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Build Sankey, alluvial, river, and CONSORT-style flow diagrams to visualize cohort transitions, cell-state changes, or pipeline filtering using ggalluvial, networkD3, plotly, and consort. Use when showing how entities move between categories across timepoints (cell states, drug response classes, patient flow through a trial) or filtering pipelines (variants filtered through QC stages).
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Annotate copy number variant segments with overlapping genes, dosage-sensitivity scores, cancer driver databases, population frequencies, and clinical-variant content. Covers bedtools/pybedtools interval intersection, AnnotSV comprehensive annotation and ranking, ClinGen haploinsufficiency/triplosensitivity scoring, gnomAD-SV/DGV frequency filtering, COSMIC Cancer Gene Census, and ClinVar overlap. Use when interpreting which genes a CNV affects, distinguishing the driver gene of a focal event from passengers, filtering against population CNVs, separating whole-gene from partial-gene overlap, or preparing CNVs for clinical classification.
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Implement safety guardrails for AI systems — content filtering, prompt injection detection, output validation, bias mitigation, and responsible AI practices. Use when tasks involve adding safety layers to LLM applications, detecting prompt injection attacks, filtering harmful content, implementing rate limiting for AI APIs, validating LLM outputs against schemas, building moderation pipelines, or ensuring AI systems comply with safety policies.
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Nexscope Ozon product report search: filters Ozon products by multi-dimensional metrics including sales, revenue, sales/revenue growth rate, cart conversion rate, order conversion rate, price, rating, review count, QA count, variant count, page views, gross margin, return/cancellation rate, ad spend share, weight/volume, listing time, brand, seller, fulfillment method, and labels. Returns each product's SKU, title, price (RUB), sales, revenue, lost revenue, conversion rate, rating, reviews, brand, seller, fulfillment method, listing days/months, and complete product report fields. Use for Ozon product selection, competitor product analysis, best-seller mining, price/conversion band filtering. Trigger when the user mentions Ozon product report, Ozon product selection, Ozon product filtering, Ozon product analysis, Ozon best-selling products, Ozon competitor product analysis, Ozon product report, Ozon product screener, filter Ozon products by sales, Ozon best-seller mining, Nexscope Ozon product report. Also trigger when the intent is to filter Ozon products by multiple metrics and view product-level reports, even without explicitly mentioning Nexscope.
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Fetch and analyze Amazon product reviews by ASIN, supporting 15 marketplaces (including US) with star rating filtering. Triggered when users mention Amazon reviews, US reviews, product reviews, buyer complaints, negative reviews, positive reviews, star ratings, review analysis, review sentiment, product improvement suggestions, Vine reviews, verified purchase reviews, competitor review research, Amazon reviews, US reviews, Amazon.com reviews, product feedback, negative review analysis, positive review analysis, star rating filter, review sentiment analysis, product improvement insights, Vine reviews, competitor reviews, customer feedback. Even if users do not explicitly say "reviews", this skill should be triggered whenever the task involves reading, filtering, or analyzing Amazon product customer reviews.
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Amazon reverse product selection: filter Amazon niches and keywords by 30+ business dimensions (market size & growth, price tiers & share, competition density & top concentration, demographics such as age/gender/income, review highlights & pain points) from a metrics pool aggregated from historical business insight reports. Trigger when users mention reverse product selection, metrics filtering, niche reverse lookup, blue ocean niche discovery, low-competition niche, newcomer-friendly niche, brand-fragmented market, pain-point entry, feature reverse lookup, pricing tier opportunity, demographic-based selection, or similar terms. Even if the user does not explicitly say "reverse product selection," trigger this skill whenever the request involves filtering Amazon niches that match specific business criteria.
