「sca」の検索結果

Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.

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Use when designing how an Agentforce agent transfers the conversation to a human agent (Omni-Channel), to another bot/agent, or to an alternate workflow — including context package, deflection, escalation triggers, and user messaging. Triggers: 'agent to human handoff', 'agentforce escalate to omni channel', 'agent to agent handoff', 'transfer conversation with context', 'agent deflection fallback'. NOT for topic scope and selector design — use agentforce/agent-topic-design. NOT for the overall Einstein Bot plus Agentforce estate topology — use architect/conversational-ai-architecture.

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Use this skill when designing, auditing, or troubleshooting behavioral boundaries for Agentforce agents — including topic (now subagent) Scope fields, agent-level system instructions, topic/action filters, the Escalation topic, restricted topics, and Instruction Adherence monitoring. Trigger keywords: agent guardrails, agent out of scope, restrict agent behavior, topic scope, subagent scope, agent fallback, Escalation topic, abuse prevention, action filters. NOT for Trust Layer content filtering (Einstein Trust Layer is a separate product concern), NOT for general topic routing design (use agentforce/agent-topic-design), NOT for prompt template construction (use agentforce/prompt-builder-templates).

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Configuring Salesforce case management: case queues, assignment rules, escalation rules, auto-response rules, Email-to-Case, Web-to-Case, case teams, entitlements, and milestones. Use when setting up or troubleshooting the Service Cloud case handling layer. Trigger keywords: email-to-case, web-to-case, escalation rules, case teams, entitlements, milestones, auto-response, case queue. NOT for Standard vs On-Demand Email-to-Case and routing-address setup — use admin/email-to-case-configuration. NOT for routing case work items to agents — use admin/omni-channel-routing-setup.

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Run a one-shot buying-intent lead scan across Reddit, Twitter/X, Instagram, and TikTok using Xpoz. Finds fresh posts from people actively looking for what a product does or frustrated with its competitors, classifies and prioritizes them, and reports where to engage and who to reach. Use when asked to "find leads", "who is asking for a tool like mine", "find people complaining about [COMPETITOR]", "buying-intent scan", or "social selling opportunities".

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Apply a refined Scandinavian visual system with a neutral black-and-white foundation, restrained sans-serif typography, purposeful product imagery, generous spacing, clear page chapters, and minimal decoration. Use when the user invokes /scandinavian-design or requests a Nordic, monochrome, calm, spacious, restrained, or minimalist interface.

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Fetch external sources (web / arXiv / GitHub) and turn them into scanner-gated wiki CANDIDATES — never canonical pages. Every fetched byte is DATA; url-guarded, size-capped, redirect-re-validated, injection-scanned, and secret-scrubbed before a candidate is even created. Requires network. NOT for capturing local knowledge (see mk:wiki); NOT for one-shot page-to-markdown (see mk:web-to-markdown).

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Advanced vulnerability analysis principles. OWASP Top 10 baseline + current threat landscape awareness, Supply Chain Security, attack surface mapping, risk prioritization. Use when performing security audits, threat modeling, or OWASP reviews. NOT for STRIDE/CSO-mode infrastructure audits (see mk:cso).

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Use when stuck on approach (not debugging). Strategic unsticking via 7 non-default techniques: simplification cascades, collision-zone thinking, meta-pattern recognition, inversion, scale game, first principles, via negativa. Triggers on complexity spiraling, innovation block, recurring patterns across domains, forced-assumption solutions, scale uncertainty, told-it's-impossible, or bloated systems needing subtraction. For evidence-based root-cause debugging, use mk:sequential-thinking instead.

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Domain-aware complexity routing. Scans task description for domain keywords, matches against domain-complexity.csv, returns complexity level, workflow intensity, and execution tier. Extends Phase 0 orchestration.

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Use when creating a new project from scratch — orchestrates the full journey from idea to running code. Research → design → plan → scaffold → implement → docs. Explicit invocation only — never auto-activates. CLI = project infrastructure (.claude/), bootstrap = application code + full pipeline. NOT for autonomous multi-hour builds of specified products (see mk:autobuild); NOT for single-task feature work on existing projects (see mk:cook).

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Operational decision frameworks: triage, escalation, case management, billing ops. Use for 'how to handle X cases', 'build triage', 'escalation protocol'.

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Scan the repository for hardcoded secrets, exposed API keys, insecure network configurations, and vulnerable dependencies. Use when the user asks for a security audit, secret detection, credential scan, vulnerability check, or wants to find leaked tokens in the codebase.

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Warehouse cost reduction, auto-scaling, query optimization, and lifecycle policies for data infrastructure. Activate on: data cost, warehouse credits, cost reduction, auto-scaling, lifecycle policy, cold storage, cost monitoring, resource optimization. NOT for: query performance tuning (use data-warehouse-optimizer), batch job optimization (use batch-processing-optimizer).

