「core」の検索結果

Detects TAD boundaries from balanced Hi-C contact matrices via the diamond-window insulation score (cooltools insulation) and HiCExplorer hicFindTADs, returning a continuous log2 insulation track, valley-prominence boundary_strength, and Li/Otsu-thresholded is_boundary flags across a list of window sizes. Covers the multi-scale window sweep (sub-TAD to compartment-domain), why the boundary is reproducible but the domain partition is not, cross-condition comparison via differential SCORE not differential partition, and the insulation-vs-compartment orthogonality. Use when calling TADs or domain boundaries, computing insulation scores, choosing a window size, ranking boundary strength, comparing boundaries across conditions, or annotating CTCF-backed boundaries; route domain rendering to hic-visualization and boundary-feature overlap to genome-intervals.

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Rank and prioritize neoantigen/epitope candidates by likely T-cell response using NeoFox feature annotation, PRIME2.0, BigMHC-IM, the Łuksza/Balachandran fitness model (agretopicity + foreignness), and pVACtools tiering. Encodes the field's hard truths that immunogenicity is the least-solved layer (dedicated scores ~AUROC 0.6-0.7, modest PPV), that scores are valid only for RANKING within one patient (never absolute go/no-go or cross-patient), that DAI has anchor-inflation and WT-denominator traps, and that stacking weak correlated scores into one number is a red flag. Use when ordering a candidate list for a vaccine. Binding lives in mhc-binding-prediction; calling in neoantigen-prediction.

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Implement computer vision features including text recognition (OCR), face detection, barcode scanning, image segmentation, object tracking, and document scanning in iOS apps. Covers both the modern Swift-native Vision API (iOS 18+) and legacy VNRequest patterns, VisionKit DataScannerViewController for live camera scanning, and CoreMLRequest/VNCoreMLRequest for custom model inference. Use when adding OCR, barcode scanning, face detection, or custom Core ML model inference with Vision.

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Calculate a productivity score using actual Agent Monitor metrics — session completion rates, cache efficiency (cache_read vs input), compaction pressure (baseline tokens), turn velocity (turn_count / total_turn_duration_ms), tool success ratio (PreToolUse vs PostToolUse), and the workflow intelligence API's complexity and effectiveness scores.

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When the user wants to optimize Core Web Vitals, fix LCP, INP, or CLS issues. Also use when the user mentions "Core Web Vitals," "CWV," "LCP," "INP," "CLS," "FID," "page speed," "page performance," "Largest Contentful Paint," "Interaction to Next Paint," "Cumulative Layout Shift," or "Page Experience." For GSC CWV, use google-search-console.

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Audits existing table permissions on a Power Pages site by analyzing them against site code and Dataverse metadata, then GRADES the configuration with deterministic 1–5 scores across 3 security/usability/correctness categories (9 dimensions). Detection is anchored by a recurring-issue catalog so findings and severities stay consistent run-to-run. Generates an HTML report with an at-a-glance verdict and per-category scores in the Overview plus a single list of **major/minor** issues (each major issue labelled permissions-only or upstream-caused with a short justification), and suggested fixes. Use when the user wants to review, verify, check, score, or grade table permissions for security issues.

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Produce the Part 3 Four Core Documents: business, segmentation, positioning, DMFlow. "produce the four core documents"

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Core ML, Create ML, Vision framework, Natural Language framework, on-device ML integration. Use when user wants image classification, text analysis, object detection, sound classification, model optimization, or custom model integration. Covers Core ML vs Foundation Models decision.

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MetaboCoreUtils defines metabolomics-related core functionality provided as low-level functions to allow a data structure-independent usage across various R packages. This includes functions to calculate between ion (adduct) and compound ma

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7-section readiness scorecard for a LOCAL repo. Reports repo type + agent topology + MCP risk + test confidence + release readiness + recommended harness plan + scorecard. Exit 0 ready, 1 needs-work, 2 blocked. --json for the 6-field scorecard, --bundle for the ADR-031 schema-1 envelope.

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5-dimension scorecard (0-100, grade A/B/C/F) for a scaffolded harness. Dimensions: Repo understanding (25%), Agent usefulness (25%), MCP safety (20%), Test coverage (15%), Publish readiness (15%). Emits a 6-field badges block (score + mcpRisk + 4 booleans) ready for the harness README. Exit 0 A/B, 1 C, 2 F.

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Use when an ad account needs a systematic CONFIGURATION audit — 84 checkpoints across account, campaign, ad set, ad, tracking, and optimization, scored into a Health Score out of 100, quick wins ranked by impact, regional benchmarks, and a dropshipping checklist, covering Meta, Google, and TikTok. Trigger on 'ads audit', 'audit my ad account', 'is my Meta setup correct', 'health score for the account', 'review my Google Ads', 'we inherited this account and it is a mess'. Not for — diagnosing why the numbers moved this week, see `03-performance-eval-global`; planning fresh budget, see `54-media-plan-global`; rebuilding the campaign hierarchy, see `52-account-structure-global`.

