Results for “time”

Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.

Solve vehicle routing problems (TSP, VRP, PDP) using NVIDIA cuOpt's Python API with cost matrices, time windows, capacity constraints, and pickup-delivery pairs.

Identifies which items (pages, campaigns, products, channels, regions) had the biggest increases or decreases for a key metric between two time periods.

Scrapes posts from an Instagram user's profile feed including captions, media URLs, like/comment counts, timestamps, and location tags using cursor-based pagination.

Analyze investment portfolios by fetching real-time holdings via Alpaca MCP Server, then assess asset allocation, diversification, risk metrics, and generate rebalancing recommendations.

Turn a customer's stated goals into a success plan that predicts the renewal. It sets time-to-value milestones, names an owner on both sides for each, and picks the leading indicators that tell you a year early whether this account renews. Built for B2B customer success teams, customizable to your CRM and product analytics. Trigger on "build a success plan", "onboarding plan for this account", "what are the milestones", "how do I know they'll renew", "time-to-value plan", or any post-sale planning.

Monitors EVM blockchain addresses and contracts for large transfers, new approvals, contract upgrades, unusual gas spends, MEV interactions, and first-time interactions with new contracts.

Reviews a video rough cut by generating deterministic metrics, critiquing the edit against brief and blueprint, and producing review_report.yaml and review_patch.json artifacts.

Interact with the X (Twitter) API to post tweets and threads, read timelines, search content, and track analytics using OAuth authentication and rate-limit handling.

Guides scaling decisions for Qdrant vector databases when data volume exceeds single-node capacity, covering tenant scaling, time window rotation, vertical scaling, and horizontal sharding.

Builds a 3D volatility surface for any optionable ticker, mapping implied volatility across strike price and time to expiration to identify cheap, expensive, or anomalous options.

Best practices for MariaDB system-versioned (temporal) tables, covering creation, querying historical data, managing history growth, and handling ALTER TABLE operations.

Run machine learning and generative AI tasks directly in BigQuery SQL using built-in functions for forecasting, anomaly detection, key driver analysis, and text generation.

Write correct, efficient Kusto Query Language queries with coverage of syntax, joins, dynamic types, datetime pitfalls, regex, serialization, memory management, and advanced functions.

Deploys and configures osquery for real-time endpoint monitoring using SQL-based queries to inspect running processes, open ports, installed software, and system configuration.

Documents MariaDB-specific ALTER TABLE syntax and behavior, including online DDL algorithms, lock modes, lock timeouts, idempotent subclauses, system versioning, and partition operations, for writing and reviewing schema migrations.

Estimate cell state transitions from unspliced/spliced mRNA dynamics using scVelo, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data.

Parse Windows Prefetch files using the windowsprefetch Python library to reconstruct application execution history, detect renamed or masquerading binaries, and identify suspicious program execution patterns.

Designs a points account system supporting multiple point types, independent expiration times, and FEFO (first-expired-first-out) consumption logic, including database schema, data flow, balance display, and partial consumption handling.

Decodes and replays MariaDB binary logs with correct flags: commented pseudo-SQL vs. executable BINLOG blobs, ANNOTATE_ROWS events, flashback undo SQL, and safe replay via the mariadb client.

Points developers to the canonical WordPress Presence API sources and explains its ephemeral-table-plus-TTL architectural pattern, so they avoid inventing postmeta-based presence implementations.

Analyze Linux system logs including auth.log, syslog, systemd journal, and auditd to reconstruct user activity, detect unauthorized access, and establish event timelines on compromised systems.

Recover metadata and content of deleted files from NTFS volumes by analyzing the Master File Table, $LogFile, $UsnJrnl, and MFT slack space using forensic tools like MFTECmd and analyzeMFT.

Parse and analyze Windows forensic artifacts including MFT, registry hives, prefetch files, event logs, LNK files, and jump lists using Eric Zimmerman's EZ Tools suite and KAPE.

Turn "is this new customer going to stick?" into a first-90-day diagnostic. Day-30 / 60 / 90 activation tracking, a sales-to-success handoff quality score, champion engagement during the onboarding window, time-to-value milestone hit rate, and an early false-negative catch on customers already adopting. Built for CS leaders and CSMs, customizable to your CRM and your product-analytics tool. Trigger on "how's {account} onboarding going?", "first 90 day check on {customer}", "is {account} activating?", "time-to-value on {customer}", "handoff quality", "who's at risk in first 90 days?", or any first-90-day adoption question.

