「baselines」の検索結果

Expert knowledge for Azure Policy development including troubleshooting, best practices, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when authoring JSON policies, deploying via ARM/Bicep/Terraform, enforcing security baselines, automating with Gatekeeper, or running policy-as-code in CI/CD, and other Azure Policy related development tasks. Not for Azure Blueprints (use azure-blueprints), Azure Role-based access control (use azure-rbac), Azure Resource Manager (use azure-resource-manager), Azure Security (use azure-security).

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Expert knowledge for Azure Attestation development including troubleshooting, best practices, security, configuration, and deployment. Use when validating attestation tokens, authoring policies, enforcing SGX/TPM baselines, or configuring private endpoints, and other Azure Attestation related development tasks. Not for Azure Confidential Computing (use azure-confidential-computing), Azure Virtual Enclaves (use azure-virtual-enclaves), Azure Dedicated HSM (use azure-dedicated-hsm), Azure Key Vault (use azure-key-vault).

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Captures, masks, compares, and updates screenshot baselines for web and mobile suites, with per-region thresholds and a reviewed-diff rule for every baseline change. Use when a screenshot assertion fails, a baseline needs updating, or visual checks are being added; not for deciding what to verify.

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Framework for measuring and tracking agent response quality over time. Detects regressions before they reach production. Use when evaluating agent changes, auditing quality, or establishing performance baselines.

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。

Diagnose MSBuild build performance bottlenecks using binary log analysis. USE FOR: identifying why builds are slow by analyzing binlog performance summaries, detecting ResolveAssemblyReference (RAR) taking >5s, Roslyn analyzers consuming >30% of Csc time, single targets dominating >50% of build time, node utilization below 80%, excessive Copy tasks, NuGet restore running every build. Covers timeline analysis, Target/Task Performance Summary interpretation, and 7 common bottleneck categories. Use after build-perf-baseline has established measurements. DO NOT USE FOR: establishing initial baselines (use build-perf-baseline first), fixing incremental build issues (use incremental-build), parallelism tuning (use build-parallelism), non-MSBuild build systems.

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Establish MSBuild/.NET build performance baselines before optimizing. USE FOR: a .NET build or solution that is slow or has build-performance concerns; cold, warm, incremental, or no-op measurements; before/after comparisons; CI build output caching; static graph build decisions; artifacts output; and dependency graph trimming. Start here before build-perf-diagnostics, incremental-build, or build-parallelism. DO NOT USE for webpack, npm, JavaScript bundlers, or other non-MSBuild build systems. For detailed target/task/analyzer bottleneck analysis after baselining, use build-perf-diagnostics.

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Add, repair, or review Playwright visual regression tests for browser-facing .NET apps, including screenshot baselines, Pixelmatch thresholds, deterministic rendering, and GitHub Actions artifacts. USE FOR: toHaveScreenshot, page.screenshot visual checks, Pixelmatch/pngjs comparison scripts, visual baseline updates, screenshot diff triage, or CI workflows for UI regression screenshots. DO NOT USE FOR: pure unit tests, accessibility audits, browser-debugging sessions, or frontend linting.

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Use when you need to add or review Flyway database migrations in a Quarkus application — quarkus-flyway extension, db/migration scripts, quarkus.flyway.* configuration, migrate-at-start, and alignment with JDBC or Panache. This should trigger for requests such as Add or review Flyway migrations in a Quarkus project; Configure quarkus-flyway or db/migration layout; Add versioned Flyway SQL migrations for Quarkus; Review Quarkus database migration ordering; Configure Flyway clean migrate or baselines in Quarkus. Part of Plinth Toolkit

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Use when the user wants to design experiments, plan ablation studies, structure baselines, or create incremental evaluation strategies. Triggers on phrases like "design ablation", "plan experiment", "what experiments should I run", "baseline comparison", or "experiment matrix".

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Design, audit, and strengthen experimental validation for IEEE manuscripts using routed claim-evidence checks. Use when planning experiments, selecting baselines, designing ablations, choosing metrics, checking fairness, adding robustness tests, explaining results, analyzing complexity, building claim-to-evidence matrices, or responding to reviewer concerns about insufficient experiments.

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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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Read, decompose, and analyze IEEE-style papers using routed technical-reading workflows. Use when extracting problem setting, object-method-condition logic, contributions, method pipeline, equations, datasets, baselines, metrics, ablations, limitations, replication details, citation role, related-work positioning, implementation notes, or reviewer-style assessment from a paper, PDF, abstract, or manuscript excerpt.

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Check, clean, verify, and improve IEEE citations, references, BibTeX entries, citation positioning, related-work logic, DOI metadata, venue names, and reference formatting using routed citation and metadata guidance. Use when preparing IEEE manuscripts, fixing bibliography errors, verifying reference completeness, improving related work, checking whether citations support claims, or identifying missing classic/recent baselines.

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Evaluate WooAIAssistant against a structured scenario suite with hard invariants + LLM-as-judge rubric scoring. Runs live against the demo store + gpt-5.1 via the woo-mobile-ai backend wrapper, writes a JSONL run record, compares against stored baselines, and surfaces regressions. Always delegated to a subagent so the main context only sees the markdown report.

