「consumer」の検索結果

Apply one governed bounded Proto UI dependency update after pui-deps establishes the exact manifest, lockfile, consumers, compatibility evidence, and rollback boundary. Use for ordinary package or toolchain updates whose intended compatibility is already fixed. Route unresolved public compatibility, security disclosure, publication, release, or provenance exceptions to their dedicated decision path.

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, run, and report proportional Proto UI validation for a completed change. Use before review, handoff, commit, or pull request updates, and after resolving validation failures. Cover focused evidence first, then affected graph, types, docs, packaging, consumers, release, or repository checks according to risk.

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 a consumer repo needs proposal-first backlog, Registry, and Atlas drafts from new project intent before source exists.

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。

Analyzes Android build files and R8 keep rules to identify redundancies, broad package-wide rules, and rules that subsume library consumer keep rules. Use when developers want to optimize their app's size, remove redundant or overly broad keep rules, or troubleshoot Proguard configurations.

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。

Work with the @upstash/redis TypeScript/JavaScript SDK, a serverless HTTP-based Redis client for Next.js, Vercel, Cloudflare Workers, edge runtimes, and Node.js. Use when adding a cache (cache-aside, write-through, TTL and expiration strategies), session storage and user sessions, a key-value store, leaderboards and rankings with sorted sets, counters, distributed locks, queues with lists, streams and consumer groups, sparse index-addressed arrays and ring buffers (ARSET, ARINSERT, ARRING, ARGREP, AROP), embeddings and nearest-neighbour vector search stored inside Redis (VECTOR commands via redis.vector, separate from @upstash/vector), JSON documents, pipelines and MULTI/EXEC transactions, Lua scripting, read replicas, or full-text search, typo-tolerant search, facets, aggregations, and search over Redis stream entries with Upstash Redis Search (different from regular FT.SEARCH; also available for TCP clients via @upstash/search-redis and @upstash/search-ioredis). Also use when migrating from ioredis or node-redis, when a Redis connection is needed from a serverless function without connection pooling, when integrating @upstash/ratelimit, or when the user says Redis cache, KV store, session store, serverless Redis, or Upstash Redis. Also use when building AI agents on Redis with Upstash AgentKit (agent memory, chat history, RAG tools, tool caching, chat persistence, resumable streams, distributed locks) for the Vercel AI SDK, TanStack AI, or Vercel Eve. Supports automatic serialization/deserialization of JavaScript types.

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。

Feedback loop implementation for continuous CTI improvement. Consumer feedback, analyst retrospectives, source quality tracking.

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。

Premium brand-kit image generation skill for creating high-end brand-guidelines boards, logo systems, identity decks, and visual-world presentations. Trained for minimalist, cinematic, editorial, dark-tech, luxury, cultural, security, gaming, developer-tool, and consumer-app brand systems. Optimized for intentional logo concepting, refined composition, sparse typography, strong symbolic meaning, premium mockups, art-directed imagery, and flexible grid layouts. Use when creating brand-kit image prompts, identity boards, logo systems, or visual-world presentations.

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。

Plan deprecation and migration—replacement first, consumer migration, strangler and adapter patterns, zombie code removal. Use when sunsetting APIs, libraries, or legacy modules.

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。

"Run conjoint analysis to measure how product attributes drive consumer preferences and willingness to pay. Use this skill when the user needs to quantify feature value trade-offs, estimate willingness to pay for specific features, or optimize product configuration — even if they say 'which features do customers value most', 'willingness to pay for feature X', or 'product attribute trade-offs'.".

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"Design bundle pricing strategies using pure bundling, mixed bundling, and consumer surplus analysis. Use this skill when the user needs to set prices for product bundles, determine whether bundling increases profit, or analyze unbundling opportunities — even if they say 'should we bundle these products', 'bundle pricing', or 'package deal pricing'.".

