881 skills · sorted by relevance
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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".
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
Competitive reputation benchmarking against direct competitors
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
Use when implementing pool/slab/arena allocators, tuning jemalloc/mimalloc/tcmalloc, writing a Rust GlobalAlloc, or benchmarking allocator performance and fragmentation.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
Use when a visual needs repeatable fixed-view rendering and independent rubric scoring. Not for free-form visual review or subjective critique.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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)
The English summary is being prepared. Showing the original description.
Use when Odylith Discipline pressure behavior, host/lane parity, or credit-safe benchmark proof needs validation.
The English summary is being prepared. Showing the original description.
Use when Odylith Discipline pressure behavior, zero-credit hard laws, host/lane parity, or benchmark-proved learning-spine contracts need validation.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
Portfolio Analyzer — sector allocation analysis vs SPY benchmark, geographic exposure, correlation matrix, concentration risk (HHI), beta-weighted portfolio delta, dividend/income analysis, and prioritized rebalancing recommendations. Triggered by "trade portfolio" or "analyze my portfolio".
The English summary is being prepared. Showing the original description.
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。
The English summary is being prepared. Showing the original description.