Results for “interpretation”

Reviews a proposal, business case, deck or plan in character as a Chief Information Security Officer archetype and returns a DRAFT review with a verdict, findings cited to specific passages, security and compliance risks and the five interrogation questions a real CISO would ask. Use when the user asks to "run a CISO review", "pressure-test the security of this plan", "what would our CISO say about this" or "check the privacy and third-party risk". Do not use for contract, liability or regulatory-interpretation reviews, use general-counsel-reviewer instead. Drafts for human review; never approves, authorises or signs off.

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Mojibake-Reparatur für doppelt/dreifach kodiertes UTF-8. Behebt Windows-cp1252/Latin-1-Fehlinterpretationen. Ohne Abhängigkeiten.

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Diagnose MSBuild build performance bottlenecks using binary log analysis. Only activate in MSBuild/.NET build context. USE FOR: identifying why builds are slow by analyzing binlog performance summaries, detecting ResolveAssemblyReference (RAR) taking >5s, Roslyn analyzers consuming >30% of Csc time, single targets dominating >50% of build time, node utilization below 80%, excessive Copy tasks, NuGet restore running every build. Covers timeline analysis, Target/Task Performance Summary interpretation, and 7 common bottleneck categories. Use after build-perf-baseline has established measurements. DO NOT USE FOR: establishing initial baselines (use build-perf-baseline first), fixing incremental build issues (use incremental-build), parallelism tuning (use build-parallelism), non-MSBuild build systems. INVOKES: binlog MCP server tools (overview, errors, search, items, properties); falls back to dotnet msbuild binlog replay + grep/cat when the MCP is unavailable.

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Use for explicit undergraduate data-science teaching, assignment guidance, concept clarification, method selection, or output interpretation. Do not activate merely because a task contains data or Python code.

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Ask clarifying questions before implementing to ensure Python requirements are understood. Use when a task lacks detail, dependencies are unclear, or multiple interpretations are possible.

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Kelly Restaurant Intel: App-in-Skill daily industry intelligence cockpit for restaurants, cafes, and F&B groups (Busabase App-in-Skill). Use when the user asks about restaurants, cafes, food and beverage, menus, local events, daily offers,餐饮, or餐厅 scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.

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Kelly Retail Intel: App-in-Skill daily industry intelligence cockpit for retail stores and consumer brands (Busabase App-in-Skill). Use when the user asks about retail, stores, promotions, merchandising, shopping trends, weather/event sales opportunities,零售, or门店 scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.

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Kelly Real Estate Intel: App-in-Skill daily industry intelligence cockpit for real estate and property agencies (Busabase App-in-Skill). Use when the user asks about real estate, property agencies, listings, transactions, competitor ads, client follow-up, or a地产/中介 intelligence workflow. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.

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Kelly Ecommerce Intel: App-in-Skill daily industry intelligence cockpit for e-commerce and cross-border sellers (Busabase App-in-Skill). Use when the user asks about e-commerce, cross-border selling, product trends, competitor prices, listing optimization, ads,电商, or跨境 scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.

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Kelly AI Newsroom: App-in-Skill daily industry intelligence cockpit for AI/news-source intelligence (Busabase App-in-Skill). Use when the user asks for AI trend sourcing, news-source monitoring, buyer-intent analysis, or a reusable industry-intelligence cockpit. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.

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Kelly Beauty Intel: Busabase-backed App-in-Skill daily industry intelligence cockpit for beauty, wellness, and medical aesthetics. Use when the user asks about beauty, medical aesthetics, wellness, treatments, competitor offers, IG/Xiaohongshu content,美容,医美, or health-service sales scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.

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Kelly Financial Services Intel: App-in-Skill daily industry intelligence cockpit for financial services, investment advisory, and family offices (Busabase App-in-Skill). Use when the user asks about financial services, investment advisory, market explainers, client memos, family office,投顾,金融服务, or family-office scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.

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Kelly Insurance Intel: App-in-Skill daily industry intelligence cockpit for insurance and wealth advisory (Busabase App-in-Skill). Use when the user asks about insurance, wealth advisory, client risk reminders, market news, policyholder follow-up,保险, or financial-advisor sales scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.

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Kelly Education Intel: App-in-Skill daily industry intelligence cockpit for education, training, tutoring, and admissions services (Busabase App-in-Skill). Use when the user asks about education, training centers, exams, study abroad, visas, school policy, parent FAQ,招生,升学, or教育机构 sales scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.

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Synthesize scientific evidence into a claim-to-source matrix, distinguish primary evidence from interpretation, assess disagreement and study limitations, and produce a calibrated research summary.