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 and audits a concept-ontology for a regelrecht corpus to decide and verify reference types — cross-law source-binding vs intra-law source-binding vs open-norm leaf vs external-fact leaf — and to catch concept conflation across laws. Core principle: a value's reference type is fixed by WHO derives it, and a binding is sound only when producer and consumer denote the SAME concept; the lexical surface (the word in the text) is not the concept. Detects false friends (one term, different concepts — e.g. a register-based vs a factual "naar de omstandigheden"-based residence/ingezetene concept), orphan plain-params (a value a regulation actually derives, modeled as a brute input), negation/complement re-entry (¬X modeled as an independent leaf next to X), and duplication (one concept re-entered as several independent leafs, losing the single-source-of-truth invariant). Produces a law-level glossary and a stelsel-level concept map with sound same-concept binding edges and flagged lexical-match-but-concept-mismatch edges. Use when deciding whether a value should be cross-law/intra-law/leaf, when the same term means different things across laws, or when a binding might silently conflate concepts.

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。

Review a Go design spec before implementation begins - dispatch a subagent that checks a design doc for completeness, consistency, and idiomatic Go (simplicity, small consumer-defined interfaces, explicit wrapped errors, context propagation). Use when a user has a Go spec to review, asks "is this spec ready?", or is about to implement from a written design. Distilled from spf13/go-skills and adapted to this repo's conventions.

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。

Evaluate and act on code-review feedback with technical judgment. Use before applying external suggestions, especially when feedback is ambiguous, broad, or may conflict with repository constraints, and carry confirmed corrections to relevant consumers without widening scope.

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 asked to check Lumen version adoption across consumer repos, generate the Lumen adoption report, or find which repos are behind on a Lumen package version, or who owns those repos.

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。

Shared in-workflow quality gates for code/PR skills — the gate model, trust tiers, and the three primitives (evidence-before-done, independent judge, anti-gaming). DO NOT TRIGGER directly — referenced by consumer skills via relative path.

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 the canonical markdown+JSON deliverable of any SfSkills runtime agent into Excel, PDF, CSV, Notion card, ServiceNow ticket, or similar downstream format without adding dependencies to the consumer's project. NOT for an Agentforce action's output — use agentforce/agent-actions. NOT for exporting Salesforce data — use integration/bulk-api-2-patterns.

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。

Place reusable auth components in the shared layer without breaking ASP.NET Core consumers or tests.

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。

Codified expertise for electricity and gas procurement, tariff optimization, demand charge management, renewable PPA evaluation, and multi-facility energy cost management. Informed by energy procurement managers with 15+ years experience at large commercial and industrial consumers. Includes market structure analysis, hedging strategies, load profiling, and sustainability reporting frameworks. Use when procuring energy, optimizing tariffs, managing demand charges, evaluating PPAs, or developing energy strategies.

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。

End-of-session pass that persists durable facts from the CURRENT session into .claude/memory/. Runs IN-SESSION in the main agent (never via Task — a subagent starts cold with no transcript), prompted by the Stop hook's nudge. Triggers on "persist durable facts from this session", "run memory-extraction", "flush session memory", or "extract memory before close". Reads the in-context transcript plus existing memory, applies the durable-fact predicate below, and writes one file per fact plus one MEMORY.md index line — append-or-update, never clobbering a consumer's existing memory.

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。

Drafts a CCPA/CPRA-compliant privacy policy covering all required statutory disclosures under Cal. Civil Code §§ 1798.100–1798.199. Use when drafting or updating a California privacy policy, CCPA compliance notice, or consumer privacy disclosure for a business meeting CCPA applicability thresholds.

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 and review durable REST, GraphQL, RPC, or event API contracts: shape, compatibility, pagination, error semantics. Discovers existing consumers first. Not implementation, schema, or release.

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。

Apache Kafka 4.2 best practices for event streaming, topic design, and producer-consumer patterns

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Applies game theory to multi-agent coordination protocols with advisory claims, repeated interactions, daemon-mediated correlation, and deviation penalties. Use for Nash equilibrium checks, folk-theorem cooperation analysis, correlated-equilibrium routing, and price-of-anarchy audits in strategic agent systems. NOT for consumer pricing, card-game strategy, or general microeconomics outside agent coordination.

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。

Azure Event Hubs SDK for Python streaming. Use for high-throughput event ingestion, producers, consumers, and checkpointing.

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。