Results for “pr”

Scaffolds an xUnit integration test project for validating Oracle-to-PostgreSQL database migration behavior in .NET solutions, including transaction-rollback base class and seed data manager.

Monitor a list of tickers for key changes in price, IV rank, unusual activity, earnings dates, and score changes. Supports custom watchlists and a default hot options list.

Analyzes game balance by extracting stats from code and running mathematical simulations, including DPS tier lists, TTK matrices, EHP comparisons, economy stress tests, progression pacing, and Monte Carlo RNG simulations.

Monitors and manages Coveo enterprise search platform: sources, indexes, query pipelines, ML models, usage analytics, and security providers, with health checks and license utilization.

TypeORM for TypeScript/JavaScript. Covers entities, repositories, and relations. Use with SQL databases. USE WHEN: user mentions "typeorm", "@Entity", "Repository", "DataSource", "QueryBuilder", "typeorm migration", asks about "decorators for database", "active record pattern", "entity relationships", "typeorm relations" DO NOT USE FOR: Prisma projects - use `prisma` skill; Drizzle - use `drizzle` skill; SQLAlchemy (Python) - use `sqlalchemy` skill; raw SQL - use `database-query` MCP; NoSQL - use `mongodb` skill; Sequelize - not supported

Generate optimized SQL queries from natural language descriptions across multiple database platforms including BigQuery, PostgreSQL, MySQL, and Snowflake. Reads database schemas from uploaded files or diagrams to produce accurate, production-ready queries.

Analyze proxy logs, DNS query logs, and netflow data to detect unauthorized SaaS and cloud service usage, classify domains, and flag high-risk services.

Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials.

Parse and analyze Cobalt Strike Malleable C2 profiles using dissect.cobaltstrike and pyMalleableC2 to extract C2 indicators, detect evasion techniques, and generate network detection signatures.

Granola archive: search, sync freshness, notes, transcripts, panels, SQL counts, and Graincrawl repo work.

Automates packet capture analysis using tshark and pyshark to extract protocol statistics, detect suspicious flows, identify IOCs, and analyze DNS anomalies from PCAP files.

Analyzes the volatility smile and skew for a single options expiration, providing implied volatility curves, skew metrics, and shape classification to reveal market pricing of tail risk and directional fear.

Scales pandas/NumPy workflows beyond memory with Dask distributed computing — parallel DataFrames, arrays, delayed task graphs, and cluster execution. Use when existing pandas/NumPy code must run on larger-than-RAM data or across clusters, for parallel file processing, distributed ML, or integration with existing pandas code. For out-of-core analytics on a single machine prefer vaex; for in-memory speed prefer polars. Part of the AlterLab Academic Skills suite.

Generates video analysis reports by routing to a VLM backend for per-clip analysis or an analytics backend for incident-range reports, with deployment profile verification and URL rewriting.

Automatically discovers social media profiles for individuals or brands across platforms like Facebook, Twitter, Instagram, LinkedIn, and TikTok, returning profile URLs, follower counts, and bio snippets as a downloadable CSV.

Deploys anomaly detection for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications.

Reporting pipelines for CSV/JSON/Markdown exports with timestamped outputs, summaries, and post-processing.

Personal protective equipment tracker. Use when json ppe tasks, csv ppe tasks, checking ppe status.

Notion archive: search, sync freshness, pages/databases, Markdown exports, SQL counts, and Notcrawl repo work.

Firm-fixed-price cost buildup mirroring 1102 IGCE FFP wrap-rate math (BLS OEWS + GSA CALC+ + GSA Per Diem). USE WHEN the user asks to "build an FFP IGCE", "wrap rate buildup", "FFP cost estimate", "layer fringe overhead G&A fee", "how should we price this FFP task order", or "IGCE parity for our bid". Pulls labor categories from pursuit vault or defaults, stacks fringe/overhead/G&A/fee, exports JSON + XLSX to Studio. DO NOT USE FOR T&M/LH pricing (`igce-builder-lh-tm`), cost-reimbursement (`igce-builder-cr`), OT milestones (`ot-prototype-strategist`), or obligation intel (`competitive-intel`).

Detects unusual options activity and classifies smart money signals to help follow institutional positioning, including volume/OI ratio spikes, block trades, sweep orders, and net premium flow.

Audits codebases for GDPR and CCPA/CPRA compliance by inventorying PII fields, mapping data collection points, reviewing consent mechanisms, verifying data subject rights, tracing third-party data sharing, and checking retention policies.

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.

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

Drizzle ORM for TypeScript. Covers schema definition, queries, and migrations. Use for type-safe SQL with minimal overhead. USE WHEN: user mentions "drizzle", "drizzle-orm", "drizzle-kit", "pgTable", "mysqlTable", asks about "lightweight orm", "sql-like orm", "drizzle schema", "drizzle migrations", "drizzle studio", "type-safe sql builder" DO NOT USE FOR: Prisma projects - use `prisma` skill; TypeORM - use `typeorm` skill; raw SQL - use `database-query` MCP; SQLAlchemy - use `sqlalchemy` skill; NoSQL databases - use `mongodb` skill

Discovers competitors via search API, deeply researches each with a multi-lane pattern, and compiles an HTML report with overview, per-competitor deep dives, feature/pricing matrix, and mentions feed.

Forecast any univariate time series (sales, sensors, energy, vitals, weather) zero-shot using Google's TimesFM foundation model, with point forecasts and prediction intervals from CSV, DataFrame, or array inputs.

Calculates IV Rank and IV Percentile for any ticker to determine whether implied volatility is high or low relative to its 252-day history, and provides trading signals based on IV zones.

Post-launch metrics & funnel analysis — cohort retention, channel performance, and progress against targets from $metrics

Generate PPTX presentations, DOCX documents, XLSX spreadsheets, and PDF reports from structured JSON input using PaperJSX.

Use when converting CSV-style tabular data into XML for downstream EDirect or XML-based processing.

Analyze whole-slide pathology images with Python: load 160+ slide formats, preprocess H&E stains, segment nuclei, construct spatial graphs, train ML models, and process multiplex immunofluorescence data (CODEX, Vectra).

Extract false-positive and false-negative gaps from VLM binary-classification-question predictions by comparing model responses against ground truth, producing a structured JSONL file and summary report for downstream root-cause analysis.

Retrieves upcoming earnings announcements for US stocks using the Financial Modeling Prep API, filtered by market cap over $2B, and organizes them into a markdown table grouped by date and timing.

Analyzes Stockbee-style Day 1 Episodic Pivot candidates by scoring catalyst quality alongside price/volume confirmation, gap/range expansion, volume shock, neglect/revaluation context, liquidity, and risk to the EP-day low.