「core」の検索結果

Full-site blog health assessment scanning all blog files for quality scores, orphan pages, topic cannibalization, stale content, and AI citation readiness. Runs canonical batch analysis before site-wide checks. Produces per-post scores and a prioritized action queue. Use when the user asks for a site-wide blog health check across many posts ("blog audit", "blog health", "audit all posts", "site audit of the blog"). For one post, or a quality score with fixes for a few named files, use blog-analyze instead.

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Scaffold and operate Claude Blog Brain, a source-cited Obsidian brain for blog content creation, optimization, and management dual-optimized for Google rankings (E-E-A-T, the 2026 core updates) and AI citations (GEO/AEO), spanning writing, rewriting and freshness, SERP-informed briefs and outlines, editorial calendars and strategy, semantic topic clusters, schema and internal linking, multilingual publishing, the FLOW framework, factchecking, personas, distribution, and the blog delivery contract, grounded in the claude-blog skill. Use when the user says "claude-blog-brain", "Claude Blog Brain", "create a blog content creation, optimization, and management dual-optimized for Google rankings (E-E-A-T, the 2026 core updates) and AI citations (GEO/AEO), spanning writing, rewriting and freshness, SERP-informed briefs and outlines, editorial calendars and strategy, semantic topic clusters, schema and internal linking, multilingual publishing, the FLOW framework, factchecking, personas, distribution, and the blog delivery contract, grounded in the claude-blog skill brain", "import sources", "synthesize plan", "render report", or wants a persistent vault-backed operating system for blog content creation, optimization, and management dual-optimized for Google rankings (E-E-A-T, the 2026 core updates) and AI citations (GEO/AEO), spanning writing, rewriting and freshness, SERP-informed briefs and outlines, editorial calendars and strategy, semantic topic clusters, schema and internal linking, multilingual publishing, the FLOW framework, factchecking, personas, distribution, and the blog delivery contract, grounded in the claude-blog skill.

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Use this skill when configuring or troubleshooting FSC Referral Management: setting up referral types via ReferralRecordTypeMapping custom metadata, routing referrals through Lead Assignment Rules keyed on the Expressed Interest picklist, tracking Referrer Score, and partner referral visibility in Experience Cloud. Trigger keywords: referral routing, FSC referral types, ReferralRecordTypeMapping, Expressed Interest picklist, partner referral Contact, Referrer Score Experience Cloud. NOT for clinical referrals in Health Cloud — use admin/referral-management-health. NOT for standard Lead capture and conversion outside FSC — use admin/lead-management-and-conversion.

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Auto-assembles a domain-specific expert panel (7–10 experts), scores any content or strategy artifact against a typed rubric, and iterates until the aggregate hits 90+ (max 3 rounds). Use as a quality gate on copy, email sequences, landing-page drafts, strategy docs, charts, titles, or recruiting evaluations — or when another skill needs a final review gate on its output. Triggers on "expert panel this", "score this", "rate these variants", "quality check this", "panel review", "expert score", "evaluate this copy/strategy/page". For variant generation and multi-round conversion optimization see autoresearch; for live-URL CRO auditing see conversion-ops; for the scripted content-production pipeline see content-pipeline.

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Score an Instagram creator's (or a list of creators') audience authenticity and engagement quality using Xpoz. Measures real engagement rate against views rather than follower count, the bot/spam share of their comments, and an overall 0-100 authenticity score for influencer vetting and partner selection. Use when asked to "vet this influencer", "are their followers real", "check engagement quality for @username", "rate these Instagram accounts", "compare creators before a sponsorship", or when the user pastes a list of Instagram handles to evaluate.

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Decide when to order coronary artery calcium (CAC) scoring in a primary-prevention patient and interpret the result per the 2026 ACC/AHA multisociety dyslipidaemia guideline. Covers the Class 1 indication (men ≥40 / women ≥45, borderline / intermediate PREVENT-ASCVD risk, uncertainty about LLT), CAC score interpretation (0, 1–99, 100–299, ≥300, ≥1000), how CAC modifies LDL and non-HDL goals, when to repeat CAC, and pitfalls (incidental CAC on non-cardiac CT, radiation, cost). Use when a clinician asks should I order a CAC score, CAC indications, what does CAC 150 mean, treatment goals with CAC score, repeat CAC frequency, CAC in a smoker, CAC vs CCTA, is CAC useful in intermediate risk. Grounded in the 2026 ACC/AHA multisociety dyslipidaemia guideline.

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Otimize Core Web Vitals (LCP, INP, CLS) para melhor experiência de página e ranking de busca. Use quando solicitado para "melhorar Core Web Vitals", "corrigir LCP", "reduzir CLS", "otimizar INP", "otimização de experiência de página" ou "corrigir deslocamentos de layout".

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Load this skill when configuring axe-core scans, reviewing automated accessibility test results, or writing tests that use @axe-core/playwright, @axe-core/react, or similar integrations. Provides a quick reference to axe 4.x rule IDs, their WCAG mapping, and default severity levels.

