'aggregation' 검색 결과

Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative

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Use CloudBase document database Web SDK to query, create, update, and delete data. Supports complex queries, pagination, aggregation, and geolocation queries.

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Statistical visualization on matplotlib + pandas. Distributions (histplot, kdeplot, violin, box), relational (scatter, line), categorical, regression, correlation heatmaps. Auto aggregation/CIs. Use plotly for interactive; matplotlib for low-level.

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Out-of-core DataFrame for billion-row data via lazy evaluation and memory-mapped files. Use when data exceeds RAM (10 GB–TB) for fast aggregation, filtering, virtual columns, and visualization without loading. Supports HDF5, Arrow, Parquet, CSV with cloud (S3, GCS, Azure). Built-in ML transformers (scaling, PCA, K-means). In-memory: polars; distributed: dask.

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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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Manages parent/child agent relationships with task delegation and result aggregation. Supports sequential chains, parallel fans, conditional routing, retry logic, timeout handling, and YAML-based visual workflow definition.

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Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.

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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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Fix ClickHouse query errors in Rust when using clickhouse-rs crate. Use when: (1) "string is not valid utf8" errors - typically FixedString columns need CAST to String, (2) "tag for enum is not valid" errors - typically Option<T> fields receiving non-NULL values, (3) Sum/count aggregations returning Float64 but Rust expects u64, (4) LEFT JOIN results with empty strings where Rust expects Option::None, (5) "not enough data, probably a row type mismatches a database schema" - query SELECT returns fewer columns than the Rust Row struct expects (common when multiple query functions share the same struct but one function is missing columns added later), (6) Garbled/corrupted data when INSERTing to FixedString(N) columns using String Rust type - silently corrupts data (no error!) because String adds a length prefix but FixedString expects exactly N raw bytes. Fix: use [u8; N] with #[serde(with = "BigArray")] from serde-big-array, (7) "the trait bound `[u8; 64]: serde::Serialize` is not satisfied" when using [u8; N] arrays in Row structs - need #[serde(with = "BigArray")] annotation from serde-big-array crate.

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QA an analysis before sharing with stakeholders — methodology checks, accuracy verification, and bias detection. Use when reviewing an analysis for errors, checking for survivorship bias, validating aggregation logic, or preparing documentation for reproducibility.

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Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.

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Structured logging with proper levels, context, PII handling, centralized aggregation. Use for application logging, log management integration, distributed tracing, or encountering log bloat, PII exposure, missing context errors.

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Cloudflare Workers observability with logging, Analytics Engine, Tail Workers, metrics, and alerting. Use for monitoring, debugging, tracing, or encountering log parsing, metric aggregation, alert configuration errors.

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Think and work like an expert Medicinal Chemist. Use when a task calls for Medicinal Chemist judgment. Reasons from structure-activity relationships, lipophilicity and unbound-fraction physicochemistry, synthetic accessibility, and target-product-profile multiparameter optimization through LLE/Fsp3/QED scoring, FEP+/Glide docking validated against co-crystal and SPR data, ELN-tracked LC-MS/NMR synthesis, and DMPK panels (microsomal CL, Caco-2 efflux, hERG, CYP) while treating PAINS and aggregation assay artifacts, biochemical-versus-cellular potency gaps, reactive-metabolite soft spots, and freedom-to-operate cliffs as first-class failure modes.

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Think and work like an expert Bioprocess Engineer. Use when a task calls for Bioprocess Engineer judgment. Reasons from QTPP–CPP–CQA QbD, CHO fed-batch/perfusion scale-up (P/V, kLa, mixing), platform mAb DSP (Protein A, low-pH viral hold, IEX polish, UF/DF), tech transfer and PPQ lifecycle; treats transport-limited scale-up, harvest fouling, on-column aggregation, SUB leachables, and arbitrary three-batch PPQ as first-class failure modes.

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Think and work like an expert Chemical Biologist. Use when a task calls for Chemical Biologist judgment. Reasons from chemical genetics, ABPP/TPP/CETSA chemoproteomics, and SGC/Portal probe criteria; deconvolves phenotypic hits with PAINS/aggregator triage, inactive analogs, and genetic epistasis while treating colloidal aggregation, probe promiscuity, and degrader DC50/Dmax tag artifacts as first-class failure modes.

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Think and work like an expert Biophysical Chemist. Use when a task calls for Biophysical Chemist judgment. Reasons from free energy landscapes, binding equilibria (K_d, ΔG = ΔH − TΔS), and probe–system coupling through ITC, SPR/BLI, smFRET, AUC, and global fitting (KinTek, SEDFIT) while treating probe perturbation, mass-transport-limited kon, aggregation-driven avidity, and two-state melting violations as first-class failure modes.

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Think and work like an expert Biochemist. Use when a task calls for Biochemist judgment. Reasons from thermodynamics, enzyme mechanisms, and binding energetics; designs orthogonal purification and assay readouts while controlling oxidation, aggregation, coupled assays, and activity-vs-abundance confounds.

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Writes and executes SQL queries from simple SELECTs to complex multi-table JOINs, aggregations, and subqueries. Use when the user asks to query a database, write SQL, run a SELECT statement, retrieve data, filter records, or generate reports from database tables.

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Annotate CLIP-seq peaks or crosslink sites to RNA features (5'UTR, CDS, 3'UTR, intron, splice junction, snoRNA, tRNA, ncRNA, repeat elements) with ChIPseeker, RCAS, RBP-Maps (Yeo splicing regulatory maps), and bedtools, applying feature-priority hierarchies, transcript-context resolution, and metagene aggregation. Use when characterizing where in transcripts an RBP binds, comparing peak distribution across regions, generating splicing-regulatory maps relative to alternative-splicing events, or distinguishing exonic vs intronic vs UTR binding.

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Review SDS-PAGE or protein purification gel images using DNA sequence, protein sequence, base-pair length, expected protein size, and lane labels. Use when the user wants to judge whether a gel ran well, whether the main band matches the expected product, or whether there may be impurities, degradation, aggregation, or low expression.

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Cell-free protein synthesis (CFPS) planning and optimization guidance. Use when: (1) Planning CFPS experiments, (2) Troubleshooting low yield or aggregation, (3) Optimizing DNA template design for CFPS, (4) Expressing difficult proteins (disulfide-rich, toxic, membrane).

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Solubility-optimized protein sequence design using SolubleMPNN. Use this skill when: (1) Designing for E. coli expression, (2) Optimizing solubility of designed proteins, (3) Reducing aggregation propensity, (4) Need high-yield expression, (5) Avoiding inclusion body formation. For standard design, use proteinmpnn. For ligand-aware design, use ligandmpnn.

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Guidance for cell-free protein synthesis (CFPS) optimization. Use when: (1) Planning CFPS experiments, (2) Troubleshooting low yield or aggregation, (3) Optimizing DNA template design for CFPS, (4) Expressing difficult proteins (disulfide-rich, toxic, membrane).

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