Results for “json-analysis”

Routes runtime-log requests into an evidence packet to isolate the first actionable blocker, repeated signature, blast radius, or safest next read-only check.

Detect sandbox evasion techniques in malware samples by analyzing timing checks, VM artifact queries, user interaction detection, and sleep inflation patterns from Cuckoo/AnyRun behavioral reports.

Query, filter, transform, and aggregate JSON data using jq, with practical patterns for shell pipelines and CLI integration.

Use DuckDB from MATLAB via Database Toolbox (R2026a+) as a non-math operations engine on large tabular files (CSV/Parquet/JSON) and as a zero-config embedded database. Use when connecting to DuckDB, querying CSV, Parquet, and JSON files directly with SQL, reducing or profiling large data before MATLAB analysis, creating portable development databases, or installing DuckDB extensions. Triggers on: DuckDB, duckdb(), large CSV/Parquet/JSON, file too large for readtable, filter/aggregate at source, deduplicate, reduce before analysis, profile large file, persistent file import, analytical engine, SQL on CSV, SQL on Parquet, SQL on JSON, query CSV with SQL, query Parquet with SQL, run SQL on files, SQL queries on files, query files directly, SQL without database, in-process SQL.

Query, filter, and transform JSON with expert jq patterns, including aggregation, string formatting, and shell pipeline integration.

Query, filter, transform, and aggregate JSON data using jq, with practical patterns for shell pipelines and integration with CLI tools.

Shuffle repetitive JSON objects safely by validating schema consistency before randomising entries.

Query, filter, transform, and aggregate JSON data using jq in shell pipelines and scripts.

Search and analyze conversation history stored in session JSONL files using jq and shell commands.

Process JSON data from files or standard input using jq filters for extraction, filtering, and transformation.

Analiza datos en bases de datos relacionales con consultas SQL eficientes y legibles, incluyendo joins, window functions, CTEs y subqueries, para extraer insights directamente de la base de datos.

Guide through a structured data analysis workflow: define the question, validate data quality, select the appropriate analytical method, and produce decision-ready findings with caveats.

Catalog of MariaDB JSON functions with signatures and semantics, covering constructors, extraction, modification, merging, validation, introspection, and formatting. Use when writing SQL that builds, queries, or alters JSON data in MariaDB.

Query, filter, transform, and aggregate JSON data using jq, with practical patterns for shell pipelines and CLI integration.

Analyze datasets to answer defined questions through statistical methods, trend identification, hypothesis testing, and correlation analysis. Use when the user needs evidence-backed findings or decisions from data; use exploratory-data-analysis instead for open-ended first-pass profiling before questions are defined.

Create data-driven charts with Vega-Lite and Vega, covering bar, line, scatter, heatmap, area, radar, and word cloud visualizations from structured data arrays.

Analyzes campaign performance with multi-touch attribution, funnel conversion analysis, and ROI calculation for marketing optimization.

Search, analyze, and engage with X/Twitter from the terminal, including bookmarks, likes, following, trends, and Grok AI analysis, with export to JSON, CSV, or Markdown.

Use this skill when the user uploads Excel (.xlsx/.xls) or CSV files and wants to perform data analysis, generate statistics, create summaries, pivot tables, SQL queries, or any form of structured data exploration. Supports multi-sheet Excel workbooks, aggregation, filtering, joins, and exporting results to CSV/JSON/Markdown.

Provides 25 paid HTTP endpoints on Base mainnet that bill agents per call in USDC for web search, AI analysis, crypto and stock data, SEC filings, company intelligence, news, sentiment scoring, and a macro dashboard, returning structured JSON.

Record and analyze post-trade outcomes for trading signals, tracking false positives, missed opportunities, and regime mismatches to generate feedback for signal aggregation and skill improvement.

Builds a navigable knowledge graph from any folder of files, with community detection, an audit trail, and outputs including interactive HTML, GraphRAG-ready JSON, and a plain-language report.

Analyze a single FASTA file (nucleotide or protein), compute sequence-level metrics (GC, ORFs, MW, pI, GRAVY, secondary-structure fractions) with Biopython, and write a Markdown report plus structured JSON for downstream chaining.

Office-document renderers for pursuit deliverables — Markdown to DOCX (Pandoc/OpenXML) and JSON envelopes to styled XLSX (openpyxl). USE WHEN the user asks to export a Studio markdown file to Word, convert compliance matrix JSON to Excel, render proposal outline as DOCX, or run a one-off format conversion on files under pursuits/. Consumer skills (proposal-generator, subcontractor-sow-builder, compliance-auditor) call these scripts internally; users can also run renderers directly from Agent Skills or chat. DO NOT USE FOR drafting content (use proposal-generator), visual decks/PDF/PPTX (use huashu-design), or domain analysis.