「budget」の検索結果

研究予算の策定・チェックを行う

当用户需要统计、管理律师工时、专家费用、调查费用，控制诉讼成本、向客户出具工时单或费用单时使用此技能。 典型触发场景包括但不限于： - 提到"工作小时""专家费用""调查费用""办案成本""工时单""费用单""预算""成本控制"等关键词 - 询问某一律师的工作小时、某一案件的办案费用或诉讼成本时 - 用户要求交付律师工作时长总结或办案工时单、办案费用单，用于交付客户或申请报销时 - 需要对某一案件或常法服务进行预算编制、预算执行监控或预算偏差分析时 - 需要评估诉讼成本与预期收益，进行诉讼经济性分析时

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。

Produces a practitioner-grade Blue Sky compliance memorandum and execution plan for U.S. securities offerings. Analyzes NSMIA covered securities preemption, classifies offerings under Regulation D/Rule 506 or alternative federal exemptions, maps state jurisdictional nexus distinguishing offers from sales, and generates a state-by-state filing plan with Form D/EFD workflow, fee schedules, amendment triggers, and renewal calendars. Use this skill when advising on private placement Blue Sky compliance, Reg D notice filings, multi-state offering coordination, state securities registration exemptions, Form D filings, EFD submissions, or state fee budgeting. Also trigger when the user mentions covered securities preemption, NSMIA analysis, or asks about state notice filing requirements. Even if the user just says "Blue Sky" or "state filings for our raise," use this skill.

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。

Read-only audit of the project's agent-tree structural overhead vs the context window. NOT for cost tracking (budget skill); NOT for read/compact decisions (mk:context-engineering).

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。

Read-only audit of .codex/ structural overhead: 'remove X save Y tokens' vs the context window. NOT for cost tracking (budget skill); NOT for runtime read/compact decisions (mk:context-engineering).

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。

Read-only audit of `.claude/` structural overhead. Reports prioritized "remove X save Y tokens" recommendations against the model context window. NOT for monetary cost tracking — that's /mk:budget. NOT for transcript size monitoring — long-session continuity defers to Claude Code native compaction. NOT for runtime context decisions (what to read, when to compact) — see mk:context-engineering. Use when planning to add context capacity, diagnosing perceived slowdowns, or auditing health.

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。

Prepare, submit and reconcile reviewed Fusion Automation jobs with exact sources, scoped authorization and explicit budgets. Not for arbitrary model-generated cloud scripts.

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。

Establish, author, restructure, or audit human-first processor Architecture, Design, Research, Review, and Verification documentation. Use for progressive documentation scaffolding, authority maps, reading paths, document-type content contracts, length-budgeted splitting, or maintainability reviews. Do not use as a substitute for cycle-accurate microarchitecture analysis or RTL implementation.

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。

Diagnose and eliminate the GraphQL N+1 resolver explosion using Facebook DataLoader's per-request batching, plus the second-order defenses (persisted queries, depth limits, cost analysis). Use when a GraphQL query that returns N items in a list triggers N+1 database round trips, when designing nested resolvers that load associations, when a list query times out under load, or when you need to budget query cost before resolvers run. NOT for REST-style endpoint design (use api-versioning-strategy), generic database query optimization (use database-query-optimizer), or schema design (use graphql-server-architect).

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 database migrations before executing them: risk assessment, dependency mapping, multi-service sequencing, rollback budgets, and stakeholder communication plans. Use when deciding HOW to migrate, not when writing the actual DDL. NOT for: writing migration SQL (use database-migration-manager), data ETL pipelines (use data-migration-specialist), query optimization, or ORM configuration.

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。

Manages context passing between DAG nodes and spawned agents. Handles context summarization, selective forwarding, and token budget optimization. Activate on 'bridge context', 'pass context', 'summarize context', 'context management', 'agent context'. NOT for execution (use dag-parallel-executor) or aggregation (use dag-result-aggregator).