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Chains a somatic (tumor-normal) SNV/indel and structural-variant pipeline end to end with GATK Mutect2 (or Strelka2), wiring the somatic-specific machinery - panel-of-normals and gnomAD germline-resource priors, GetPileupSummaries/CalculateContamination, and LearnReadOrientationModel FFPE/oxoG orientation-bias filtering fed into FilterMutectCalls. Use when calling somatic mutations from a tumor-normal pair (or tumor-only with PoN caveats), deciding which artifact filter removes which class of false positive, reasoning about VAF/purity/ploidy and clonal-vs-subclonal detection, adding somatic SV/CNV or TMB/MSI/signatures, or routing variants to AMP/ASCO/CAP tier and oncogenicity interpretation (never germline ACMG).

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End-to-end biomarker discovery workflow from expression data to validated biomarker panels. Covers feature selection with Boruta/LASSO, leakage-safe cross-validation, calibration, and SHAP interpretation. Use when building and validating diagnostic or prognostic biomarker signatures from omics data.

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End-to-end multi-omics integration workflow. Orchestrates data harmonization, MOFA/mixOmics integration, factor interpretation, and downstream analysis across transcriptomics, proteomics, metabolomics, and other modalities. Use when integrating multiple omics datasets.

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Analyze metabolomics data end-to-end — metabolite identification, quantification (TIC normalization, batch correction), differential analysis, and pathway interpretation. Use for processing mass-spec metabolomics output, normalization choice, untargeted metabolomics workflows, and integrating with other omics layers.

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Pharmacogenomics (PGx) research — drug-gene interactions (CPIC, PharmGKB), CPIC dosing guidelines, variant-drug-response associations, ethnic-allele-frequency considerations, and metabolizer-status scoring. Use for PGx-informed dosing recommendations, CYP/HLA pharmacogenomic allele interpretation, and clinically-actionable PGx report generation.

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Organic chemistry reasoning guide for reaction product prediction, mechanism analysis (electrophilic/nucleophilic substitution, addition, elimination, pericyclic, radical), and spectroscopy interpretation (1H/13C NMR, IR, MS). Reasons from first principles (electron flow, kinetic vs thermodynamic) rather than pattern-matching named reactions. Use for organic synthesis problems and mechanism explanations.

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Decision-grade statistical analysis for metabolomics intensity tables. Covers transformation and scaling (Pareto vs unit-variance as a hidden hypothesis), unsupervised structure (PCA/HCA for QC), permutation-validated PLS-DA/OPLS-DA (R2 vs Q2, double CV, VIP as heuristic), univariate testing (Welch/Mann-Whitney/ANOVA/LMM with covariate adjustment), and dependence-aware multiple testing. Use when testing which metabolites differ, building or validating a discriminant model, choosing a scaling, or correcting many correlated tests. For sample-wise normalization/drift correction see metabolomics/normalization-qc; for ML classifiers and selection-inside-CV leakage see machine-learning/biomarker-discovery and machine-learning/model-validation; for pathway interpretation see metabolomics/pathway-mapping; for design/power/multiplicity regime see experimental-design/multiple-testing.

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Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026).

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Detects m6A modifications from Oxford Nanopore direct-RNA-sequencing (ONT DRS) signal data using m6Anet (Hendra 2022 *Nat Methods* 19:1590; multiple-instance-learning neural network over DRACH 5-mer signal). Covers the required upstream pipeline (Dorado / Guppy basecalling -> minimap2 transcriptome alignment with `-ax map-ont -uf -k14 --secondary=no` -> nanopolish eventalign with `--scale-events --signal-index` (m6Anet-required) plus `--summary` / `--threads` housekeeping -> m6anet dataprep -> m6anet inference), per-site vs per-read probability interpretation including the `mod_ratio` per-site stoichiometry column, the DRACH-only modeling constraint, minimum-coverage thresholds (20-50 reads per site for stable probability estimates), multi-condition comparison via xPore (Pratanwanich 2021 *Nat Biotechnol* 39:1394), Nanocompore (Leger 2021 *Nat Commun* 12:7198), ELIGOS (Jenjaroenpun 2021 *NAR* 49:e7), and Dorado native modification calling (RNA004 chemistry, 2024+), reference-transcriptome version pinning, the cDNA-vs-DRS chemistry distinction (cDNA-Nanopore CANNOT be used for modification detection), and orthogonal validation against MeRIP / GLORI. Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting per-site `probability_modified` vs `mod_ratio` vs per-read modification probabilities, comparing methylation between conditions from ONT data, deciding between m6Anet for known DRACH sites and Dorado/Remora for genome-wide screening, pinning RNA002 vs RNA004 chemistry and basecaller model versions, or troubleshooting eventalign / dataprep failures.

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