「benchmarking」の検索結果

Expert FinOps guidance for cloud, AI, and SaaS technology spend. Covers AI cost management, agentic FinOps, GenAI capacity planning, self-hosted vs managed inference, Anthropic billing, open-weight vendor APIs (DeepSeek, Qwen, Kimi, GLM), AWS (EC2, Bedrock, SageMaker, Savings Plans, CUR), Azure (reservations, AHB, OpenAI PTUs), GCP (Vertex AI, BigQuery), OCI, Kubernetes cost allocation, data platforms (Databricks, Fabric F-SKUs, Snowflake), tagging governance, SaaS management (SAM, shadow IT), AI coding tools (Cursor, Claude Code, Copilot, Codex), ITAM, allocation, showback, chargeback, anomaly management, KPIs and benchmarking, waste detection playbooks, workload onboarding and M&A, and GreenOps (cloud carbon, CSRD). Use for any question about technology cost or waste in your estate - expiring commitments or RIs, NAT gateway bills, idle VMs, snapshot sprawl - and for commitment sizing, rightsizing, allocation, SaaS sprawl, AI dev tool spend, or connecting spend to business value. Built by OptimNow.

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。

Nature-style engineering manuscript reviewer skill for testing devices, robots, platforms, systems, algorithms embodied in physical systems and engineering methods against requirements, validation, benchmarking, uncertainty, robustness and 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。

Rates any SKILL.md on a 0-100 scale across 10 dimensions with a SHIP / REWORK / SCRAP verdict. Evaluates trigger precision, instruction clarity, output predictability, edge case coverage, anti-hallucination guardrails, developer experience, composability, open-source readiness, wow factor, and real-world utility. No flattery — calibrated against Anthropic's own skill-creator rubric. Use this skill whenever someone says "score this skill", "rate my skill", "is this skill good", "skill review", "skill audit", "roast my SKILL.md", "grade this", "will this skill work", "evaluate my skill", "how good is this", or pastes a SKILL.md and asks for feedback. Also trigger when comparing two skills, benchmarking a skill collection, or asking "what's wrong with this skill" — even without the word "score".

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 benchmarking, profiling, or A/B-testing CUDA kernels or end-to-end perf in the imp inference engine on RTX 5090 (sm_120), including refreshing tests/perf_baseline.json or publishing numbers to docs/BENCHMARKS.md and the README. Triggers on "benchmark kernel", "profile cuda", "ncu", "nsys", "kernel timing", "kernel sum", "occupancy", "bandwidth bound", "compute bound", "roofline", "perf baseline", "is this regression real", "decode dropped", "aggregate throughput", "two-image A/B", "prefill kernel A/B". Do NOT use for writing/optimizing kernel code (sm120-cuda-expert) or output-quality checks (check-degeneration).

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 building imp, running its test suite, checking CI status, or debugging build/test failures - "make build", "run the tests", "test-gpu", "verify-fast", GTEST_FILTER, Docker/CUDA toolchain, dependency bumps, "CI is red/blocked", "which gate failed", stale objects / segfault after a header edit, hook edits, docker-entrypoint env vars, determinism or perplexity checks. Do NOT use for benchmarking/profiling (benchmark-cuda) or output-quality batteries (check-degeneration).

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。

Evaluates structured product tranche pricing with spread analysis, yield attribution, and comparable structure benchmarking. Use when assessing tranche value, comparing structured product pricing, or analyzing spread components.

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。

Constructs fund-level performance reporting with IRR, MOIC, DPI, RVPI, PME, and vintage year benchmarking. Use when building fund reports, calculating performance metrics, or preparing LP reporting packages.

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。

Monitors secondary market pricing trends with discount/premium analysis, bid-ask spreads, and market-clearing dynamics. Use when analyzing secondary pricing, tracking market trends, or benchmarking transaction levels.