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Establishes CPU/GPU baselines before resource-intensive operations. Use before builds, training runs, or any task that pins cores or GPUs for over a minute.

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Optimize DSPy programs with dspy.GEPA — a reflective/evolutionary optimizer to consider against task-specific baselines within an authorized evaluation budget. Use when the user says optimize, compile, GEPA, reflective optimization, or "make this program better" and a DSPy program + metric + trainset exist.

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Design reproducible evaluations for AI agents with representative task sets, explicit rubrics, appropriate graders, baselines, regression gates, and failure analysis. Use when defining agent quality, comparing prompts or models, validating a release, measuring tool-use reliability, investigating regressions, or deciding whether an agent is ready for production.

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Think and work like an expert Machine Learning Engineer. Use when a task calls for Machine Learning Engineer judgment. Reasons from decision-policy framing, Google's Rules of ML, point-in-time data, and prefill/decode inference physics through GBDT/PyTorch baselines, vLLM/SGLang/KServe serving with FP8/AWQ quantization, hybrid-retrieval RAG, RAGAS and human-validated LLM judges, OpenTelemetry GenAI tracing, and post-Omnibus EU AI Act obligations while treating train-serve skew, temporal leakage, prompt injection, LLM nondeterminism, and degenerate feedback loops as first-class 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。

Think and work like an expert Computer Graphics Researcher. Use when a task calls for Computer Graphics Researcher judgment. Reasons from the rendering equation, Monte Carlo bias/variance, sampling theory, and BSDF energy conservation through pbrt-v4/Mitsuba 3 references, MIS/ReSTIR/path guiding, DXR 1.2 real-time stacks, 3DGS and NerfBaselines protocols, FLIP/ColorVideoVDP evaluation, OpenUSD/MaterialX/OpenPBR interchange, and ACES 2/OCIO color while treating unconverged references, unequal-time comparisons, denoiser and temporal-reuse bias, scene- vs display-referred color errors, and white-furnace energy failures as first-class failure modes.

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Think and work like an expert Computer Vision Scientist. Use when a task calls for Computer Vision Scientist judgment. Reasons from image formation, projective geometry (pinhole intrinsics and extrinsics, epipolar/PnP/bundle adjustment, similarity-scale ambiguity), and COCO/LVIS AP mechanics through DINOv3/SigLIP 2 foundation baselines, RF-DETR/YOLO26/SAM 3 models, COLMAP 4/GLOMAP and VGGT geometry, pycocotools/TrackEval/BOP evaluation, and CVPR reporting and EU AI Act limits while treating train–test and pretraining leakage, AP evaluation-setting gaming, preprocessing mismatches (EXIF, BGR, aliased resizing), label noise, and camera-convention and scale errors as first-class 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。

Think and work like an expert Conservation Biologist. Use when a task calls for Conservation Biologist judgment. Reasons from IUCN Red List A–E and Green Status recovery metrics, PVA/Ne, occupancy and distance sampling (unmarked, msocc, RMark), prioritizr/Marxan SCP, Conservation Evidence and ROSES synthesis, counterfactual impact evaluation, METT/SMART PAME, and eDNA false-positive models while treating pseudoreplication, GBIF effort bias, offset baselines, and Red List≠priority conflation as first-class 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。

Think and work like an expert Climatologist. Use when a task calls for Climatologist judgment. Characterizes climate via WMO CLINO baselines (1991–2020 vs 1961–1990), ETCCDI indices, and teleconnection modes; bridges ERA5 climatology to CMIP6/ScenarioMIP SSP deltas (xsdba/QDM), optimal-fingerprint attribution, AR6 ERF/ECS/TCR, and proxy reconstructions (CPS/EIV, PAGES2k, MXD divergence)—distinct from weather forecasting and generic physical-climate narration.

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Proteina-Complexa flow-based protein backbone generation with fold-conditioned sampling guidance. Use this skill when: (1) Generating de novo protein backbones with hierarchical fold conditioning, (2) Exploring long-chain backbone generation beyond standard diffusion baselines, (3) Using NVIDIA Proteina-style flow matching workflows for controllable backbone design, (4) Comparing flow-based backbone generation against RFdiffusion or BoltzGen, (5) Prototyping fold-guided backbone campaigns before sequence design. This skill is based on the public NVIDIA Digital Bio Proteina project and uses "Proteina-Complexa" as the BioClaw-facing skill label. For sequence design after backbone generation, use proteinmpnn or solublempnn. For QC thresholds, use protein-design-qc.

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 visual regression testing with Playwright screenshots, Chromatic, Percy, and Argos CI. Covers baseline management, diff threshold tuning, dynamic content masking, responsive viewport testing, and review/approval workflows. Use when: "visual test," "screenshot," "visual regression," "pixel diff," "snapshot diff," "update baselines," "Chromatic," "percy snapshot," "argos screenshot." Not for: bulk baseline regeneration after a redesign broke many tests — use selector-drift-recovery; cross-browser rendering matrices — use cross-browser-testing; general Playwright test structure — use playwright-automation. Related: playwright-automation, ci-cd-integration, cross-browser-testing.

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。