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Expert guide for defending AI applications against prompt injection, jailbreak attacks, indirect injection via tool outputs, and data exfiltration through tool calls — input sanitization, output filtering, canary token detection, and layered defense architecture / Panduan ahli pertahanan aplikasi AI terhadap prompt injection, serangan jailbreak, injeksi tidak langsung via output tool, dan eksfiltrasi data melalui tool calls — sanitasi input, filtering output, deteksi canary token, dan arsitektur pertahanan berlapis.
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Add a viewer, configure a viewer, change viewer options, find viewer, close viewer, view a scatter plot, bar chart, histogram, line chart, box plot, pie chart, heat map, correlation plot, 3D scatter, trellis, density plot, statistics, on a Datagrok TableView via the datagrok_exec tool. Use whenever the user asks to plot, chart, visualize, show a graph, draw a distribution, color by a column, swap a viewer's axis, toggle a legend / regression line / log scale, replace one viewer with another, close every chart, reset the view to just the grid, or find an existing viewer by type. Plugin viewers like "Chem space", "sequence space", "activity cliffs" are NOT viewer types — they're registered functions — route those to `grok.functions.call`. Does NOT cover filtering (separate skill `datagrok-filtering`), selection (`datagrok-selection`), grid cell rendering (`datagrok-grid-customization`), layout save/restore, or custom-viewer authoring.
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Find, describe, add, remove, rename, clone, or set metadata on columns of a Datagrok DataFrame via the datagrok_exec tool. Use whenever the user asks to locate "the X column", summarize a column, add a typed/empty/values-filled/virtual column, set semantic type / units / format / friendly name, apply linear or categorical or conditional color coding, drop or rename columns, or copy a DataFrame. Covers everything in DataFrame.columns and Column.meta — but not row filtering/selection (datagrok-filtering, datagrok-selection) and not formula-only columns (datagrok-calc-column).
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Sort, hide, show, reorder, resize, pin, format, and color-code columns in a Datagrok TableView grid via the datagrok_exec tool. Use whenever the user asks to sort by a column (any direction), multi-sort, hide / show / reorder / pin / resize columns, freeze the first N columns, change number-format display, color-code cells (defaults and grid-only tint here; full per-type reference in datagrok-df-and-columns), set row height, or reset the grid back to defaults. Distinct from datagrok-df-and-columns (which owns column-level data metadata like semType, units, friendlyName, and is also where canonical color-coding lives) and from datagrok-viewers (which owns scatter plot / histogram / etc.). Does NOT cover filtering (`datagrok-filtering`), selection (`datagrok-selection`), custom cell renderer authoring (`create-cell-renderer`), saving / restoring layouts, or grid event handlers.
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Manipulate row selection (`df.selection`) on a Datagrok DataFrame via the datagrok_exec tool — set, clear, invert, add to, remove from, intersect, and read the selection mask. Also covers current row (`df.currentRowIdx`), the cross-skill bridges to/from the filter, and how to materialize the selected rows as a new DataFrame. Use whenever the user says "select rows", "deselect", "invert selection", "highlight rows", "selected rows", "clear selection", "current row", "currentRowIdx", "count selected", "list selected indexes", "selection from filter", "filter from selection", or asks for selected rows as a new table. Does NOT cover row filtering (separate skill `datagrok-filtering`) or generic DataFrame cloning (`datagrok-df-and-columns`).
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Filter rows of a Datagrok DataFrame via the datagrok_exec tool through the Filters panel — by range, equals/contains/in-set, multi-value, boolean, free-text row expressions, or substructure (SMILES / SMARTS / molblock). Also covers clearing, inverting, the show-only-filtered vs destructive-drop split, and the filter event lifecycle (onRowsFiltering / onFilterChanged / onRowsFiltered). Use whenever the user says "filter", "show only", "hide rows where", "narrow to subset", "find rows that", "contains", "substructure search", "categorical filter", "range filter", "invert", "clear the filter", "clear filters", "drop rows", or asks for the filtered subset as a new table. Does NOT cover selection (separate skill) or generic DataFrame cloning (datagrok-df-and-columns).
