Results for “resolution”

Detects A/B chromatin compartments from balanced Hi-C contact matrices via eigenvector decomposition of the distance-normalized, Pearson-correlated cis matrix with cooltools (eigs_cis), then orients (phases) the compartment eigenvector against a GC or gene-density track so the active (A) sign is not arbitrary. Covers the eigenvector-is-a-choice problem (per-arm view_df to remove the centromere gradient; picking the eigenvector by max correlation with activity, not by eigenvalue), GC phasing with bioframe.frac_gc, resolution choice (100kb-1Mb), saddle plots and saddle_strength for compartmentalization strength, the cohesin-loss-strengthens-compartments result, subcompartments (SNIPER/Calder/dcHiC), and cross-condition compartment switching. Use when calling A/B compartments, computing E1/eigenvectors, phasing the eigenvector, building saddle plots, choosing a compartment resolution, quantifying compartment strength, or comparing compartmentalization across conditions.

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Detects focal chromatin loops (point interactions / corner-dots) in balanced Hi-C and Micro-C contact maps and aggregates/validates a loop set. Covers de-novo calling with cooltools dots (HiCCUPS-style 4-background local enrichment with lambda-chunked FDR), chromosight (template-correlation), and Mustache (scale-space blob detection); aggregate peak analysis (APA) via cooltools pileup for confirmation; the depth/resolution prerequisite (de-novo needs ~5-10kb resolution = hundreds of millions to billions of valid pairs); consensus across callers and convergent-CTCF support as validation; and differential loops via union anchors plus chromosight quantify. Use when calling chromatin loops or dots from a cooler, deciding whether a map is deep enough to call de-novo vs running APA on known CTCF/cohesin anchors, building an aggregate peak pileup, comparing loops across conditions, or validating loop calls. For HiChIP/PLAC-seq/PCHi-C protein-anchored data use FitHiChIP/MAPS, not dots.

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Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2025 Nat Genet; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.

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Analyzes alternative splicing from PacBio Iso-Seq (HiFi, Kinnex/MAS-Iso-seq) and Oxford Nanopore (direct cDNA, direct RNA, R10.4.1+) long-read RNA-seq with full-isoform resolution. Tools include FLAIR (correct/collapse/quantify/diffSplice for PacBio + ONT), IsoQuant (de-novo or annotation-guided isoform discovery 2024 SOTA), Bambu (annotation-aware Bayesian discovery + quantification with Novel Discovery Rate), SQANTI3/SQANTI-LR (isoform classification: FSM/ISM/NIC/NNC + artifact flags), rMATS-long (event calling on long-read isoforms), and minimap2 (-ax splice:hq for HiFi; -ax splice -k14 for ONT cDNA; add -uf only for direct RNA or stranded cDNA preps). Solves microexon detection, recursive splicing, complex multi-exon isoforms, and DTU without transcript-quantification uncertainty. Use when short-read AS limitations (anchor length, complex isoforms, microexons, recursive splicing, transcript ambiguity) demand full-isoform resolution.

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在Python 2.7环境下解析Windows目录链接（Junction）或符号链接的真实物理路径。提供基于PyWin32 API和PyQt4/5 QProcess调用cmd命令的两种解决方案，解决os.path.realpath失效及输出解析问题。

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Skill manifest management for dotnet-harness. Tracks skill dependencies, conflicts, version compatibility, and provides validation and resolution tools. Triggers on: skill manifest, dependency resolution, skill compatibility, version conflicts, build manifest, validate dependencies.

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Skill manifest management for dotnet-agent-harness. Tracks skill dependencies, conflicts, version compatibility, and provides validation and resolution tools. Triggers on: skill manifest, dependency resolution, skill compatibility, version conflicts, build manifest, validate dependencies.

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Deep-dive offensive methodology for dependency confusion and namespace attacks across all major package ecosystems. Covers npm scope confusion exploiting the gap between public and private scoped packages and .npmrc misconfigurations where registry mappings fail to pin internal scopes exclusively. Addresses PyPI namespace attacks through --extra-index-url resolution ordering, NuGet feed priority exploitation when multiple package sources are configured without clear directives, Maven and Gradle repository ordering where artifact resolution traverses repositories sequentially, Go module proxy abuse through GOPROXY misconfiguration, Ruby gems namespace squatting, and Docker image tag confusion with unqualified image references. Provides complete proof-of-concept methodology using safe callbacks including DNS canary via interactsh or Burp Collaborator and HTTP beacon with no destructive payload. Covers reconnaissance techniques for discovering internal package names through GitHub repository analysis, error ...

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Use the VideoDB Python SDK to search video by speech or visual content and create playable clips with timestamps. Add subtitles and audio, change aspect ratios, and configure alerts for live video or desktop sessions.