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Maintains repo-local action contracts and harness repositories where product CLI and MCP adapters stay thin over core libraries. Use when adopting or improving steward.yaml actions, capability-level adoption evidence, cold-start proof loops, probes, benchmarks, CLI/MCP/core parity, adapter refactors, packages/core boundaries, or sibling harness layout; use repo-quality-system-lifecycle first for general app/library/tool stewardship baselines.

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SOP for the micro-execution C.O.R.E. phase. Trigger when: - Implementing plan steps, managing code changes at commit boundaries, and verifying incremental progress. - Navigating states: Absorb, Clarify, Plan, Execute. - Prompt contains: /core, core workflow, absorb, clarify, execution invariants, commit boundary, verify assertion.

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Builds a DRAFT structured interview scorecard from a job description: five to eight competencies traced to it, past-behaviour and situational evidence questions with probes, four-level rating anchors written as observable behaviours, a blank scoring sheet, a panel plan and the items the hiring manager must confirm. Use when the user asks to "build an interview scorecard for this job description", "write competency questions for this role", "draft the interview guide for the panel", "set up a rating scale for the interviewers", "put together a structured interview kit" or "create a hiring rubric from the JD". Do not use for writing or fixing the job description itself, use job-description-drafter instead; for the new hire's first 90 days, use onboarding-plan-builder. Drafts for human review; never approves, authorises or signs off.

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Score how much attention a person deserves: classify them (investor, engineer, student, partner, community, noise, personal), estimate the value they can bring in the next 90 days, their temperature, and the next move. Triggers: /worth, how valuable is <who>, who is <who>, should I reply to <who>, score this person, score this lead.

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Front the studio as Manny the Manticore. Use when the user says "Manny", "Manticore", or "talk to Manny".

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Use when working with Aws Iot Core — aWS IoT Core management including things, thing groups, certificates, policies, rules, topic monitoring, device shadows, and fleet indexing. Covers device connectivity health, message throughput, rule action errors, certificate expiration, and shadow drift analysis.

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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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Optimize Entity Framework Core queries by fixing N+1 problems, choosing correct tracking modes, using compiled queries, and avoiding common performance traps. Use when EF Core queries are slow, generating excessive SQL, or causing high database load.

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Integrate on-device AI using Foundation Models framework, Core ML, and open-source LLM runtimes on Apple Silicon. Covers Foundation Models (LanguageModelSession, @Generable, @Guide, SystemLanguageModel, structured output, tool calling), Core ML (coremltools, model conversion, quantization, palettization, pruning, Neural Engine, MLTensor), MLX Swift (transformer inference, unified memory), and llama.cpp (GGUF, cross-platform LLM). Use when building tool-calling AI features, working with guided generation schemas, converting models, or running on-device inference.

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Anime.js v4 core animations: production-level target selection, property keyframes, duration, ease, loop, play/pause. Use when implementing, reviewing or testing anime.js v4 core animations for React, Vite, Next.js or Perfect_AI projects.

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Performs comprehensive FiveM resource security, performance, and compatibility audits. Detects backdoors, RATs, SQL injection, event exploitation, NUI vulnerabilities, dupes, crash/DoS vectors, npm and build-chain supply chain attacks, and malware patterns across ESX, QBCore, QBox, ox_lib, and ND_Core frameworks, on both GTA V Legacy and Enhanced builds. Use whenever a FiveM/cfx resource is being reviewed — even if not explicitly asked — including: audit FiveM script, review FiveM security, optimize FiveM resource, check FiveM performance, FiveM code review, review Lua script security, audit ESX resource, audit QBCore resource, audit QBox resource, check for exploits, FiveM vulnerability scan, GTA V Enhanced migration check, resmon optimization, or asking whether a leaked, cracked, nulled or unknown-origin script is safe to run.

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Tell the user (or an agent) exactly what machine they are on — chip, cores, RAM, NPU TOPS, memory bandwidth, security posture, DSH tier, and the right Ollama model to pull. Refreshes the canonical profile at agents/core/.mesh/machine.json on every call. Reads the result via agents.core.machine — zero re-probing in application code.

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Runs and configures curated nf-core community Nextflow pipelines (rnaseq, sarek, atacseq, methylseq, ampliseq, taxprofiler, fetchngs) reproducibly, pinning the pipeline revision with -r and selecting a container engine and institutional config via -profile. Use when deciding to adopt a community pipeline versus author one from scratch; picking a pipeline and pinning its -r revision; selecting -profile test/docker/singularity/conda plus an institutional config from nf-core/configs; building and validating a samplesheet CSV against the pipeline schema (nf-schema); choosing --genome/iGenomes versus custom references; configuring resources and max_memory for SLURM/AWS Batch; using -resume and -stub; and reading MultiQC outputs.

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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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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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Guides agents through scorecards, outcomes, model profiles, and eval suites. Use when running /cc-scorecard or /cc:scorecard, measuring a deliverable, or checking workflow gates with suite-run.

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