「bounded」の検索結果

[BETA] AEM Cloud Service expert skill — handle an explicitly-unbounded query (`p.limit=-1` predicate or JCR `setLimit(-1)`): bound it when capping is provably safe, otherwise flag it for human pagination. Use for "bound my query", "unbounded query", "query causing OOM", or a scan that flags `p.limit=-1`. Top CSO OOM cause: an unbounded result set traversed in a loop fills the heap and saturates the instance. The analyzer locates the explicit markers; the recipe triages each by how the result is consumed — single-result → 1, already-bounded list → N, iterate-all on the request path → escalate. Never silently cap a result the caller reads in full. This skill is in beta. Verify all outputs before applying them to production projects.

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Delegate reading large or multiple files to the `bulk-read` command, which sends the files to a cheaper reader model and returns only its answer. Use when a question needs the content of a file over about 350 lines or of several files at once, when a PreToolUse guard denied an unbounded read, or when the user says "bulk read", "一括読み込み", or "まとめて読んで". Do NOT use for files you are about to edit, for subtle correctness reasoning such as concurrency bugs, or for short files that fit a bounded read.

Test-Driven Repair — given a failing test, spawn a bounded headless `claude -p` (Read/Edit/Bash only) that makes the test pass without modifying it. Modeled on agent-harness-generator's ADR-175 Test-Driven Repair mode. Bounded cost via --max-budget-usd, bounded capability via --allowedTools. Closes the loop the TDD plugins didn't — we generate tests, this fixes the code to satisfy them.

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Use for bounded reasoning, partial planning, commitment policy, deliberation cost, and reconsideration-threshold design instead of assuming free deliberation. NOT for exhaustive planning, static optimization, or domains where reconsideration cost is irrelevant.

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Use for bounded reasoning, partial planning, commitment policy, deliberation cost, and reconsideration-threshold design instead of assuming free deliberation. NOT for exhaustive planning, static optimization, or domains where reconsideration cost is irrelevant.

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Create and manage a DDD bounded context with standard directory structure. Use when starting a new subdomain, splitting a monolith into bounded contexts, or scaffolding the domain/application/infrastructure layout for a fresh module.

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Phase 3 of building a Claude Managed Agent — the bounded grade→iterate loop. Define a CMA outcome (a required markdown rubric graded by an isolated grader), read each verdict, decide the next move (sharpen / re-run / promote to schedule), and once a version passes, run held-back eval cases in parallel. Use when the user says "grade my agent", "make it pass the rubric", "iterate until it's good", "is it good enough", or when the orchestrator routes phase=grade-iterate. outcome_builder.py builds the user.define_outcome payload (rubric required, max_iterations clamped 1..20 — never unbounded); verdict_reader.py reads the grader result and recommends the next move; eval_scaffold.py generates held-back cases + a parallel run plan (capped at the 25-thread CMA ceiling). Distinct from stage-launch (first launch) and run-without-you (scheduling).

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Discover, find, compare, audit, repair, adapt, and design repeatable AI-agent loops with explicit triggers, actions, verification, stopping conditions, guardrails, and handoffs. Use when a user asks to analyze a codebase for potential loops, mine coding-thread history for work done more than once, turn repeated engineering work into a loop, find or recommend a published loop, create a recurring agent workflow or automation cadence, turn an outcome into a bounded copy-ready loop, or review an existing loop for weak checks, unsafe authority, unbounded repetition, stale state, or unclear stopping behavior.

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Specialist in designing and implementing scalable modular monolith architectures using NestJS with DDD, Clean Architecture, and CQRS patterns. Use when building modular monolith backends, designing bounded contexts, creating domain modules, implementing event-driven module communication, or when user mentions "modular monolith", "bounded contexts", "module boundaries", "DDD", "CQRS", "clean architecture NestJS", or "monolith to microservices". Do NOT use for simple CRUD APIs, frontend work, general NestJS questions without architectural context, or stack-agnostic evolutionary modular monolith design (use evolutionary-modular-architecture).

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Guides design and implementation of evolutionary modular-monolith platforms with DDD (strategic + tactical), flat-by-aggregate organization, an Anti-Corruption Layer for vendor independence, a transactional outbox for events, smart resilience (backoff with jitter, circuit breakers, idempotency), and a polished architecture HTML document with elegant SVG diagrams. Use when designing a platform or backend, defining bounded contexts, organizing modules and folders, choosing monolith vs microservices, decoupling from an external service (ERP, storage, AI), making calls resilient, adding real-time push, picking a 2026 TypeScript stack (Nx, NestJS, React), or producing an architecture document or diagram. Also triggers on 'modular monolith', 'bounded contexts', 'flat-by-aggregate', 'ports and adapters', 'architecture diagram'. Do NOT use for simple CRUD, NestJS-only deep implementation (use nestjs-modular-monolith), or pure domain-model review (use tactical-ddd).

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Execute one bounded CCB work item and report evidence without changing workflow authority.

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Produce a token-bounded, citation-ready context slice from an existing Obsidian vault for a downstream agent or task. Use for "/wiki-context-pack", "use my vault as context", "context slice for X", "pack the wiki for my agent", or "bounded context for Y".

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[omh] Entire product design problem to delegate: prepare a bounded design direction, existing-lane composition, and executor-neutral handoff. Use when the user says: design-orchestration, design orchestration, design ownership, handle this product design, take on the design, デザインを任せる, デザイン全体を任せ, プロダクトデザインを任せ.