• Find spoken topics or visual scenes and create clips
• Add subtitles, background music, or dubbing
• Change video resolution and aspect ratio

Plan manufacturing jobs around available machines, operators, materials, and delivery dates. Use bottleneck analysis, changeover planning, and workload balancing to compare production sequences and revise schedules when breakdowns, shortages, or absences disrupt the plan.

• Sequence jobs competing for limited equipment
• Revise production schedules after equipment breakdowns
• Compare batch sizes and changeover costs

Work through unresolved GitHub pull request reviews one at a time. The skill explains each comment, proposes a fix, asks how to proceed, and handles chosen edits, replies, commits, and thread resolution.

• Addressing unresolved pull request review comments
• Choosing between a code fix, a reply, or skipping a comment
• Handling duplicate review comments together

Manage GitHub pull requests, which are proposals to merge code changes, from creation through review, testing, conflict resolution, and merging. Coordinate multiple agents to divide review work and track progress across the process.

• Creating a pull request and coordinating its review
• Dividing reviews of large changes among multiple agents
• Checking tests, lint, and builds before merging

Guide GAIA benchmark runs from environment checks and cost estimates to result comparison. Create a signed submission package compatible with HAL, including agent traces, metadata, and an audit report that checks for benchmark exploitation.

• Check readiness for a GAIA benchmark run
• Estimate costs and run an evaluation
• Package existing results for submission

Manage Git branches, resolve conflicts, and coordinate pull requests, which propose code changes. Check change risks and automated test status before merging, and inspect metrics such as merge frequency and review time.

• Create a feature branch and prepare a pull request
• Collect fixes into a release branch
• Check change risks before merging

Turn research findings stored in memory into structured reports with evidence ratings, source references, and practical recommendations. Compare conflicting claims, identify unresolved questions, and explain limitations so accumulated research can support decisions and next steps.

• Combine findings from several research sessions
• Compare conflicting claims and their supporting evidence
• Prepare a research brief with recommended next steps

Download individual YouTube videos with a chosen quality, file format, and destination folder. Save video as MP4, WebM, or MKV, or download only the audio as MP3 using the included Python script.

• Saving a video at a chosen resolution
• Downloading only the audio as MP3
• Choosing a video format and destination folder

Review local character animations before sharing previews or final renders. Check structured design data, asset files, browser errors, sampled motion frames, and MP4 metadata, then classify the result as pass, revise, or fail.

• Checking character animation previews before sharing
• Finding missing assets and browser errors
• Inspecting sampled frames for motion and visual issues

Turn written descriptions or Mermaid code into diagrams showing processes, interactions, and data relationships. Choose a color theme and save both SVG files, which stay sharp when enlarged, and high-resolution PNG images.

• Illustrating workflows and decision trees
• Diagramming API interactions
• Visualizing database relationships

Create a morning brief as a styled HTML page using connected calendars, email, and chat. It shows the day's meeting load, items needing attention, recent resolutions, and preparation for tomorrow, with an option for recurring weekday runs.

• Getting a visual overview of today's schedule
• Checking unanswered requests that need attention today
• Finding concrete preparation for tomorrow's meetings

Guide production incident handling by establishing impact, start time, and recent changes before proposing action. Document evidence for rollback requests, keep the issue updated, and distinguish service recovery from resolution that explains the incident.

• Establish the impact and start time of a production incident
• Check whether deployments align with the incident timeline
• Prepare a rollback request with supporting evidence

Create and review scientific figures with Matplotlib, Seaborn, or Plotly. The skill covers panel layouts, uncertainty, missing data, accessible color choices, and exported file checks, helping prepare figures for papers, presentations, or web publication.

• Create multi-panel figures for a paper
• Show uncertainty and missing observations clearly
• Review palette contrast and color-independent cues

Match scientific labels to ontology identifiers, standardized IDs for concepts such as tissues, cell types, and diseases. Checks existing IDs, labels, obsolete terms, and prefixes to support metadata annotation, submission preparation, and table audits.

• Annotating tissue, cell type, or disease fields with standard IDs
• Auditing metadata tables for incorrect IDs and labels
• Checking obsolete terms and available replacements

Turn research content into conference posters with editable LaTeX sources and a PDF. Arrange columns, figures, and text using poster templates, then check page dimensions, embedded fonts, and image resolution before printing.

• Turning a research paper into a conference poster
• Arranging figures and text in a multi-column poster
• Building a poster from an institutional template

Predict spatial gene expression from H&E-stained tissue images using DeepSpot-M. Query a released panel of about 19,000 genes and combine predictions from small image tiles into slide-wide maps for research analysis.

• Mapping predicted marker-gene expression across a tumor section
• Predicting expression across slides without matching gene-expression assays
• Adding predicted expression to a histology image analysis pipeline