「experiments」の検索結果

Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.

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Archived DESIGN.md impact experiment compatibility gate. Use when an old Lab #01 run is requested, but do not generate new legacy 9/12-section documents; direct new experiments to the Core v2 benchmark track.

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AI-powered paywall optimizasyon. Sektore gore otomatik analiz, A/B test senaryolari, churn prediction sinyalleri, push notification stratejileri, agresif satis teknikleri ve RevenueCat Experiments entegrasyonu. paywall-strategy, revenuecat-patterns ve subscription-pricing skill'lerinin ustune insa eder.

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Autonomous experiment loop: hypothesize > modify > test > evaluate > keep/discard > repeat. Run N experiments automatically with measurable metrics. Works for performance optimization, A/B testing, prompt engineering, and any measurable improvement task.

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Run file-based C# apps with the .NET CLI when the user explicitly wants C#/.NET code without creating a project. Use for C# language/API experiments, one-file C# apps, small multi-file C# apps composed with `#:include`/`#:exclude`, or C# file-based apps linked with `#:ref`. Do not use for language-agnostic throwaway scripts, generic computations, Python/PowerShell-style automation, full projects, or existing app integration.

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Use when designing, implementing, reviewing, testing, or cleaning up feature toggles, feature flags, kill switches, runtime configuration gates, canary controls, staged rollouts, experiments, or temporary compatibility switches in Java enterprise systems. This should trigger for requests such as Design a feature toggle strategy; Review this feature flag; Add a kill switch safely; Test toggle on and off paths; Clean up an expired feature toggle; Plan controlled rollout and rollback behavior. Part of Plinth Toolkit

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Designs, sanity-checks and reads out product experiments (A/B tests, holdouts, pre/post, geo and incrementality tests) and produces an experiment brief plus a readout with a ship / hold / replicate call. Use when you ask 'should we A/B test this', 'how long should the test run', 'is this result real', 'sample size', 'stat sig', 'SRM', 'holdout', 'our win rate is low', or need to grade an existing test plan or readout. Draws on 191 insights from 71 Lenny's Podcast guests, led by Ronny Kohavi, Ramesh Johari and Archie Abrams.

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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.

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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.

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End-to-end paper reproduction from arxiv URL through smoke runs to replication experiments. Handles missing or partial official code, missing training scripts, missing hyperparameters, and private datasets via similar-public-dataset substitution. Use when the user asks to reproduce, implement, replicate, or re-run a paper from scratch, or pastes an arxiv URL with reproduction intent.

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Use when managing multiple initiatives across time horizons (now/next/later, H1/H2/H3), balancing risk vs return across portfolio, sizing and sequencing bets with dependencies, setting exit/scale criteria for experiments, allocating resources across innovation types (core/adjacent/transformational), or when user mentions portfolio planning, roadmap horizons, betting framework, initiative prioritization, innovation portfolio, or resource allocation across horizons.

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Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.

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Quantum computing framework for building, simulating, optimizing, and executing quantum circuits. Use this skill when working with quantum algorithms, quantum circuit design, quantum simulation (noiseless or noisy), running on quantum hardware (Google, IonQ, AQT, Pasqal), circuit optimization and compilation, noise modeling and characterization, or quantum experiments and benchmarking (VQE, QAOA, QPE, randomized benchmarking).

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Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.

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Use when building durable AI agents or agentic workflows with Inngest and AgentKit, including model calls, tool calls, multi-agent networks, human approval, realtime progress, provider rate limits, crash-safe execution, and Agent Evals handoff. Covers AgentKit, `step.ai`, `step.run`, `step.waitForEvent`, native realtime, and when to use lower-level Inngest primitives instead of an in-memory agent loop. Use `inngest-agent-evals` with this skill when the user wants scoring, sessions, experiments, deferred scorers, or outcome-based evaluation for the agent.

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ENCODE Portal REST API for regulatory genomics: TF ChIP-seq, ATAC-seq/DNase-seq peaks, histone marks, and RNA-seq across 1000+ cell types. Search experiments by assay/biosample/target; download BED/bigWig; retrieve SCREEN cCREs by region or gene. Use to annotate variants with regulatory tracks, find open chromatin in a cell type, or fetch peak files for ChIP/ATAC analysis. For regulatory variant scoring use regulomedb-database; for GWAS associations use gwas-database.

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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.

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Structured hypothesis formulation: turn observations into testable hypotheses with predictions, propose mechanisms, design experiments. Follows the scientific method. Use scientific-brainstorming for open ideation; hypogenic for automated LLM hypothesis testing on datasets.

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Three-tiered sgRNA design guide using validated Addgene sequences, CRISPick pre-computed datasets, or de novo design rules for CRISPR experiments

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Draft, restructure, and diagnose IEEE-style technical manuscript sections using a routed static/dynamic workflow. Use for IEEE conference, journal, Transactions, Letters, or magazine papers when writing titles, abstracts, introductions, related work, methods, experiments, conclusions, contribution statements, section outlines, or Chinese-to-English engineering paper drafts in AI, computer science, communications, control, signal processing, electrical/electronic engineering, power, robotics, circuits, and applied systems.

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Polish, translate, diagnose, and restructure technical manuscript text into IEEE-style academic English using routed failure-mode guidance. Use for IEEE abstracts, introductions, related work, methods, experiments, conclusions, figure captions, rebuttals, Chinese-to-English technical writing, AI-like or template-like academic prose, and engineering prose that needs clearer problem logic, contribution framing, method rationale, evidence discipline, natural author voice, or reviewer-ready wording.

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Draft, diagnose, and improve IEEE rebuttals, revision plans, cover letters, response letters, and point-by-point replies using routed reviewer-comment handling. Use when responding to IEEE reviewer or editor comments about novelty, scope, method, experiments, baselines, figures, tables, citations, writing, LaTeX formatting, limitations, compliance, technical correctness, or reviewer comments that appear generic, contradictory, unsupported, careless, or possibly AI-generated.

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Use the DVC repository skill for data versioning, pipelines, experiments, remotes/cache, metrics/params/plots, Python API usage, and DVC codebase maintenance.

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Routes CogDL graph-learning workflows for experiments, graph data, models, training wrappers, and pipeline apps.

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。