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。

Decides HOW to mutate a DAG when a node fails, quality is below threshold, or new information changes the plan. Selects from mutation strategies (add node, replace agent, fork paths, loop back, downgrade model) based on failure type, cost budget, and execution history. Activate on "DAG failed how to fix", "mutation strategy", "replan on failure", "adaptive DAG", "recovery strategy", "what to do when node fails". NOT for detecting failures (use dag-quality), executing mutations (use dag-planner), or runtime execution (use dag-runtime).

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。

Audit LLM token cost estimates against actual API usage. Activate on 'cost verification', 'token estimate accuracy', 'API cost audit', 'estimation variance'. NOT for pricing lookups, budget planning, or cost optimization 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。

Building resilient distributed systems with circuit breakers, retries with full-jitter exponential backoff, retry budgets (per-request 3-attempt + per-client 10% ratio per Google SRE), deadline propagation, and the cascading-failure math (4 layers × 3 retries = 64x amplification). Grounded in Resilience4j, Microsoft Cloud Patterns, AWS Architecture Blog (Marc Brooker), and Google SRE Book.

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。

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

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。

Act as a senior Amazon account growth operator that combines Amazon performance-drop analytics, Amazon growth-opportunity analytics, Amazon Ads data, retail readiness, BSR/rank signals, margin, inventory, and campaign/search-term evidence into a prioritized profitable growth action plan. Use when Codex needs to decide what to protect, fix, scale, reduce, pause, harvest, launch, or monitor for an Amazon brand, agency, marketplace, ASIN set, or account; when building daily, weekly, or monthly Amazon account operating plans; when reallocating ad budget; when deciding which ASINs are safe or risky to push; or when turning performance-drop and growth-opportunity findings into approval-gated actions with success/failure criteria.

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 for read-first Amazon Sponsored Products optimization, product-aware growth, preflight readiness, and post-change monitoring reviews that can run standalone from static exports or use Rocketcart MCP as an optional Amazon Ads plus product-intelligence layer. Guides Codex to inspect Rocketcart profiles, live SP campaigns, product ads/ASIN context, category rank/BSR movement, competitor signals, product readiness, budget and targeting drift, snapshots/changelogs, and proposed approval-gated action rows without executing writes by default.

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。

Analyze Amazon product, sales, Amazon Ads, retail-readiness, and Best Seller Rank data to find commercially actionable growth opportunities. Use when Codex needs to identify which ASINs, campaigns, keywords, search terms, product targets, placements, categories, or BSR movements deserve more investment, optimization, harvesting, bid/budget changes, retail-readiness fixes, investigation, protection, or controlled scaling across Sponsored Products, Sponsored Brands, Sponsored Display, search term reports, targeting reports, campaign reports, placement reports, advertised/purchased product reports, Business Reports, organic performance, and BSR history.

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 complete day trips, walking tours, and multi-stop itineraries with time budgets using Camino AI's journey.

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 multi-waypoint journeys with route optimization, feasibility analysis, and time budget constraints.

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。

A comprehensive toolkit for AI agents operating under budget constraints.

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。

Systematically find and fix performance problems in applications, frontend and backend, by measuring first. Use this skill whenever the user reports slow endpoints, high latency, poor throughput, memory leaks, large bundles, or slow page loads; wants to profile with cProfile, py-spy, Chrome DevTools, Node --cpu-prof, or perf; run load tests with locust or k6; set performance budgets or SLOs; optimize database queries (EXPLAIN, indexes, N+1); add caching (Redis); improve frontend performance (code splitting, lazy loading, image optimization, memoization, virtualization); or tune the network (CDNs, gzip/br, HTTP/2/3). Also trigger for p50/p95/p99 latency, throughput, error rate, connection pooling, async vs threads, GIL, and memory leak investigations.

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。

Marketing operations manager handling budget planning, financial reporting, meeting preparation and follow-up, vendor management, and administrative workflows for marketing and communications departments. Designed for SME leaders who need to minimize time spent on operational overhead.

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。

LLM and cloud cost awareness — model tiering, token budgets, right-sizing, and when a cheaper model suffices. Trigger before finalising any architecture that calls LLMs, before scaling a workload, or when a cost estimate is needed.

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。