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Medicinal chemistry filters for compound triage. Apply drug-likeness rules (Lipinski rule of five, Veber, Oprea, CNS, lead-like, rule of three), structural alert catalogs (PAINS a/b/c, NIBR screening-deck severity, Brenk, BMS, Glaxo, Dundee, ChEMBL common alerts), ZINC-15 percentile complexity metrics (Bertz, SAscore, QED, Whitlock, Barone), chemical-group detection, Lilly demerits, and the medchem query language (MATCHRULE, HASALERT, HASPROP, HASGROUP) for filtering a library at scale. Also trigger on medchem, `import medchem as mc`, RuleFilters, NIBRFilters, CommonAlertsFilters, NamedCatalogs, QueryFilter, PAINS filtering, or structural alerts.
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TanStack Table best practices for building headless, type-safe data tables in React with sorting, filtering, pagination, row selection, and column management. Use when building data grids, implementing client-side or server-side table features, defining column structures, managing table state, or optimizing table rendering performance.
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Use when filtering Django querysets - FilterSet, custom filters, Django REST Framework integration, explicit fields
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Prepare high-quality datasets for LLM fine-tuning with filtering, deduplication, augmentation, and RLHF data formatting. Activate on: fine-tuning data, training data curation, RLHF dataset, data quality filtering, SFT dataset. NOT for: model training infrastructure (ai-engineer), prompt engineering without fine-tuning (prompt-engineer).
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Call germline SNPs and indels with GATK HaplotypeCaller and the GVCF joint-genotyping workflow. Covers the local-reassembly + PairHMM mechanism (why HC beats pileup callers on indels), the -ERC GVCF reference-confidence model and <NON_REF> allele, BQSR-vs-DRAGSTR and --dragen-mode error modeling, allele-specific (AS_) annotations, and edge cases (ploidy, Mutect2 mitochondria mode, sex chromosomes/PAR, contamination gating). Use when deciding whether to use HaplotypeCaller vs a pileup or DRAGEN caller, whether BQSR still earns its place, whether to call per-sample GVCFs for a cohort, or how to handle non-diploid, mitochondrial, sex-chromosome, or contaminated samples. Not for post-calling filtering depth (see variant-calling/filtering-best-practices) or cohort joint-genotyping scaling (see variant-calling/joint-calling).
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Build Sankey, alluvial, river, and CONSORT-style flow diagrams to visualize cohort transitions, cell-state changes, or pipeline filtering using ggalluvial, networkD3, plotly, and consort. Use when showing how entities move between categories across timepoints (cell states, drug response classes, patient flow through a trial) or filtering pipelines (variants filtered through QC stages).
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Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.
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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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Annotate copy number variant segments with overlapping genes, dosage-sensitivity scores, cancer driver databases, population frequencies, and clinical-variant content. Covers bedtools/pybedtools interval intersection, AnnotSV comprehensive annotation and ranking, ClinGen haploinsufficiency/triplosensitivity scoring, gnomAD-SV/DGV frequency filtering, COSMIC Cancer Gene Census, and ClinVar overlap. Use when interpreting which genes a CNV affects, distinguishing the driver gene of a focal event from passengers, filtering against population CNVs, separating whole-gene from partial-gene overlap, or preparing CNVs for clinical classification.
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Modelo de moderação especializado 7-8B do Meta para filtragem de entrada/saída de LLM. 6 categorias de segurança - violência/ódio, conteúdo sexual, armas, substâncias, automutilação, planejamento criminal. Precisão de 94-95%. Deploy com vLLM, HuggingFace, Sagemaker. Integra com NeMo Guardrails.
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Use when designing or implementing search, filtering, sorting, query bars, and result states in Apple-platform apps — keep search, filter, and sort as separate jobs with clear no-result states.
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Expert en sécurité des agents IA (prompt injection, jailbreak prevention, tool misuse, output filtering)
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