Results for “process”

Reference for MATLAB audiostreamer (Audio Toolbox R2025a+). Without this skill, agents consistently default to legacy audioDeviceWriter/audioDeviceReader or base MATLAB sound(), producing less capable code. Use when writing code for audio playback, recording, full-duplex device I/O, real-time audio measurements, or audio I/O processing with callbacks. Also use when debugging audiostreamer errors, dropouts, or latency issues, or migrating from audioDeviceReader, audioDeviceWriter, audioPlayerRecorder, or audioplayer/audiorecorder.

Detect-only pre-release validation sweep over the processkit content tree. Walks entity files, SKILL.md definitions, MCP server tools, and cross-references, then emits a single human-readable report with ERROR / WARN / INFO counts. Use when the user invokes `/pk-release-audit`, before tagging a release, or any time you need a comprehensive structural health check beyond what pk-doctor covers. Detect-only; never modifies any file under `context/`.

Universal Simulator.Company platform assistant. Use when the user asks anything about Simulator.Company, wants to work with the Simulator API, mentions actors, forms, graphs, layers, accounts, transactions, or any other Simulator entity. Also use when the user asks to "use simulator", "call the simulator API", or needs to manage business processes in Simulator. This skill provides deep knowledge of the platform model and guides you to use the simulator MCP tools correctly.

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams. Use when "crewai, multi-agent team, agent roles, crew of agents, role-based agents, collaborative agents, crewai, multi-agent, agents, orchestration, roles, collaborative-ai" mentioned.

Provide direct REST API access to PubMed via the NCBI E-utilities API, supporting advanced Boolean/MeSH queries, batch processing, and citation management. Use when searching biomedical literature by MeSH terms, retrieving abstracts or PMIDs in bulk, or scripting custom PubMed queries over raw HTTP/REST — for Python workflows prefer biopython (Bio.Entrez) instead, use this for direct REST work or custom API implementations. Part of the AlterLab Academic Skills suite.

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.

Inspect, extract, OCR, create, merge, split, reorder, rotate, annotate, fill, redact, compress, secure, and verify PDF documents while preserving source files and visual fidelity. Use when working with one or more .pdf files; converting documents to or from PDF; extracting text, tables, images, metadata, forms, or page ranges; applying true redactions or signatures; diagnosing malformed, encrypted, scanned, or inaccessible PDFs; or validating that a PDF transformation preserved the intended content and layout.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that don't fit in memory.

Lark/Feishu real-time event listening / subscribing / consuming: stream events as NDJSON via `lark-cli event consume <EventKey>` (covers IM messages/reactions/chat changes, Approval status changes, Task updates, VC meeting started/joined/ended, Minutes generated, Whiteboard updated, etc.). Use for Lark bots, real-time message processing, long-running subscribers, streaming webhook/push handlers. Supports `--max-events` / `--timeout` bounded runs and a stderr ready-marker contract — designed for AI agents running as subprocesses.

Node.js runtime best practices. Covers event loop, async patterns, streams, worker threads, memory management, and production optimization. USE WHEN: user mentions "node.js", "event loop", "streams", "worker threads", asks about "process.nextTick", "memory leaks", "cluster mode", "async patterns" DO NOT USE FOR: Express/NestJS frameworks - use framework-specific skills DO NOT USE FOR: Language syntax - use `javascript` or `typescript` skills DO NOT USE FOR: Package management - use npm/pnpm/yarn skills

This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that don't fit in memory.

Use essa skill para processar e analisar grandes conjuntos de dados tabulares (bilhões de linhas) que excedem a RAM disponível. Vaex excels em operações DataFrame out-of-core, avaliação lazy, agregações rápidas, visualização eficiente de big data e machine learning em datasets grandes. Aplique quando usuários precisarem trabalhar com arquivos CSV/HDF5/Arrow/Parquet grandes, realizar estatísticas rápidas em datasets massivos, criar visualizações de big data ou construir pipelines de ML que não cabem em memória.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that don't fit in memory.

Wraps RDKit in a high-level, pandas-friendly datamol interface with sensible defaults for everyday drug discovery — SMILES/SDF loading into DataFrames, molecule standardization, descriptors, fingerprints, Butina clustering, 3D conformer generation, scaffold analysis, and parallel batch processing, returning native rdkit.Chem.Mol objects. Use when running standard cheminformatics pipelines on molecule tables with minimal boilerplate; for low-level control, custom sanitization, or specialized algorithms prefer alterlab-rdkit. Part of the AlterLab Academic Skills suite.