Organization-grade cloud and supply-chain attack-surface discovery: S3/GCS/Azure Blob bucket discovery via observed-name mining (CNAME/cert-SAN/Wayback) and bounded two-class permutation (6 prefixes x 15 suffixes on trusted tokens, bounded target-bound expansion on subdomain stems), existence (HEAD/GET) vs public-listing confirmation, object-key triage into 9 value tiers (database dumps, credentials, IaC state, kubeconfig, VCS dirs, config, archives, PII, logs), dangling-CNAME bucket-takeover detection, and the ownership-gated severity model that stops an unattributable public bucket from becoming a false CRITICAL; the fully offline AWS-account-ID recovery from a leaked AKIA/ASIA/AROA access key (base32 decode, runnable stdlib Python, canonical test vector, AWS-documentation-example-ID screening); dependency-confusion confirmation for npm/PyPI (internal-signal classifier -- private-registry binding vs org-namespace match -- paired with a read-only public-registry 404 check and the npm scope-claimability nuance the public search API misses); and passive cloud-native/container/Kubernetes/CI control-plane fingerprinting (Lambda URLs, API Gateway, Cloud Run, App Service, kubelet/etcd/K8s API/dashboard, Jenkins/GitLab/Argo CD) as an org-attribution and exposure surface. Passive/discovery only -- no exploitation, no credential submission, no active control-plane confirmation (a stage-6 validate_cloud active tier is described but out of scope). Use when enumerating a target's cloud storage footprint, recovering an AWS account ID from a leaked key, confirming a supply-chain dependency-confusion vector, or fingerprinting cloud-native/K8s/CI infrastructure for an authorized external recon engagement.

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Model software around the business domain using bounded contexts, aggregates, and ubiquitous language. Use when the user mentions "domain modeling", "bounded context", "aggregate root", "ubiquitous language", "anti-corruption layer", "context mapping", "domain events", "strategic design", "the code doesnt match the business", or "how do we split this big system". Also trigger when breaking a monolith into services, defining service boundaries, or aligning code structure with business processes. Covers entities vs value objects, domain events, and context mapping strategies. For architecture layers, see clean-architecture. For complexity, see software-design-philosophy.

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Turn a lawyer's bounded expertise, workflow, review standard, or legal judgment pattern into a safe, inspectable legal AI skill. Use when a user provides legal memos, comments, checklists, negotiation notes, review habits, compliance playbooks, or professional feedback and wants a reusable Lawve-style skill. Do not impersonate the lawyer or claim to reproduce a person; distill only authorized, source-bounded capability.

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Use when you need to generate Java project diagrams — including UML sequence diagrams, UML class diagrams, C4 model diagrams, UML state machine diagrams, UML Deployment Diagrams, ER (Entity Relationship) diagrams, and bounded-context diagrams — through a modular, step-based interactive process that adapts to your specific visualization needs. This should trigger for requests such as Generate UML diagram; Create sequence diagram; Create class diagram; Create state machine diagram; Create deployment diagram; Create C4 diagram; Create bounded-context diagram; Create context-map diagram. Part of Plinth Toolkit

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Analyzes an existing codebase using strategic Domain-Driven Design to discover bounded contexts, surface ubiquitous language and semantic collisions, find places where code boundaries diverge from domain boundaries, and produce an evidence-backed domain and context map. Use when the goal is DDD-specific: bounded context discovery, ubiquitous language analysis, domain model discovery, or mapping contested ownership and responsibility. The entire repository is a valid scope; no module or directory must be named first. Does not assess architectural coupling, cohesion, or technical debt — use architectural-analysis. Does not design service communication, integration patterns, or deployment topology — use architectural-analysis or plan-a-feature. Does not refactor code, diagnose bugs, or review code quality — use refactor, investigate, or code-review.

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Apply DDD tactical patterns when working with domain code, and validate proposed domain models before approval (design mode). Enforces aggregate design, value objects over primitives, entity identity rules, and bounded context boundaries. Use when creating or modifying domain models, designing aggregates, working in the domain layer, or when the user mentions 'domain', 'aggregate', 'value object', 'entity', 'bounded context', or 'DDD'.

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Use for 1-7 independent read-only sessions investigating one bounded Anabasis failure, diff, or design question before a fix. Defines non-leading prompts, evidence packets, symmetric fault hypotheses, self-falsification, source adjudication, and concise synthesis. Do not use for whole-run coverage at any session count; use whole-run-investigation instead.

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Run an explicit end-to-end goal loop with frozen Scope/criteria, worker delegation, external verification, evaluator review, and bounded retries. Use when the user asks for 'goal-loop', 'ゴールまで回す', '必ずやり遂げる', 'loop until done', 'until it actually works', or an orchestrator + evaluator loop. Do not use for ordinary planning, casual advice, reviews, or small single-file fixes.

Expert guidance for Domain-Driven Design architecture and implementation. Use when designing complex business systems, defining bounded contexts, structuring domain models, choosing between modular monolith vs microservices, implementing aggregates/entities/value objects, or when users mention "DDD", "domain-driven design", "bounded context", "aggregate", "domain model", "ubiquitous language", "event storming", "context mapping", "domain events", "anemic domain model", strategic design, tactical patterns, or domain modeling. Helps make architectural decisions, identify subdomains, design aggregates, and avoid common DDD pitfalls.

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Build a timeframe-bounded research dossier by decomposing a topic into year orchestrators and month-level research passes that each write one condensed paragraph plus sources directly into a target file. Use when the user asks for a dossier, timeline, chronology, release history, or month-by-month research bounded by specific years or months. Not for timeless explainers or single-document summaries with no temporal scope.

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Use when autonomously improving a measurable scalar metric through bounded, git-tracked iterations: modify one scoped change, verify, keep or revert. Triggers on /mk:loop, 'optimize coverage/bundle size/lint count', 'iterate until the metric improves'. NOT for subjective cleanup (see mk:cook), known-root-cause bugs (see mk:fix), behavioral grading (see mk:evaluate), or shipping (see mk:ship).

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