Results for “pascalcase”

Generate production-ready Dataverse SDK solutions with architecture recommendations, data models, and implementation code for common business use cases.

Create Pydantic models following the multi-model pattern with Base, Create, Update, Response, and InDB variants for clean API contracts in Python applications using Pydantic v2.

Computação paralela/distribuída. Escale pandas/NumPy além da memória disponível, DataFrames/Arrays paralelos, processamento multi-arquivo, grafos de tarefas, para datasets maiores que RAM e workflows paralelos.

Creates pastel-toned, lifestyle-oriented presentation decks with rounded cards, soft glow blobs, and Playfair italic numbering.

Use when planning a full ISCA campaign on a calendar — working back from the expected mid-November abstract and paper deadlines to freeze simulation evidence in time, staffing the winter two-round review wait, preparing for the February-March rebuttal and revision window, and branching to camera-ready, artifacts, and the June conference.

Collect and parse forensic artifacts from Windows systems using KAPE for rapid DFIR triage.

Scales pandas and NumPy workflows to datasets larger than memory using parallel and distributed computing, with support for dataframes, arrays, bags, and custom task graphs.

Oracle Database specific features. Covers data types, sequences, synonyms, partitioning, and Oracle-specific SQL syntax. Use for Oracle database work. USE WHEN: user mentions "oracle", "oracle database", "sequences", "synonyms", "DUAL", "SYSDATE", "NVL", "DECODE", "Oracle partitioning", "Oracle specifics" DO NOT USE FOR: PostgreSQL - use `postgresql` instead, SQL Server - use `sqlserver` instead, PL/SQL programming - use `plsql` instead

Write, review, debug, test, and optimize Python Polars code with version-grounded object types, schemas, and execution boundaries.

Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

Use when running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).

Dask parallel computing reference for Python. Covers Dask DataFrame (parallel Pandas), Dask Array (parallel NumPy), Dask Delayed for custom parallelism, Dask Bag, distributed clusters, dashboard monitoring, and scaling best practices.

Provides guidance on new System.Text.Json APIs introduced in .NET 11, including typed JsonTypeInfo access and PascalCase naming policy.

Translates natural language data intents into syntactically valid Perfetto SQL queries and executes them against a local trace file using trace_processor.

Generate production-ready Python code for Dataverse SDK with advanced patterns including error handling, batch operations, OData optimization, and Pandas integration.

Oracle PL/SQL procedural language. Covers stored procedures, functions, packages, triggers, cursors, collections, and exception handling. Use for Oracle database server-side programming. USE WHEN: user mentions "plsql", "Oracle procedures", "Oracle packages", "Oracle triggers", "BULK COLLECT", "FORALL", "DBMS_OUTPUT", "Oracle functions" DO NOT USE FOR: basic Oracle SQL - use `oracle` instead, PostgreSQL - use `plpgsql` instead, T-SQL - use `tsql` instead

Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Manage Planka Kanban projects, boards, lists, cards, and notifications through a command-line interface.

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.

Ingests case studies, past proposals, and boilerplate from library/inbox into structured indexes. Use when the user requests to 'index proposal library', 're-index case studies', 'ingest inbox docs', or 'validate library'.

Process tabular data with Polars' expression API, lazy evaluation, and parallel execution for fast in-memory analysis and pandas migration.

Deploy and operate CAPEv2 sandbox for automated malware analysis with behavioral monitoring, payload extraction, configuration parsing, and anti-evasion capabilities.

Process tabular data with Polars' expression API, lazy evaluation, and parallel execution for faster pandas-style workflows.

Expert PCI DSS compliance advisor covering PCI DSS v4.0.1 (current) and v4.0. Use this skill whenever a user asks about PCI DSS, payment card security, cardholder data protection, CDE scoping, SAQ types (A, A-EP, B, B-IP, C, C-VT, P2PE, D), ROC, AOC, QSA assessments, ASV scans, merchant levels, service provider levels, network segmentation, penetration testing, tokenisation, encryption of PAN data, or any of the 12 PCI DSS requirements. Also trigger for questions like "are we PCI compliant?", "how do I scope my CDE?", "which SAQ applies to us?", "what changed in PCI DSS v4.0?", "how do I prepare for a QSA audit?", or any request involving payment data security, cardholder data environment, or PCI certification readiness.

Process data with high-performance DataFrames using Polars' expression-based API, lazy evaluation, and parallel execution for ETL, analytics, and pandas migration.

Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

Runs an Opportunity Solution Tree workflow with a lightweight graph database and CLI, routing through outcome, opportunity, solution, and assumption phases.

Scale pandas and NumPy workflows to larger-than-memory datasets using parallel and distributed computing.

Fast DataFrame library (Apache Arrow). Select, filter, group_by, joins, lazy evaluation, CSV/Parquet I/O, expression API, for high-performance data analysis workflows.

Cascade PCB components, add lumped elements, export Touchstone, and bridge to eye diagram or antenna array workflows. TRIGGER: user asks to cascade or connect multiple RF PCB components, add lumped R/L/C, export S-parameters to Touchstone, or combine PCB elements into a circuit. Invoke BEFORE writing pcbcascade or circuit code — cascade rules and port matching are non-obvious. SKIP: designing individual components (use the specific matlab-design-pcb-* skill), EM analysis of a single component (use matlab-analyze-em), material/stackup setup only (use matlab-manage-pcb-material), optimization (use matlab-optimize-pcb-design).

Use to reconstruct what actually happened to one customer across every conversation, channel and handoff, for an escalation, complaint, post-mortem or goodwill decision. Trigger for "build a timeline for this customer", "what happened on this case", "summarize the back-and-forth before I decide", "when were they first told", escalation summaries, complaint investigations, or a case spanning several tickets.

Canonical kip knowledge-store patterns for any process or agent: recall prior facts before work, assert decisions/gate outcomes/rejections as structured facts after work, resolve entities with explicit --model sonnet, and invoke the CLI Windows-safely via node packages/kip-sdk/dist/cli/kip.js when kip is not on PATH.

Turn happy customers into references, case studies, and referrals, on purpose instead of by accident. Reads who is happy enough to ask, picks the right ask, sets the value exchange, and builds a pipeline of advocacy over time. Built for B2B customer success teams, customizable to your product and your program. Trigger on "build an advocacy program", "who can be a reference", "find case study candidates", "ask for a referral", "customer advocacy pipeline", or any reference-and-referral planning.

Applies the PCI-DSS framework to identify, assess, and mitigate security risks in systems, processes, and data handling.