668 个 · 按相关度排序
Create custom external web service APIs for Moodle LMS. Use when implementing web services for course management, user tracking, quiz operations, or custom plugin functionality. Covers parameter validation, database operations, error handling, service registration, and Moodle coding standards.
中文简介正在准备中。当前显示原文说明。
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
中文简介正在准备中。当前显示原文说明。
Comprehensive checklist for conducting thorough code reviews covering functionality, security, performance, and maintainability
中文简介正在准备中。当前显示原文说明。
Use this skill before any creative or constructive work (features, components, architecture, behavior changes, or functionality). This skill transforms vague ideas into validated designs through disciplined, incremental reasoning and collaboration.
中文简介正在准备中。当前显示原文说明。
Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuning Use when: adding search to, algolia, instantsearch, search api, search functionality.
中文简介正在准备中。当前显示原文说明。
Biopython is a comprehensive set of freely available Python tools for biological computation. It provides functionality for sequence manipulation, file I/O, database access, structural bioinformatics, phylogenetics, and many other bioinformatics tasks.
中文简介正在准备中。当前显示原文说明。
Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.
中文简介正在准备中。当前显示原文说明。
Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuning Use when: adding search to, algolia, instantsearch, search api, search functionality.
中文简介正在准备中。当前显示原文说明。
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
中文简介正在准备中。当前显示原文说明。
5-level verification pyramid: static→unit→Playwright E2E (homepage-first, 6bp)→AI visual→post-deploy. 8-check quality gate. Multi-agent testing (functional/security/a11y/performance). Playwright v1.59+ AI agents (Planner/Generator/Healer). WCAG 2.2 AA via axe-core v4.11. Percy+Chromatic visual regression. ADA Title II 2027/2028 deadlines.
中文简介正在准备中。当前显示原文说明。
Refactor Python code to improve quality while keeping functionality intact. Use when cleaning up complexity, removing duplication, or tightening structure.
中文简介正在准备中。当前显示原文说明。
Use for the infrastructure design phase between domain exploration and Lean formalization — deriving non-functional requirements from the Lean model, deciding environments (test/local/prod), identity provider, the relational database's product, version and configuration (the storage kind is fixed to relational), ID representation, and the list of things NOT to build. Produces documents/infra-design with INFRA-D/A/Q entries.
中文简介正在准备中。当前显示原文说明。
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
中文简介正在准备中。当前显示原文说明。
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
中文简介正在准备中。当前显示原文说明。
Add PostHog feature flags to gate new functionality. Use after implementing features or reviewing PRs to ensure safe rollouts with feature flag controls. Also handles initial PostHog SDK setup if not yet installed.
中文简介正在准备中。当前显示原文说明。
Simplifies and refines code for clarity, consistency, and maintainability while preserving all functionality. Use when asked to "simplify code", "clean up code", "refactor for clarity", "improve readability", or review recently modified code for elegance. Focuses on project-specific best practices.
中文简介正在准备中。当前显示原文说明。
End-to-end 16S/ITS amplicon workflow from demultiplexed FASTQ to a consensus differential-abundance result, orchestrating cutadapt primer removal, per-run DADA2 ASV inference (learnErrors/mergeSequenceTables/removeBimeraDenovo), region-matched taxonomy assignment, a SEPP/Greengenes2 tree, alpha/beta diversity at a declared sampling depth (phyloseq/vegan, adonis2 paired with betadisper), compositional DA as a consensus of >=2 tools (ALDEx2/ANCOM-BC2) on unrarefied counts, and optional PICRUSt2 functional prediction gated on NSTI. Covers the stage-ordering decisions (primers before truncation, per-run error model, rarefy for diversity not DA, predicted potential not activity) and defers each per-step choice to the six microbiome skills. Use when staging an amplicon study end to end or chaining ASV inference, taxonomy, diversity, and differential abundance. For shotgun reads see workflows/metagenomics-pipeline.