Analyze Windows LNK shortcut files and Jump List artifacts to establish evidence of file access, program execution, and user activity using LECmd, JLECmd, and manual binary parsing.

Scrapes posts from a Facebook group given a group URL, sort order, and desired count, returning structured post metadata including post_id, permalink, author, timestamp, body text, media, reactions, comments, and shares.

Scrapes tweets from any X (Twitter) URL — search results, user profile, single tweet detail, or list timeline — and returns normalized per-tweet data with text, author, engagement counts, media, hashtags, mentions, and cursor for pagination.

Scrapes posts from any public Facebook Page timeline, returning structured data including post text, engagement metrics, reaction breakdowns, hashtags, external links, and media type. Supports date range filtering and cursor-based pagination.

Perform survival analysis and time-to-event modeling in Python using scikit-survival, including Cox models, random survival forests, gradient boosting, survival SVMs, and evaluation metrics like concordance index and Brier score.

Expert database architect specializing in data layer design from scratch, technology selection, schema modeling, and scalable database architectures. Masters SQL/NoSQL/TimeSeries database selection, normalization strategies, migration planning, and performance-first design. Handles both greenfield architectures and re-architecture of existing systems. Use PROACTIVELY for database architecture, technology selection, or data modeling decisions.

Builds and deploys Firebase SQL Connect (aka Firebase Data Connect) backends with PostgreSQL securely. Use when designing schemas with tables and relations, writing authorized queries and mutations, configuring real-time data updates, or generating type-safe SDKs. Use when you need a relational database with Firebase, or when the user mentions SQL Connect or Data Connect.

Import recorded driving sensor data (GPS, camera, lidar, actor tracks, lanes) into scenariobuilder.* objects (GPSData, CameraData, LidarData, ActorTrackData, Trajectory, laneData) and run preprocessing — synchronize, offset correction, crop, normalizeTimestamps, convertTimestamps. Also: compute actor tracks from lidar when no annotations exist, attach camera/lidar mounting + intrinsics, export to MAT/workspace/timetable/script. Use for raw driving dataset files (KITTI, nuScenes, Waymo, Pandaset, ROS/ROS2 bags, .mat, .csv, .mp4) or driving/vehicle/sensor logs that need wrapping. drivingLogAnalyzer (DLA) is OPT-IN ONLY — invoke only on explicit user request ('DLA', 'open in DLA', 'inspect/explore/analyze the recording') or reported sensor problem (sync drift, timestamp mismatch, overlay misalignment). NEVER auto-launch DLA after wrapping (Rule 0). For 'build scenario / export to RoadRunner / drivingScenario / OpenSCENARIO / Unreal / simulate', hand off to matlab-use-scenario-builder.

SQL injection occurs when untrusted user input is interpolated directly into database queries, allowing attackers to alter query logic. Detect via single-quote errors, boolean-based blind responses (AND 1=1 vs AND 1=2), time-delay payloads (SLEEP, WAITFOR), UNION column enumeration, and error messages from MySQL, Oracle, MSSQL, PostgreSQL. Tools: sqlmap, sqlbftools, Burp Suite, wfuzz with SQLi fuzz strings.

Import raw data (CSV, XLSX, TXT, or MATLAB tables) into formats used by Sensor Fusion and Tracking Toolbox. Handles both ground truth trajectories and sensor detection data. For truth: builds trackingScenarioRecording, tuning timetable, truthlog, or converted table. For sensor data: builds task-oriented dataFormat structs (preferred) or objectDetection arrays (legacy). Use when importing flight logs, GPS logs, radar detections, IR measurements, lidar/camera bounding boxes, ADS-B data, AIS ship tracks, or any recorded data for use with trackers, filter tuning, or tracker evaluation.

Diagnose and fix retention problems using behavior design (B=MAP). Use when the user mentions "users drop off", "activation rate", "onboarding friction", "retention metrics", "why users dont complete", "churn analysis", "user activation", or "aha moment". Also trigger when analyzing cohort retention curves, designing activation milestones, reducing time-to-value for new users, or investigating why users stop after their first session. Covers the Ability Chain, prompt design, and tiny behaviors that compound. For habit loops and variable rewards, see hooked-ux. For intrinsic motivation, see drive-motivation.