDOMAIN for Cloudflare-managed records, (5) Even --dns01-recursive-nameservers with 1.1.1.1 doesn't fix the propagation check despite TXT records being verifiable from busybox pods in the same namespace. Covers the full debugging flow and certbot manual workaround.

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。

Convert Chinese or mixed-language text, iMandalArt cards, Obsidian 九宮, 表格, Mandala notes, or eight-domain outlines into a Markdown 3x3 nine-grid table with an ◎ center, then copy, save, append, or hand it to apps that render Markdown tables such as Obsidian and AIDA. Use when the user asks for Markdown 九宮格, Obsidian 九宮, Markdown table 九宮, 曼陀羅表格, nine-grid clipboard, AIDA table output, or conversion between Markdown 九宮, iMandalArt, and 81-grid workflows.

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。

Performs abstract interpretation to produce summarized execution traces and high-level program behavior representations. Highlights key control flow paths, variable relationships, loop invariants, function summaries, and potential runtime states using abstract domains (intervals, signs, nullness, etc.). Use when analyzing program behavior, understanding execution paths, computing loop invariants, tracking variable ranges, detecting potential runtime errors, or generating program summaries without concrete execution.

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。

Research a company using Common Room data. Triggers on 'research [company]', 'tell me about [domain]', 'pull up signals for [account]', 'what's going on with [company]', or any account-level question.

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。

Search and retrieve agent skills at runtime. This skill should be used when the agent needs to find specialized capabilities, workflows, or domain knowledge to accomplish a task. Skyll aggregates skills from skills.sh and returns full SKILL.md content ready for context injection.

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。

Dokku is a self-hosted Platform-as-a-Service built on Docker that provides Heroku-like git-push deployment on your own infrastructure. With 32,000+ GitHub stars, it supports buildpacks, Dockerfiles, custom domains, SSL via Let's Encrypt, and a rich plugin ecosystem for databases, caching, and storage.

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。

Native agent-to-agent language for compact multi-agent messaging. A shared tongue agents speak directly, not a translation layer. 340+ atoms across 7 domains; 3x smaller than natural language.

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。

Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use when architecting complex backend systems or refactoring existing applications for better maintainability.

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。

Map the big picture around a topic or software repository by identifying its boundaries, layers, actors, components, relationships, flows, history, fault lines, and open questions, then explain how the pieces fit together and where to look next. Use when a user asks to understand a whole field, domain, technology, ecosystem, industry, codebase, architecture, unfamiliar repo, or phrases such as "give me the big picture", "map the landscape", "how does this all fit together?", "help me get oriented", or "what am I missing?"

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。

Real-world Active Directory environment constraints that silently break attacks when ignored: NTLM disabled (Kerberos fallback), AES-only KDCs (RC4 blocked by GPO), LDAP signing and channel binding requiring LDAPS on 636, Kerberos clock skew (KRB_AP_ERR_SKEW), SPNs that must be FQDNs never short names or IPs (the misleading invalidCredentials), Protected Users / LAPS / gMSA / MachineAccountQuota, LDAP 1000-object paging, and VPN latency. Use this whenever a bind, TGT request, LDAP query, or SMB negotiation fails in a way that looks like bad credentials but is really hardening, or before running any AD tooling against a domain you have not fingerprinted. Standard-tooling knowledge (netexec, impacket, certipy, bloodyAD, kerbrute), no vendor-specific engine.

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 normalized database schemas with tables, relationships, indexes, and constraints for any application domain. Use when the user requests database schema design or provides relevant inputs for this workflow.

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。

Think and work like an expert Mining Geologist. Use when a task calls for Mining Geologist judgment. Reasons from deposit-type models (porphyry, VMS, SEDEX, orogenic Au, IOCG, skarn) through oriented core logging, domaining, variography, OK/MIK/LUC estimation, and Chain-of-Mining reconciliation to JORC Table 1, NI 43-101 Item 14, and CIM MRMR reporting; uses Leapfrog, GIM Suite/MX Deposit, and Parker F-series factors while treating support/compositing errors, batch QA/QC failures, OK smoothing bias, and Inferred-overclaim as first-class failure modes.

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。