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Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Capblancq & Forester 2021 RDA Swiss-army-knife, genomic-offset prediction with Lind & Lotterhos 2025 three-regime caveat, Lotterhos-Whitlock sampling optima, Wang & Bradburd 2014 IBD vs IBE, and Circuitscape + ResistanceGA. Use when identifying adaptive loci across gradients, choosing K for LFMM2, deciding among GEA methods, predicting maladaptation with the novel-environment caveat, distinguishing IBD vs IBE, or optimizing sampling design.

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Visualizes ChIP-seq data using deepTools (computeMatrix, plotHeatmap, plotProfile, bamCoverage, bamCompare), pyGenomeTracks (modern INI-driven track plots), Gviz (R browser-style), EnrichedHeatmap (ComplexHeatmap-based), ChIPseeker tag heatmaps, and IGV batch screenshots. Handles bigWig normalization choices (CPM, BPM, RPGC, spike-in scaled), bamCompare operations (log2 ratio, subtract) with SES scaling, k-means clustering of heatmaps for biological subgrouping, and spike-in-scaled tracks for global-shift experiments. Use when generating publication-quality ChIP-seq signal heatmaps, profile plots, genome-browser tracks, or comparing samples visually.

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Runs cis-pQTL Mendelian randomization for drug-target validation using UKB-PPP (Olink), deCODE (SomaScan), Fenland, INTERVAL, ARIC, and FinnGen-PPP proteomes plus colocalization triangulation, phenome-wide on-target adverse-effect scans, cross-platform Olink/SomaScan replication, and PAV (protein-altering variant) sensitivity. Use when nominating or de-risking a drug target from plasma-proteome GWAS, mimicking pharmacological inhibition via cis-pQTL instruments, separating shared-causal from LD-confounded signal under the Schmidt 2020 cis-MR framework, screening on-target adverse phenotypes pheWAS-style, or producing publication-grade STROBE-MR plus PP.H4 evidence for a target gene.

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Scans CopilotKit's public product / pricing / Premium pages at the start of every weekly report to produce the authoritative list of COMMERCIAL surfaces (Premium · CopilotKit Enterprise · Intelligence Platform · paid tiers) and detect any newly-added or changed commercial feature since last week. This list defines what the report's 🏢 Enterprise section is about — CopilotKit's commercial/paid product, NOT "CopilotKit running inside an enterprise company." Emits the surface list + a free-vs-paid classifier the report uses to decide "does this issue hit a commercial surface?", plus a diff of what changed. Runs as a subagent (many web fetches) so the orchestrator's context stays small. Invoked by weekly-report (spawned at report start) and the enterprise skill; also triggers on "scan product surfaces", "what's premium", "commercial surfaces", "did we add a premium feature".

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Padrões especializados para implementação de busca Algolia, estratégias de indexação, React InstantSearch e ajuste de relevância. Use quando: adicionar busca, algolia, instantsearch, search api, funcionalidade de busca.

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Orientação de liderança técnica para equipes de engenharia, decisões de arquitetura e estratégia de tecnologia. Inclui analisador de débito técnico, calculadora de escalabilidade de equipes, frameworks de métricas de engenharia, ferramentas de avaliação de tecnologia e templates de ADR. Use ao avaliar débito técnico, escalar equipes de engenharia, avaliar tecnologias, tomar decisões de arquitetura, estabelecer métricas de engenharia ou quando o usuário menciona CTO, débito técnico, débito técnico, escalabilidade de equipes, decisões de arquitetura, avaliação de tecnologia, métricas de engenharia, métricas DORA ou estratégia de tecnologia.

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Banco de dados de embeddings de código aberto para aplicações de IA. Armazene embeddings e metadados, realize buscas vetoriais e full-text, filtre por metadados. API simples com 4 funções. Escala de notebooks para clusters de produção. Use para busca semântica, aplicações RAG ou recuperação de documentos. Melhor para desenvolvimento local e projetos de código aberto.

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Processamento escalável de dados para workloads de ML. Execução em streaming em CPU/GPU, suporta Parquet/CSV/JSON/imagens. Integra-se com Ray Train, PyTorch, TensorFlow. Escala de uma única máquina para centenas de nós. Use para inferência em lote, pré-processamento de dados, carregamento de dados multi-modal ou pipelines distribuídos de ETL.

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Orquestração de treinamento distribuído em clusters. Escala PyTorch/TensorFlow/HuggingFace do laptop para milhares de nós. Ajuste de hiperparâmetros integrado com Ray Tune, tolerância a falhas, escalabilidade elástica. Use ao treinar modelos massivos em múltiplas máquinas ou executar varreduras distribuídas de hiperparâmetros.

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Strategy cascade based on Roger Martin and A.G. Lafley's Playing to Win framework. Five interconnected choices that define a real strategy — not a vision statement, not a list of priorities, not a strategic plan that's really just a budget with adjectives. Runs in three modes — Diagnostic for building or rebuilding the full cascade, Review for pressure-testing an existing one, and Alert Triggers for defining the tripwires that tell you the cascade is breaking. Updates company-context.md after every run. Triggers on questions about strategy, strategic choices, winning aspiration, where to play, how to win, competitive strategy, or when someone says "we need a strategy" and means it. Based on practice by Eterdis (eterdis.com).

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