VS-Enhanced Literature Review Strategist - Comprehensive support for multiple review methodologies Full VS 5-Phase process: Prevents Mode Collapse and presents creative search strategies Supports: Systematic Review (PRISMA 2020), Scoping Review (JBI/PRISMA-ScR), Meta-Synthesis, Realist Synthesis, Narrative Review, Rapid Review Use when: conducting any type of literature review, systematic reviews, meta-analyses, scoping reviews, finding prior research Triggers: literature review, PRISMA, systematic review, scoping review, meta-synthesis, realist synthesis, narrative review, rapid review

Turn a call or meeting into a clean recap and a ready-to-send follow-up. The decisions, the action items with owners and dates, the open questions, and a follow-up note you can send as is. Built for B2B revenue teams, customizable to your notes and your process. Trigger on "recap this call", "meeting notes", "write the follow-up", "what did we agree", "action items from this", or any post-meeting summary request.

Use this skill when the user asks about DataQ — FMCSA's data review system at dataqs.fmcsa.dot.gov — for disputing inspection violations, crash records, or other entries that appear in a carrier's CSA / SMS score. Covers Request for Data Review (RDR) process, success rates, common dispute grounds, what evidence to attach, timeline expectations, and how successful disputes reduce BSI (BASIC Severity Indicator) scores. Cite 49 CFR 392.7 and the FMCSA DataQs User Guide.

Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.

Redige pedido de gratuidade da justica (CPC 98-102) e impugnacao a gratuidade da parte contraria. Trata a diferenca PF x PJ: pessoa fisica tem presuncao relativa (CPC 99 §3); pessoa juridica deve PROVAR a insuficiencia (Sumula 481/STJ). Use ao requerer ou impugnar justica gratuita, ao analisar declaracao de hipossuficiencia, quando o operador disser pedir gratuidade, justica gratuita, hipossuficiencia, impugnar a gratuidade do autor, beneficio da assistencia. Acionada pelo civel-master na gestao processual transversal.

Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.

Turn "is this deal real?" into a deal-by-deal risk verdict. An 8-state risk taxonomy, a qualification-vs-stage gap check, multi-thread coverage, days-dark per deal, and a commit-creep watch. Built for B2B sales teams, customizable to your CRM and your sales process. Trigger on "how are my deals", "which deals are stuck", "what's at risk this month", "is the rep sandbagging", "multi-thread check", or any open-deal diagnostic.

Provides a structured workflow for migrating an Android XML View to Jetpack Compose. This skill details the step-by-step process, from planning and dependency setup, to theming and layout migration, validation and XML cleanup. Use this skill when you need to migrate an XML View to Jetpack Compose in an Android project. It solves the problem of converting the UI of a legacy XML View into modern, declarative Compose components while maintaining interoperability.

Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.

Orquestrador do plugin civel (Civil Litigation Master) e porta unica do contencioso civel. Recebe qualquer demanda em linguagem natural, identifica a fase processual e o objetivo, e DIRIME (seleciona e conduz) TODAS as skills pertinentes sem esquecer nenhuma, fechando pela suprema-corte-civel. Use quando o operador descrever uma tarefa civel sem chamar skill especifica, ou disser civel-master, novo caso civel, vou ajuizar, fui citado, recebi sentenca, quero recorrer, preciso da inicial, contestacao, replica, tutela, agravo, apelacao.

Monta e analisa as preliminares de contestacao do art. 337 do CPC (as 13 hipoteses, I a XIII) e as prejudiciais. Cobre inexistencia/nulidade de citacao, incompetencia, incorrecao do valor da causa, inepcia da inicial, perempcao, litispendencia, coisa julgada, conexao, incapacidade/defeito de representacao, convencao de arbitragem, ausencia de legitimidade ou interesse, falta de caucao, indevida concessao de gratuidade. Use quando o operador for contestar, disser preliminares, art 337, inepcia, ilegitimidade, coisa julgada, litispendencia, ou avaliar defesa processual.

