821 件 ・ 関連度順
概要と使いどころ
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。
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。
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。
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。
Benchmark external agent frameworks, auto-detect source type, scan for prompt injection, and convert findings into a concrete TDD upgrade backlog for agent-studio evolution.
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 the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the user asks to "score this paper draft", "evaluate against the benchmark", "compare two papers", or "run the autoraters".
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 and tune a running llama.cpp server for local coding agents: propose config (ctx, prompt cache, MTP), benchmark before/after, report deltas. Use to optimize llama-server for Claude Code or Aider. Don't use for vLLM, ollama, or non-llama.cpp.
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。
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
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。
Create or improve a Senpai target repository's program.md. Use this skill whenever the user wants to point Senpai at a fresh ML or research target repository, define the research objective, primary metric, benchmark contract, allowed edit boundaries, W&B reporting contract, or prepare a repo for autonomous advisor/student experiment loops.
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。
Use when a visual needs repeatable fixed-view rendering and independent rubric scoring. Not for free-form visual review or subjective critique.
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 agent strategies need a reproducible finite tournament under a frozen evaluation protocol. Not for open-ended or infinite matchups: use a continuous benchmark.
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 optimize code, make a path faster, make this as fast as possible, reduce allocations, repair a regression, profile a target, grill every inefficiency, or run a subsystem or repo-wide performance campaign against a measured floor; when a performance requirement, slowness report, Core Web Vitals miss, or profiling evidence identifies a bottleneck; or for an "extremely optimize" performance campaign, optimizing suspected hot paths without waiting for benchmarks, or estimating hot and complexity-neutral cold paths while refusing complexity theater. Not for remote, credential, publish, deploy, or irreversible changes.
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。
Step-by-step tutorial for adding a new lightweight SYCL JIT kernel for Intel XPU to sgl-kernel-xpu's python/sgl_kernel/jit module (SYCL source + Python wrapper + tests + benchmark)
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 Odylith Discipline pressure behavior, zero-credit hard laws, host/lane parity, or benchmark-proved learning-spine contracts need validation.
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 Odylith Discipline pressure behavior, host/lane parity, or credit-safe benchmark proof needs validation.
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 guide + benchmarked pitfalls for building Monte-Carlo / Simulated Annealing refinement layers on top of greedy hex-grid layouts for code visualization (packages/gui/src/store/hexLayout.ts, sandbox/hex-sandbox, future Rust ports). Use when: (1) planning to add MC/SA on top of an existing hex-grid pack, (2) deciding whether a simpler SA would match a hierarchical MC with force-directed + chain drag, (3) budgeting wall-clock for million-node cold-start, (4) debugging "blob turns Swiss-cheese" or "folders stop nucleating" during MC refinement, (5) choosing between folder cohesion and raw Σ-link-length as cost function. Contains four hard-won, quantified lessons and anti-patterns that would take a full day of experiments to rediscover.
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。
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
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。
当首次使用 msmodeling optix 的用户需要根据硬件、模型、负载和优化目标推荐 MindIE/vLLM 寻优参数、搜索范围、benchmark 侧字段或 config.toml 片段时使用。
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。