Results for “resolution”

Corrects fluorophore spillover (conventional compensation) or spectral overlap (spectral unmixing) and applies variance-stabilizing transforms (logicle/biexponential, arcsinh, log) for flow and mass cytometry. Covers spillover-matrix estimation from single-stain controls, AutoSpill, the spillover spreading matrix and why panel design (not compensation) bounds resolution, compensate-then-transform ordering, and arcsinh cofactor choice (5 for CyTOF, ~150 for fluorescence, per-channel via flowVS). Use when correcting spectral overlap, preparing data for gating/clustering, choosing logicle vs arcsinh, deciding a cofactor, or distinguishing compensation from spectral unmixing.

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Assigns pathogen lineages (SARS-CoV-2 Pangolin via UShER mode; Nextclade clade + QC; pango-designation alias_key.json resolution) and tracks variant frequencies over time using Nextstrain (Augur + Auspice), wastewater deconvolution (Freyja, COJAC, alcov, lineagespot), lineage fitness modelling (Wenseleers / Bedford-Figgins multinomial logistic), and recombinant detection (3SEQ, RDP4, Bolotie). Covers Pangolin pangolin-data version pinning (mandatory for reproducibility), Nextclade dataset versioning (lineage-defining mutations change with dataset), Freyja barcode forward-only date constraint, ARTIC primer scheme version churn (V3 / V4 / V4.1 / V5.3.2 / Midnight 1200) with documented dropout regions, recombinant X-prefix Pango designation lag, GISAID vs INSDC dual-deposition tensions, and the Karthikeyan 2022 wastewater early-detection signal with explicit reproducibility caveats. Use when assigning Pango lineages and Nextclade clades to viral consensus sequences, building Nextstrain Augur surveillance pipelines, deconvolving wastewater pooled samples into lineage frequencies with Freyja, tracking lineage frequencies and growth advantages over time, pinning pangolin-data / Nextclade dataset versions for reproducibility, handling ARTIC primer dropouts (V4.1 amplicons 64 / 76 / 88-90), or running variant surveillance for SARS-CoV-2 / influenza / Mpox / RSV / H5N1 / measles.

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Resolve subclonal copy number, whole-genome doubling, and copy-number tumor evolution from bulk sequencing with Battenberg, TITAN, and MEDICC2. Covers clonal versus subclonal copy-number states, haplotype phasing for subclonal resolution, cancer cell fraction, whole-genome-doubling detection and timing relative to mutations, mirrored subclonal allelic imbalance, and copy-number phylogenies. Use when a tumor is heterogeneous and bulk data shows non-integer copy number, when calling subclonal CNAs, detecting or timing whole-genome doubling, reconstructing copy-number evolution, or deciding between Battenberg and TITAN.

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Detect single-nucleotide crosslink (CL) sites in CLIP-seq data using truncation patterns (iCLIP/eCLIP CITS), crosslink-induced mutations (HITS-CLIP CIMS deletions, PAR-CLIP T-to-C), or HMM/kernel-density methods (PureCLIP, PARalyzer, CTK). Use when single-nucleotide resolution is required for motif registration (mCross), allele-specific binding (BEAPR), variant-effect prediction, or comparing crosslink chemistry across CLIP variants.

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Annotate CLIP-seq peaks or crosslink sites to RNA features (5'UTR, CDS, 3'UTR, intron, splice junction, snoRNA, tRNA, ncRNA, repeat elements) with ChIPseeker, RCAS, RBP-Maps (Yeo splicing regulatory maps), and bedtools, applying feature-priority hierarchies, transcript-context resolution, and metagene aggregation. Use when characterizing where in transcripts an RBP binds, comparing peak distribution across regions, generating splicing-regulatory maps relative to alternative-splicing events, or distinguishing exonic vs intronic vs UTR binding.

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Map N6-methyladenosine (m6A) RNA modifications at single-nucleotide resolution using miCLIP (Linder 2015), miCLIP2 + m6Aboost machine learning (Kortel 2021), GLORI (Liu 2023, antibody-free chemical conversion), DART-seq (Meyer 2019, APOBEC1-YTH fusion), m6Anet (nanopore direct RNA), or MeRIP-seq with calibration. Use when distinguishing antibody-based from antibody-free m6A detection methods, applying the DRACH motif constraint, reconciling cross-method disagreements (DART 44% in DRACH vs GLORI), or detecting m6Am at the cap.

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Calls HLA class I and class II alleles at 2/4/6/8-field resolution from WGS/WES/RNA-seq/long-read data using OptiType, HLA-LA, T1K, Polysolver, HLA-HD, arcasHLA, StarPhase, or HIBAG imputation. Use when typing for HSCT, solid-organ transplant, neoantigen prediction, PGx screening (B*57:01, B*15:02, etc.), or disease-association studies, with reconciliation across tools and IPD-IMGT/HLA version mismatch handling.

