「budget」の検索結果

Load this skill when the user asks specific insurance education questions in the Indian context, such as term vs endowment vs ULIP, riders, claim settlement process, nominee concepts, or policy jargon; do not load for generic budgeting or emergency fund queries.

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。

Query the BuildAI PostgreSQL database (read-only). Returns results as JSON for construction project data — projects, RFIs, submittals, budgets, change orders, daily logs, punch lists, insurance, vendors, pay applications.

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 cloud infrastructure costs and produce a concrete optimization plan with specific changes and estimated savings. Use when asked to "how much is this costing", "reduce cloud spend", "cost optimization", "are we overpaying", "cloud bill", or "budget for this infra".

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 AI cost topology — billing attribution, team-level spend, forecast vs actuals, alert gaps. Use when asked to "map our AI spend", "who is spending on LLMs", or "set up AI cost attribution".

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Render agent findings as a styled HTML report in the browser. Use when asked for "full report", "detailed report", "show in browser", or when CLI output exceeds the 40-line budget.

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 AI spend — per-model cost breakdown, top consumers, waste identification, optimization levers. Use when asked "why is our AI bill so high", "audit LLM spend", or "where is our token waste".

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Design cost reduction strategies — model tiering, prompt compression, caching, batch inference. Use when asked to "reduce our AI costs", "set up model tiering", or "cut LLM spend".

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。

Expert guide for scaling test-time compute, dynamic reasoning token allocation (Gemini 4 Pro, Claude 5.5, GPT Astra 6), Monte Carlo Tree Search (MCTS), and Process Reward Models / Panduan ahli penskalaan komputasi waktu uji (test-time compute), alokasi dinamis budget penalaran, MCTS, dan Process Reward Models.

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Expert guide for AI FinOps, dynamic model routing (LiteLLM, Portkey, RouteLLM), cost optimization, budget enforcement, semantic caching, and latency-based fallback architectures / Panduan ahli untuk AI FinOps, routing model dinamis, optimasi biaya, penegakan anggaran, semantic caching, dan arsitektur fallback berbasis latensi.

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Expert guide for designing Machine-to-Machine (M2M) micro-economies, autonomous agent wallets, and swarm budget allocation / Panduan ahli merancang ekonomi mikro antar-agen (M2M), dompet agen otonom, dan alokasi anggaran swarm.

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。

Expert guide for engineering and optimizing ultra-large context windows (2M+ tokens) across Gemini 4 Pro, Claude 5.5, and GPT Astra 6 — context partitioning, retrieval-augmented context injection, sliding window strategies, and cost-aware token budgeting / Panduan ahli rekayasa dan optimasi context window ultra-besar (2M+ token) di Gemini 4 Pro, Claude 5.5, dan GPT Astra 6 — partisi konteks, injeksi konteks berbasis retrieval, strategi sliding window, dan penganggaran token sadar-biaya.

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Household-scale nutrition planning that treats meals as a weekly system, not a series of independent decisions. Covers macronutrient balance, the plate method, weekly rotation, leftover architecture, pantry-to-plate translation, and the food-safety floor that every meal plan must meet. Use when building a weekly menu, adapting a diet to a new household member, planning for a week of predictable meals on a budget, or teaching a learner how a kitchen feeds people over time.

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Bench instruments and measurement techniques for electronics — digital multimeter, oscilloscope, logic analyzer, spectrum analyzer, function generator, power supply, and their proper use. Covers probing technique, ground loops, loading effects, trigger modes, FFT mode pitfalls, signal integrity, noise budget analysis, and debugging workflows from "it doesn't work" to "here is the failure mechanism." Use when measuring, debugging, or characterizing any electronic circuit or firmware-hardware interaction.

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Reliability engineering for cloud systems — SLIs, SLOs, error budgets, SRE practices, runbooks, incident response, on-call rotation, blameless postmortems, chaos engineering, and the NASA Systems Engineering methodology (MCR/SRR/PDR/CDR/ORR phase gates, TAID verification, requirements tracing) adapted to cloud operations. Use when establishing SLOs for a new service, running an incident, writing a runbook, preparing a launch readiness review, or bringing NASA SE discipline to cloud deployments.

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Classify the information-need of a query and dispatch it to the appropriate retrieval or reasoning strategy. Use before read-side memory access, before multi-strategy retrieval, or any time you'd otherwise default to "one retriever for everything". Returns a strategy label, a token budget, and a retrieval depth so downstream handlers can be specialised. Backed by Pre-Route (arxiv 2605.10235v2) and MemFlow (arxiv 2605.03312v1), which together show LLMs possess latent routing ability elicitable via a structured prompt — and that externalising the routing decision improves small-model performance by ~2x. Triggers: "route this", "what strategy", "before retrieving", "intent classification", or any query whose ideal handling depends on what KIND of question it is.

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。

Reviews LLM integration, prompt templates, model selection, token/cost budgets, evaluation harnesses, structured outputs, retries, and streaming. Checks trust boundaries and task evidence against the selected provider/model contract rather than prescribing universal model tiers or sampling settings.

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Reviews a whole deliverable (talk, course module, paper, chapter, or referee report) against its manifest without rewriting it. Runs a deterministic gate for build, citations, numbers, facts, and leaks and stops on failure, then runs a finder over each section and a strong-model pass over the whole work, and adjudicates into checks/<date>/findings.json and report.md with anchored findings, severities, and honest NOT CHECKED coverage within a dollar budget. Use when asked to lint or review the whole talk, module, or paper, for a pre-ship review, or for the night shift. Revision stays serial and author-led.

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。

House rules and craft for LaTeX Beamer slide decks (lectures, seminars, talks), drawn from the author's own Overleaf edits and published slide-design guidance. Covers storyboarding, frame titles, slide prose, word and frame budgets, layout without overfull boxes, XeLaTeX fonts and Korean (xeCJK), color, figures and screenshots, Overleaf source hygiene, speaker notes, and a build-and-QA loop with a contact sheet. Use when writing, editing, reviewing, or building a Beamer deck, theme, or storyboard. A course skill supplies course facts and the frame skeleton; this skill supplies the craft. Not for HTML or PowerPoint decks, posters, or papers.

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。

Paid media strategy and optimization across platforms. Use when planning paid campaigns, optimizing ad performance, managing budgets, or setting up tracking for paid channels.

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。

审核Claude Code上下文窗口在代理、技能、MCP服务器和规则中的消耗情况。识别膨胀、冗余组件，并提供优先的令牌节省建议。

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。

Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.

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。

House rules and craft for LaTeX Beamer decks (storyboarding, slide prose, frame budgets, layout, XeLaTeX and Korean fonts, color, figures, Overleaf hygiene, build QA). Use to write, edit, review, or build a Beamer deck.

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。

Classifies raw pilot request → structured JSON {task_type, class, artifact, budget_estimate, confidence, routing_tag, resolution_path, schema_version, result_type, expected_result_kind, result_kind_resolution, hypothesis_relation}. Keyword-fast (<200ms) or Haiku fallback (<60s). Does NOT create WP or call executor.

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Review changed Anabasis v2 code for reuse, simplification, efficiency and altitude, then apply the fixes. Quality only: it hunts no bugs, which /code-review does. Starts from what the repository already finds (the lint plugins, the whole-tree census, the not-slop ledger), measures before and after with reduce-complexity's scripts, and holds a pass to its budgets and to the rules no pass trades. Use as /simplify on your own diff before a commit, or when the operator asks whether code is simple enough, why something exists, to go one layer higher, or to remove something and find another way.

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。