中文简介正在准备中。当前显示原文说明。
Orchestrates the full path from differential expression results to redundancy-collapsed functional enrichment: choose ORA vs GSEA, convert gene IDs per method, run enrichGO/enrichKEGG/enrichPathway/enrichWP or gseGO/gseKEGG (clusterProfiler, ReactomePA, rWikiPathways), and visualize. Use when a DESeq2/edgeR/limma result must become enriched GO terms, KEGG/Reactome/WikiPathways pathways, or a GSEA leading edge; when the input is a full ranking for all genes (GSEA, named decreasing vector) or only a pre-selected list (ORA plus a defensible background universe); or when assembling DE-to-pathway end to end. The DE list and ranking statistic come from differential-expression/de-results; per-method nuance lives in the pathway-analysis skills.
中文简介正在准备中。当前显示原文说明。
Searches molecular libraries for substructure matches using SMARTS patterns with explicit handling of recursive SMARTS, ring membership, aromaticity dialect, vector binding, atom map indices, and reactive/PAINS/REOS/Brenk/Aldridge filter catalogs. Use when filtering compounds by pharmacophore features, functional groups, scaffold matches, or screening for assay-interference / structural alerts.
中文简介正在准备中。当前显示原文说明。
Perform a final quality pass fixing alignment, spacing, consistency, and micro-detail issues before shipping. Use when the work is functionally complete and needs finishing touches—not when the hierarchy, structure, tone, or technical foundation still need major changes.
中文简介正在准备中。当前显示原文说明。
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
中文简介正在准备中。当前显示原文说明。
Assign cell types from marker expression in IMC/MIBI data using clustering (PhenoGraph/FlowSOM/Leiden/Pixie), marker-based probabilistic classifiers (Astir), or image-context CNNs (CellSighter), covering the double-positive segmentation artifact, lineage-vs-state markers, the two spillover types, and why a "cell type" in imaging is conditioned on a segmentation guess. Use when phenotyping segmented IMC cells, choosing clustering vs classification, diagnosing implausible double-positive populations, separating lineage from functional markers, or transferring labels across a cohort.
中文简介正在准备中。当前显示原文说明。
Quantifies biodiversity from species abundance/incidence tables using Hill numbers (iNEXT) with coverage-based rarefaction-extrapolation (Chao & Jost 2012), asymptotic richness via Chao1/ACE/jackknife as a lower bound, Baselga turnover/nestedness partition with the Podani alternative as sensitivity check, mandatory Hellinger transformation before ordination (Legendre & Gallagher 2001), Faith PD and SES_MPD/SES_MNTD with explicit null-model choice, and Maire 2015 functional-diversity dimensionality optimization. Use when comparing diversity across sites with unequal sampling effort, picking the right richness estimator for singleton-heavy amplicon data, partitioning beta diversity into turnover vs nestedness, reporting Hill-number effective species counts rather than raw entropies, computing SES_MPD with explicit null-model justification, or deciding whether to apply standard metrics to compositional amplicon data. Not for clinical 16S microbiome diversity (see microbiome/diversity-analysis).
中文简介正在准备中。当前显示原文说明。
Analyzes base-editing screens for variant function. Covers library design (Sanson 2020 GRACE, Hanna 2021 BRCA1/2 SNV scanning, Cuella-Martin 2021), CBE vs ABE chemistry choice (BE3/BE4 vs ABE7.10/ABE8.20/ABE8e), editing-window math (positions 4-8 from PAM-distal end, wider for ABE8e), bystander-edit quantification and the variant-call ambiguity it creates, sgRNA-efficiency filtering before hit calling, indel byproduct interpretation, the substitution-vs-indel diagnostic, variant annotation against ClinVar / COSMIC, and the Broad be-validation-pipeline. Use when designing a BE variant screen, choosing CBE vs ABE for a specific edit, interpreting bystander-confounded hits, distinguishing functional signal from indel artifact, integrating CRISPResso2 output with screen scoring, or deciding BE vs PE for SNV installation.
中文简介正在准备中。当前显示原文说明。