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。

Deconstructs SaaS operating metrics including ARR, NRR, gross retention, magic number, rule of 40, and cohort economics. Use when analyzing SaaS businesses, benchmarking software metrics, or evaluating subscription model 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。

Computes fund performance with gross/net IRR, MOIC, DPI, RVPI, TVPI, PME, and direct alpha methodologies. Use when calculating fund performance, reconciling return metrics, or benchmarking against peer groups.

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。

Evaluates transportation assets with ridership analysis, fare structure assessment, and operating efficiency benchmarking. Use when analyzing transportation projects, evaluating mass transit, or assessing toll road economics.

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。

Values infrastructure assets with DCF, regulated asset base, and comparable transaction methodologies adjusted for regulatory and contractual frameworks. Use when valuing infrastructure assets, analyzing regulated utilities, or benchmarking infra transactions.

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。

Evaluates credit fund returns with yield attribution, mark-to-market dynamics, and performance comparison across credit strategies. Use when analyzing credit fund performance, decomposing returns, or benchmarking credit 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。

Deploy machine learning models to edge devices using Google AI Edge Gallery, TensorFlow Lite, ONNX Runtime, and MediaPipe. Covers model quantization (INT8/INT4), on-device inference with Gemma 4 models, Android/iOS deployment via AI Edge Gallery, hardware delegate selection (GPU/NPU/DSP), and performance benchmarking on constrained devices. Use when deploying models to mobile phones, IoT devices, or embedded systems where cloud inference is impractical due to latency, cost, or connectivity 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。

Competitive reputation benchmarking against direct competitors

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 implementing pool/slab/arena allocators, tuning jemalloc/mimalloc/tcmalloc, writing a Rust GlobalAlloc, or benchmarking allocator performance and fragmentation.

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 writing, optimizing, or benchmarking a C++ CPU kernel with AVX2 or AVX512 intrinsics for the Hugging Face kernels ecosystem. Not for CUDA kernels: use cuda.

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。

When the user wants help with pricing decisions, packaging, monetization strategy, or subscription models. Also use when the user mentions 'pricing', 'price', 'monetization', 'freemium', 'Van Westendorp', 'how much should I charge', 'pricing tiers', 'good-better-best', 'SaaS pricing', 'subscription model', 'free vs paid', 'packaging', 'pricing page', 'annual vs monthly', or is deciding between free and paid models. Make sure to use this whenever someone is making ANY pricing decision — even questions like 'should I charge for this?' or 'is freemium right for us?' or 'my conversion rate is low, is it the price?' are pricing strategy questions. Covers value-based pricing, tier structure, price psychology, and competitor benchmarking.

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。

Perform triangulated cognitive research combining SQLite memory, GitHub 10k+ stars patterns, and ToT DAG benchmarking before critical architectural decisions. Automatically triggers subagent research escalation when confidence is low (<0.70). Keywords: research, benchmark, github, memory, triangulation, grounding, best-practices, escalation

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 validating, benchmarking, or monitoring LLM application performance. Keywords: RAG evaluation, LLM-as-a-judge, CI/CD gating, trajectory scoring, test suites, prompt quality.

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 task needs the judgment of a Compensation and Benefits Manager — designing or evaluating a pay structure/leveling system, benchmarking compensation against market, deciding on a benefits package tradeoff, or auditing for pay equity issues. Narrower and more specialized than the broader hr-people-manager role's compensation-adjacent heuristics.

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 an HR operations system for headcount planning effectiveness, attrition pattern detection, compensation benchmarking accuracy, workforce analytics maturity, and onboarding process optimization..

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。

Profile and audit mobile app performance -- cold/warm/hot startup time, memory leaks, battery drain, network efficiency, frame rate jank, and binary size. Triggers: diagnosing slow launches, investigating ANRs, reducing app size, or benchmarking rendering performance.

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。

Benchmark franchise locations by comparing top and bottom quartile performance across revenue, profitability, and operational scores.

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。