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Analyzes alternative splicing at single-cell resolution. The first decision is library chemistry — 10X 3' is fundamentally limited (RT primes from poly-A, R2 falls in 3' UTR, <0.1 junction read per cell per AS event). Plate-based full-length methods (Smart-seq3, FLASH-seq, VASA-seq, STORM-seq) and single-cell long-read (MAS-Iso-seq, scISOr-Seq2) are the chemistries that give per-cell isoform structure. Tools include MARVEL (R, Smart-seq integrated), BRIE2 (Bayesian PSI with regulatory features and ELBO_gain test), scQuint (junction-cluster, plate-based; not for 10X), SpliZ (annotation-free Z-score), Psix (graph-smoothness regulated AS), and Sierra (alternative polyadenylation, often confused with AS). Use when analyzing isoform usage in scRNA-seq, identifying cell-type-specific splicing, or determining whether scRNA-seq chemistry supports splicing analysis at all.

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Use this skill when the user asks about Household Goods (HHG) movers — 49 CFR 375 + 376 consumer protection rules, FMCSA HHG-specific authority, mover certification, binding vs non-binding estimates, dispute resolution, weight ticket requirements, valuation coverage, and how HHG operations differ from general freight. Cite 49 CFR 375.

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Combines and synthesizes outputs from parallel DAG branches. Handles merge strategies, conflict resolution, and result formatting. Activate on 'aggregate results', 'combine outputs', 'merge branches', 'synthesize results', 'fan-in'. NOT for execution (use dag-parallel-executor) or scheduling (use dag-task-scheduler).

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This skill should be used when the user wants to "synthesize two competing solutions without averaging", "resolve a design/value disagreement by lifting to a higher frame", "止揚 / アウフヘーベン", "矛盾を止揚", "独立案を統合", "平均化せず統合", "contradiction lift", "sublate competing designs", or has two good-but-divergent answers to the same question and wants a higher-order resolution (a selection mechanism) or an honest aporia — not a compromise. NOTE: Use this when two independent solutions diverge and you want to PRESERVE both truth-moments while lifting to a higher frame. Use devils-advocate to stress-test ONE proposal with an assigned opponent, strong-inference to find an unknown cause, and dialectic-loop to validate a claim against a real corpus.

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Runs image, video and audio generation on a local ComfyUI server and guards the GPU before long renders. Trigger to generate an image, video, audio or music locally; to run, test or check a ComfyUI workflow before a long or queued render; when a video render crashed the machine, ran out of memory or hung in VAE decode; to render while LM Studio holds the GPU; to run a pixelsmith brief locally; or say comfyrunner (audit, check, test, run, status). Refuses an untiled video decode and scores resolution times frames. Briefs and judging art are pixelsmith's; sound briefs soundsmith's; freeing LM Studio lmstudiorunner's; schedules agentwright's.

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Generates PSR-11 Container implementation for PHP 8.5. Creates ContainerInterface with service resolution, autowiring support, and exceptions. Includes unit tests.

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Codified expertise for production scheduling, job sequencing, line balancing, changeover optimization, and bottleneck resolution in discrete and batch manufacturing. Informed by production schedulers with 15+ years experience. Includes TOC/drum-buffer-rope, SMED, OEE analysis, disruption response frameworks, and ERP/MES interaction patterns. Use when scheduling production, resolving bottlenecks, optimizing changeovers, responding to disruptions, or balancing manufacturing lines.

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See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio, video), and create real time alerts for events from live streams or desktop capture.

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Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.

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Agent Workforce Orchestration: Hybrid Human+AI Teams. Build agent-led workforce orchestration: capability matching, escrow-based payments for AI agents and human gig workers, unified reputation scoring, SLA enforcement, dispute resolution, and compliance reporting. Includes detailed Python code examples for every pattern.

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Public-records OSINT investigation framework — SEC EDGAR filings, USAspending contracts, Senate lobbying, OFAC sanctions, ICIJ offshore leaks, NYC property records (ACRIS), OpenCorporates registries, CourtListener court records, Wayback Machine archives, Wikipedia + Wikidata, GDELT news monitoring. Entity resolution across sources, cross-link analysis, timing correlation, evidence chains. Python stdlib only.

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[Extended thinking: This workflow implements a sophisticated debugging and resolution pipeline that leverages AI-assisted debugging tools and observability platforms to systematically diagnose and resolve production issues. The intelligent debugging strategy combines automated root cause analysis with human expertise, using modern 2024/2025 practices including AI code assistants (GitHub Copilot, Claude Code), observability platforms (Sentry, DataDog, OpenTelemetry), git bisect automation for ...

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You are a GitHub issue resolution expert specializing in systematic bug investigation, feature implementation, and collaborative development workflows. Your expertise spans issue triage, root cause analysis, test-driven development, and pull request management. You excel at transforming vague bug reports into actionable fixes and feature requests into production-ready code.

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Orchestrate multi-agent incident response with modern SRE practices for rapid resolution and learning:

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Expert SRE incident responder specializing in rapid problem resolution, modern observability, and comprehensive incident management. Masters incident command, blameless post-mortems, error budget management, and system reliability patterns. Handles critical outages, communication strategies, and continuous improvement. Use IMMEDIATELY for production incidents or SRE practices.

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Expert database optimizer specializing in modern performance tuning, query optimization, and scalable architectures. Masters advanced indexing, N+1 resolution, multi-tier caching, partitioning strategies, and cloud database optimization. Handles complex query analysis, migration strategies, and performance monitoring. Use PROACTIVELY for database optimization, performance issues, or scalability challenges.

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Calls native libraries via P/Invoke. LibraryImport, marshalling, cross-platform resolution.

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