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Self-evolving skill system. Skills are scored after execution (0-100) on 5 dimensions. Score 90+ over 5 runs = crystallized (locked). Score below 30 = auto-repair attempted. Skills improve themselves through usage feedback.

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Migrate a .NET 8 project to .NET 9 and resolve all breaking changes. USE FOR: upgrading TargetFramework from net8.0 to net9.0, fixing build errors after updating the .NET 9 SDK, resolving behavioral changes in .NET 9 / C# 13 / ASP.NET Core 9 / EF Core 9, replacing BinaryFormatter (now always throws), resolving SYSLIB0054-SYSLIB0057, adapting to params span overload resolution, fixing C# 13 compiler changes, updating HttpClientFactory for SocketsHttpHandler, and resolving EF Core 9 migration/Cosmos DB changes. DO NOT USE FOR: .NET Framework migrations, upgrading from .NET 7 or earlier, greenfield .NET 9 projects, or cosmetic modernization unrelated to the upgrade.

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Guides creation and modification of ASP.NET Core Web API endpoints with correct HTTP semantics, OpenAPI metadata, and error handling. USE FOR: adding new API endpoints (controllers or minimal APIs), wiring up OpenAPI/Swagger, creating .http test files, setting up global error handling middleware. DO NOT USE FOR: general C# coding style, EF Core data access or query optimization (use optimizing-ef-core-queries), frontend/Blazor work, gRPC services, or SignalR hubs.

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Write, review, filter, mock, and debug Core unit tests with the repository's installed Vitest 5 configuration. Use for tests under Core's Vitest include paths, `vi.mock`/spies/timers, jsdom component tests, coverage output, focused reruns, or unit-test failures. Do not use for browser E2E flows or async Next.js Server Components.

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Build and maintain Core UI with the ReUI registry and authenticated ReUI MCP. Use for new pages/sections/features, ReUI components/examples/Pro blocks, @reui installs, REUI_LICENSE_KEY setup, and ReUI API or documentation research. Preserve exact base-maia/Base UI, packages/ui ownership, existing shared tables, and Core tokens; use live APIs and worked examples.

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Compute the bacterial pan-genome from Prokka/Bakta GFF3 annotations with Roary's CD-HIT + BLAST + MCL clustering pipeline. Builds gene presence/absence matrices, core/soft-core/shell/cloud partitions, multi-FASTA core gene alignments (with `-e`), and a pan-genome reference. Use Panaroo for higher-accuracy pan-genomes from highly fragmented assemblies, PIRATE for paralog-aware clustering, or PPanGGOLiN for graph-based partitioning.

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AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. Use when working with any AgentCore service including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, or Evaluations. Covers agent deployment, MCP tool integration, credential management, agent discovery, governance workflows, and automated quality assessment. Essential when user mentions AgentCore, agent runtime, agent registry, agent evaluation, MCP gateway, deploy agent, register MCP server, discover agents, evaluate agent quality, agent credentials, or wants to build, deploy, catalog, or monitor AI agents on AWS.

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Score a website's AI answer-engine visibility 0–100 against the open AIV rubric, and, with the user's own API keys, check and track through the OpenAI, Perplexity, Gemini and Anthropic APIs whether AI engines actually cite it. Use when the user wants to know whether AI engines can find, parse, trust and cite their site, or whether they do. Triggers on: 'does ChatGPT cite us', 'AI citation rate', 'share of voice in AI answers', 'track AI visibility weekly', 'AIV', 'AI visibility', 'GEO audit', 'generative engine optimization', 'AEO', 'llms.txt', 'will AI cite my site', 'AI search ranking', 'get cited by ChatGPT'.

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Creates structured hiring scorecards for any role. Takes job title, requirements, and team context. Generates comprehensive scorecard with weighted scoring rubric, interview questions per competency, evaluation matrix, red/green flags, and reference check questions.

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API reference: Core Animation (QuartzCore). Query for CALayer, CAAnimation, CABasicAnimation, CAKeyframeAnimation, CASpringAnimation, CATransaction, CAShapeLayer, CAGradientLayer, CAEmitterLayer, CATransform3D, CADisplayLink.

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Applies Functional Core, Imperative Shell to isolate logic from side effects. Use when business logic is entangled with I/O or unit tests are slow and brittle.

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Score and filter topics for X content creation using weighted criteria. Use when user wants to evaluate collected materials, filter topics by score, or mentions "filter topics", "score materials", "x-filter", "选题筛选". Applies 10-point scoring system with customizable weights.

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