Results for “background”

Designs the first screen visitors see — the hero section. Layout patterns (split, centered, asymmetric, full-bleed), navbar design, headline typography, CTAs, background treatments (gradients, video, particles, 3D), scroll cues, and above-the-fold conversion strategy. Activate on 'hero section', 'landing page hero', 'above the fold', 'first impression', 'homepage header', 'navbar design', 'hero layout', 'landing page design'. NOT for full multi-page site architecture (use web-design-expert), not for SEO content strategy (use seo-visibility-expert).

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Auto-activate for litestar_queues, QueuePlugin, QueueConfig, WorkerConfig, QueueService, @task, QueuedBackgroundTask, QueueEventsConfig, or SQLSpecBackendConfig. Not for litestar-saq, Celery, or Dramatiq.

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Auto-activate for litestar_saq, SAQPlugin, SAQConfig, QueueConfig, TaskQueues, CronJob, litestar workers run, background jobs, schedules, or SAQ UI. Not for litestar-queues, Celery, RQ, or Dramatiq.

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Reliable progress watching for long-running background commands (training runs, live audits, batch jobs, CI) without losing output to stdout buffering. Prefer post-completion cat-and-grep over tail -F.

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Preserve output from expensive one-shot commands for monitoring and later analysis. Use when rerunning a build, API query, CI inspection, or diagnostic would be costly and a managed background process is not the better interface.

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Extracts, filters, annotates, and exports differential expression results from DESeq2 or edgeR with proper handling of padj=NA (independent filtering, Cook's outliers, all-zero), multiple-testing correction choice (BH vs Storey q-value vs IHW vs lfsr), TREAT vs post-hoc fold-change filtering, p-value histogram diagnostics, gene annotation via org.db/biomaRt/mygene, GSEA preranked input, ORA background construction, replication reality (Schurch 2016 small-n result), and SABV/sex-stratified reporting. Use when extracting and interpreting DE results, troubleshooting padj=NA, choosing FDR method, preparing ranked lists for pathway analysis, annotating gene IDs, or comparing DESeq2 vs edgeR outputs.

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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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Build sequence logos from aligned DNA, RNA, or protein motifs using ggseqlogo (R), Logomaker (Python), or WebLogo with explicit bits vs probability encoding, background-frequency correction, custom alphabets, and multi-logo stacking. Use when visualizing motif PWMs (TF binding, splice sites, CRISPR spacers), aligned-position composition, or comparing two motif sets.

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Discovers de novo motifs and tests known motif enrichment in ChIP-seq, ATAC-seq, or other peak sequences using HOMER, MEME-ChIP (STREME, CentriMo, TOMTOM, FIMO), monaLisa, and AME. Handles background selection (GC-matched, dinucleotide-shuffled, Markov order-2, peak-flanks), motif databases (JASPAR 2024 CORE PWMs, JASPAR 2026 deep-learning collection, HOCOMOCO v12, HOMER built-in), centrally-enriched motif testing, and differential motif analysis. Use when identifying TF binding motifs in peaks, testing for known TF enrichment, scanning for motif instances, comparing motif content between conditions, or interpreting motifs from deep learning models.

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Analyze TF motif accessibility variability across samples or single cells using chromVAR. Use when identifying TF motifs whose accessibility correlates with conditions, computing per-sample motif z-scores after matched background correction, comparing to ArchR / Signac equivalents, or distinguishing motif-accessibility signal from per-site footprinting.

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Design and implement backend service changes: API contracts, service boundaries, auth integration, reliability and delivery checks. Use for backend/API handlers, services, webhooks, jobs, or backend architecture changes. Routes schema/migrations to mk:database; security review to the existing security workflow.

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Use when writing or reviewing Go code in fusionaly-oss — adding routes, handlers, domain contexts, background jobs, the manager CLI, wiring the app, or writing tests. Covers the cartridge framework, matcha for the manager, Phoenix Contexts, lifecycle/shutdown, and the test conventions.

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Prevent session idle during background agent work via SubagentStop prompt hook auto-continuation

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Use when male Chinese web novel writing or 男频小说写作 needs contemporary Chinese 热梗, 网络流行语, 轻笑话, 网络热门事件模板, 网络热门人物口头禅, 知名人物事件的安全化改写或模仿, 弹幕, 评论区, 角色对白, 短视频文案, Fanqie male-rank market radar, or work-study notes on writing style, characters, background, outline, and themes for AI-assisted fiction writing.

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Build or modify backend APIs, services, jobs, persistence, and integrations while preserving contracts, authorization, data integrity, and failure behavior. Use for server-side features, database changes, background processing, backend defects, and service refactors; do not use for frontend-only or infrastructure-only work.

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Interactive QA session where user reports bugs or issues conversationally, and the agent files GitHub issues. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, file issues conversationally, or mentions "QA session".

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Use this skill to drive a research question (RQ) end-to-end: validate the RQ README, generate a fill batch-plan, start the Docker batch in the background, monitor progress, run aggregation, and propose findings updates. Trigger when the user says "RQ-N voranbringen", "run-rq", "fill RQ-N", "run RQ-N", "Forschungsfrage N starten", or names a specific RQ-N directory in research/.

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Automatically format code files after every edit using Prettier and ESLint. Active in the background whenever Claude edits a file. Formatting is silent — no prompts, no output unless a formatter fails. Use when you want consistent formatting enforced automatically during a coding session.

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Use when optimizing application performance — caching strategies, eager loading, query optimization, Redis patterns, or background job design.

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Edit an existing image — inpaint, background swap, variation — via providers that support it. Use when editing/modifying/inpainting an image or making variations.

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Phase exécution. Détecte le mode (epic/task/bug) selon le type d'issue et lance le bon mécanisme : loop driver background pour epic, code-dev synchrone pour task/bug. Usage: /implement <issue#>

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Investigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.

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The kpopper method: a knowledge record (GROUNDING.yaml) for work that gets revisited - what is known, where each piece came from, and what would make each conclusion wrong. Use when asked what kpopper is or how to keep a record, when choosing which kpopper skill fits a moment, or when no narrower one does. The occasions have their own skills: ground (read the record before answering), record (write what the work found), map (map existing materials), annotated-doc (a standalone HTML document with evidence), hub (the record's visual home), consolidate (hypotheses and merges), watch (background checks).

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Write what the work found into the project's knowledge record, so the next session inherits it instead of re-deriving it. Use the moment something worth keeping exists: a fact taken from a source, a rule worked out, a decision or conclusion, a measurement, a correction to a recorded value, or a question left open - and check for unrecorded findings before implementation, handoff (including a PR), or finishing work. Record within the user's write authorization. The first write creates GROUNDING.yaml. Covers one-report update, add, set, review, same and distinct, judgments with a falsifier, and background capture.

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