Model software around the business domain using bounded contexts, aggregates, and ubiquitous language. Use when the user mentions "domain modeling", "bounded context", "aggregate root", "ubiquitous language", "anti-corruption layer", "context mapping", "domain events", or "strategic design". Also trigger when splitting a monolith into services, defining microservice boundaries, or aligning code structure with business processes. Covers entities vs value objects, domain events, and context mapping strategies. For architecture layers, see clean-architecture. For complexity, see software-design-philosophy.

Run a Codex prompt repeatedly on a fixed interval. Use for "/loop", "run this every N minutes/hours", "poll X every 5 minutes", "repeat this Codex prompt", or any recurring interval-based Codex job. This is the interval-repeat companion to the separate `schedule` skill (cron / specific-time). For a durable, always-on alternative that survives reboots and terminal exits, prefer Codex app Automations; this local CLI is a terminal fallback that only fires while its daemon process is running.

Redige a reconvencao — pretensao propria do reu deduzida na mesma peca da contestacao (CPC 343): conexa com a acao principal ou com o fundamento da defesa, processada simultaneamente. Cobre o cabimento, a admissibilidade independente da contestacao (343 §2 e §6), o valor da causa proprio, as custas e a contestacao do autor-reconvindo (15 dias). Use quando o operador disser reconvencao, reconvir, pedido contraposto, contra-ataque do reu, o reu tambem quer cobrar, ou houver pretensao propria do reu conexa a demanda.

Find the right skill for a capability. Load when a user or skill needs to check if a skill exists for a given task, when process-decomposer assigns skills to steps, or when agent-builder checks skill availability. Triggers on "what skill does this need", "find a skill for", "is there a skill that", "which skill handles", "does a skill exist for", "skill lookup", "check skill library". Prevents skill sprawl by always checking existing skills before creating new ones. The gatekeeper for all skill creation.

Integrate with SAP systems and build extensions. Use when a user asks to connect to SAP S/4HANA, SAP Business One, or SAP ERP via OData, RFC, BAPI, or IDoc interfaces, build SAP BTP (Business Technology Platform) applications, work with SAP CAP (Cloud Application Programming), consume SAP APIs from the API Business Hub, manage master data, automate procurement or sales processes, build Fiori apps, extract SAP data for analytics, or integrate SAP with external systems. Covers S/4HANA APIs, Business One, BTP, CAP, and integration patterns.

The analyst that watches your other analysts. It reads the trail your reports and workflows leave behind, then surfaces scoring blind spots, CRM hygiene gaps, and workflow drift, and turns every miss into a training signal. Built for GTM teams running any stack of reports, customizable to your process. Trigger on "run QA", "weekly QA report", "system health check", "where are our scoring blind spots", "what should we coach on this week", "audit our workflow performance", or any system-level health or feedback-loop question.

Read BEFORE troubleshooting or enhancing camera image quality. Diagnoses and enhances image quality from cameras connected via Image Acquisition Toolbox or USB Webcams support package. Discovers camera capabilities at runtime, analyzes captured images for quality issues (brightness, contrast, sharpness, noise, color balance, backlighting), suggests hardware setting adjustments tailored to the specific camera, and applies Image Processing Toolbox enhancement functions. Use when a user wants to improve camera image quality, troubleshoot dark/blurry/noisy/grainy/ overexposed/washed out/color cast images, or optimize camera settings.

Drafts inspection-ready GMP standard operating procedures for regulated manufacturing. Covers document control, role accountability, process controls, deviation/CAPA handling, and records management aligned to FDA CGMP (21 CFR 210/211), Part 11, ICH Q7/Q9/Q10, WHO GMP, PIC/S, and EU GMP. Use when creating or overhauling a GMP SOP, preparing for audits or inspections, or building compliance-ready procedures. Trigger: GMP, SOP, CGMP, 21 CFR 210, 21 CFR 211, Part 11, ICH Q7, ICH Q9, ICH Q10, WHO GMP, PIC/S, EU GMP.

Decompose multi-step work into a revisable subgoal plan with stable step IDs, checkpointing, and plan-ahead execution. Load when a task needs explicit planning before action, subgoal decomposition, plan checkpointing, or ReCAP-style plan-ahead execution. Also triggers on "structured plan", "plan with steps", "subgoal graph", "revise the plan", "checkpoint plan", or multi-step tasks where naive one-shot execution would be fragile. Skips trivial single-step tasks. Pairs with dynamic-routing on failure. Distinct from process-decomposer (registry/triage) and problem-to-plan (doc deliverables).