648 件 ・ 関連度順
概要と使いどころ
Counts RNA-seq reads overlapping GTF gene features. Takes sorted STAR BAMs plus GTF; outputs a per-gene tab-delimited matrix across samples. Handles strandedness (0/1/2), paired-end, multi-sample batch counting in one command, and outputs assignment statistics. Use Salmon for alignment-free quantification; use featureCounts when STAR BAMs already exist.
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Per-feature NaN-safe Spearman/Pearson correlation across many features (genes, proteins, variants) with missing values. Covers why bulk matrix shortcuts fail, correct pairwise deletion, degenerate input filtering, and large-dataset performance. Use statistical-analysis for test choice; shap-model-explainability for interpretability.
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Statistical visualization on matplotlib with native pandas support. Auto aggregation, CIs, grouping for distributions (histplot, kdeplot), categorical (boxplot, violinplot), relational (scatterplot, lineplot), regression (regplot, lmplot), matrix (heatmap, clustermap), grids (pairplot, FacetGrid). Use for quick statistical summaries; matplotlib for fine control; plotly for interactive HTML.
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GRN inference from expression via GRNBoost2 (gradient boosting) or GENIE3 (Random Forest). Load matrix, filter by TFs, infer TF-target-importance links, save network. Dask-parallelized to single-cell scale. Core SCENIC component.
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Predict RNA secondary structure, MFE folding, base-pair probabilities, RNA-RNA interactions via ViennaRNA Python bindings. Pipeline: sequence → MFE → partition function and pair-probability matrix → dot-bracket → duplex. Use for siRNA/sgRNA targeting, ribozyme design, RNA accessibility. Use RNAfold CLI for batch use without Python.
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Evaluate technical options with scoring matrix, trigger Gate 4 for significant decisions
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Map slice dependencies using DSM matrix and prioritize by risk
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Evaluates which SaaS tools can be replaced with AI agents. Takes a list of current SaaS subscriptions with costs, assesses replacement feasibility, estimates build vs buy economics, identifies Claude+MCP alternatives, and generates a comprehensive replacement plan with priority matrix, ROI analysis, implementation timeline, and risk assessment.
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Build and run a labelled eval set for a System One model (Jev, Von, or any typed-decision config), then sweep criteria wordings and thresholds against it. Use when a Jev/Von classification is wrong or unreliable, when choosing between the hosted API and a local open model, when tuning noul thresholds, or before shipping any typed-decision feature. Produces an accuracy-by-wording matrix and a calibrated threshold.
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Creates structured hiring scorecards for any role. Takes job title, requirements, and team context. Generates comprehensive scorecard with weighted scoring rubric, interview questions per competency, evaluation matrix, red/green flags, and reference check questions.
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Drop a folder of vendor quotes, proposals, and SOWs on Cowork -- normalizes them into one comparison matrix, computes true total cost of ownership, surfaces the terms each vendor buried, and arms you with negotiation leverage points and reference-check questions.
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Turn an RFP and a folder of your past proposals, case studies, and capability docs into a requirement-by-requirement compliance matrix and a drafted response. Flags disqualifiers and unanswerable requirements before you burn a week writing.
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Online (incremental) covariance, correlation, and precision estimation in Python — the streaming complement to sklearn.covariance. Use when code needs a covariance/correlation matrix updated per observation, recomputes np.cov/np.corrcoef in a rolling loop, must judge or compare covariance estimates, or proposes a new covariance methodology. Points to task-specific skills.
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Use when one validated cohort analysis must be repeated across many exposure/outcome pairs. Generates one R/Python script per combination from a single methodology template, changing only the variables, and aggregates the results into a summary matrix.
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Enterprise SSL VPN attack - vendor fingerprinting, CVE matrix (Cisco, Fortinet, Citrix, Palo Alto, Pulse/Ivanti), default credentials, pre-auth exploit commands. Wiki-first, FIND schema output.
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Auth bypass and ATO hunting - legacy protocol matrix (XMLRPC, SharePoint /_vti_bin/, EWS, Citrix, etc.), JWT manipulation, password reset poisoning, SAML auth bypass, session fixation. Wiki-first, FIND schema output.
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Prompt engineering regression test matrix register: baseline outputs, semantic drift thresholds, judge evaluations, and golden dataset.
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Comprehensive PET selection guide covering differential privacy, homomorphic encryption, secure multi-party computation, federated learning, zero-knowledge proofs, and trusted execution environments. Includes use-case matching matrix, performance comparison, and GDPR alignment assessment for each technology.
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Multi-state harmonized privacy compliance program. Common requirements matrix across all US state privacy laws, state-specific deltas, unified privacy program architecture, and implementation strategy for operating across California, Virginia, Colorado, Connecticut, Texas, Oregon, Montana, and Kentucky.
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Architecture patterns for GDPR Article 5(1)(c) data minimization and Article 25(1) data protection by design. Covers field-level encryption, data masking, aggregation, pseudonymization per Article 4(5), and anonymization per Recital 26. Includes ENISA pseudonymization techniques and a data minimization assessment matrix.
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Harmonises data classification across jurisdictions mapping GDPR special categories vs CCPA sensitive PI (1798.140(ae)) vs HIPAA PHI (160.103) vs LGPD sensitive data (Art. 5-II). Provides cross-regulation mapping matrix for multinational compliance. Keywords: cross-jurisdiction, GDPR, CCPA, HIPAA, LGPD, sensitive data, multinational, classification mapping.
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Provides combined DPIA and AI Act conformity assessment template with integrated risk scoring matrix. Covers GDPR Art. 35 DPIA elements, AI Act high-risk system requirements, mitigation measures, and human oversight assessment. Keywords: DPIA template, conformity assessment, risk scoring, AI Act, combined assessment, high-risk AI.
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Adds, changes, or reviews a screen, sheet, dialog, or destination slice (endpoint to repository to ViewModel to UI to navigation to DI to tests) in a Compose or CMP project. Use when adding a screen, building a new feature, adding a ViewModel or destination, wiring list/detail or a form screen, scaffolding a slice, or reviewing a feature change. Covers Contract.kt, launchGuarded wiring, SavedStateHandle drafts, the state matrix, and new-feature.sh. Do NOT use for routing or architecture (compose-architecture), pure refactors, Gradle work (compose-project), recomposition or styling (compose-ui), repository or persistence work (compose-data), or expect/actual splits (compose-platform).
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Apply the bioengineering, translational systems biology, and tissue modeling frameworks of Linda Griffith, Professor of Biological Engineering at MIT and tissue engineering pioneer. Use this skill whenever facing decisions, designs, or evaluations in bioengineering, tissue modeling, microphysiological systems (organs-on-chips), synthetic matrix design, disease subtyping, or drug target validation. Reach for this skill when evaluating preclinical models (human vs. animal), reframing overlooked or neglected pathologies into rigorous engineering challenges, selecting biomaterials, designing cell signal processing assays, or diagnosing operational flaws in experimental platforms (e.g., PDMS compound absorption or Matrigel lot variability).
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