「metrics」の検索結果

Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.

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Evidence-before-action diagnosis of failing ML experiments. Probes the system before guessing causes, process list, dmesg, GPU stats, log scrollback, checkpoint state, then states a hypothesis as a hypothesis and runs a smoke before claiming a root cause. Use when the user asks why a run is failing, diverging, OOMing, hanging, slow, producing weird metrics, has crashed, or asks to debug, diagnose, troubleshoot, or investigate a training issue.

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。

Same-epoch comparison of training runs across wandb, neptune, tensorboard, or mlflow. Aligns runs at the student's current step (never current-vs-final-of-baseline) and separates proxy metrics from downstream targets. Use when the user asks to compare runs, check if a run is improving, track lag against a baseline, rank experiments, or evaluate run-vs-run 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。

Model-guided autonomous root-cause analysis over a UModel object-graph semantic layer. Use with `umodel-query` when diagnosing service degradation, SLO breach, latency/error spikes, incidents, outages, or questions like why a service is slow, degraded, erroring, or timing out. The skill guides evidence collection across object graph, metrics, logs, topology, runbooks, deployments, config changes, business traffic, and runtime capacity; it is especially useful for ruling out red herrings such as recent but unrelated deployments. Triggers: root cause, RCA, incident, SLO breach, outage, postmortem, latency spike, error spike, timeout, 根因分析, 故障排查, 告警定位, 为什么慢, SLO 击穿.

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。

Token holder distribution, concentration metrics, insider detection, and supply analysis for Solana tokens

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。

High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics

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。

Set up Prometheus for comprehensive metric collection, storage, and monitoring of infrastructure and applications. Use when implementing metrics collection, setting up monitoring infrastructure, or configuring alerting systems.

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。

Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms

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 targeted linting, formatting, and code quality checks on modified files. Use this to validate code style, type safety, security, and other quality metrics before committing. Supports running all checks or targeting specific checks on specific files for efficient 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。

Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.

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 comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

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。

Tracks LLM token consumption and usage metrics for billing, monitoring, and optimization. Use this to log token usage, calculate costs, generate invoices, and understand which agents or users consume the most resources.

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。

This skill calculates key financial ratios and metrics from financial statement data for investment analysis

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。

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

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Access AlphaFold's 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.

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Complete Product Owner / Product Manager capability — a wiki-style knowledge map of product practice in active software development: the role and its boundaries, strategy cascade (vision → OKRs → roadmap → backlog), continuous discovery, backlog craft (epics, INVEST stories, acceptance criteria), prioritization frameworks (RICE, MoSCoW, Kano, WSJF), sprint delivery, metrics (North Star, AARRR), stakeholder management, and anti-patterns. The PO/PM owns what gets built, in what order, and why — including the end-to-end user flows and user journeys. Use when scoping a product or feature, decomposing an idea into outcomes/epics/stories, prioritizing a backlog, defining an MVP, mapping user journeys, measuring success, or saying no to a stakeholder. Pairs with super-ui-ux-design, which owns how each screen looks and behaves.

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。

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search 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。

Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.

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 Vercel cost and performance optimization on deployed projects, especially Next.js, SvelteKit, Nuxt, and limited Astro apps. Collect Vercel metrics, usage, project config, and code scan results first; investigate only metric-backed candidates; produce ranked recommendations grounded in verified files and version-aware Vercel/framework docs. Trigger for Vercel bill reduction, slow or expensive routes, caching opportunities, Function Invocations, Build Minutes, Fast Data Transfer, Core Web Vitals, Bot Management, Fluid compute, or cost breakdown requests.

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。

Therapeutics Data Commons (TDC) AI-ready drug discovery datasets. Curated ADME, toxicity, DTI, DDI with scaffold/cold splits, standardized metrics, molecular oracles, and ADMET benchmarks for therapeutic ML and property prediction. For chemical database queries use chembl-database-bioactivity; for featurization use molfeat.

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Medicinal chemistry filters for compound triage. Drug-likeness rules (Lipinski Ro5, Veber, Oprea, CNS, leadlike, REOS, Golden Triangle, Ro3), structural alerts (PAINS, NIBR, Lilly Demerits), chemical group detectors, complexity metrics, and filter composition query language. Built on RDKit/datamol. For hit-to-lead filtering, library design, ADMET pre-screening. For molecular I/O use rdkit-cheminformatics or datamol.

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。

Pipeline for Neuropixels extracellular electrophysiology: probe geometry (ProbeInterface), Kilosort sorting via SpikeInterface, quality metrics, unit curation (ISI, firing rate, SNR), post-sort analysis (PSTH, tuning curves, population decoding). Supports Neuropixels 1.0/2.0/Ultra in rodent/primate experiments.

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。

Unified Python framework for extracellular electrophysiology. Load 20+ formats (SpikeGLX, OpenEphys, NWB, Intan, Maxwell, Blackrock), preprocess, run 10+ sorters (Kilosort4, SpykingCircus2, Tridesclous, MountainSort5) via one API, compute quality metrics (SNR, ISI, firing rate), compare sorters, export NWB/Phy. For format-agnostic multi-sorter workflows. For Neuropixels-specific PSTH/decoding use neuropixels.

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。

Extract GWP, life-cycle stages, certifications, and impact metrics from an EPD PDF. Use when given a declaration to parse; not to find or compare EPDs.

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。