'data flow' 검색 결과

Summarizes Google Cloud Data Lineage graphs to help users debug data quality issues and understand data provenance for BQ/GCS. Use when summarizing upstream and downstream data flows, and presenting complex lineage data as an intuitive Markdown report. Don't use for generic BigQuery queries, editing lineage relationships, or downstream deprecation. Don't use for downstream blast-radius impact analysis (use datalineage-bigquery-asset-impact-analysis skill instead).

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Use when designing or reasoning about an Agentforce Grid worksheet — the spreadsheet-like Agentforce workspace where rows are jobs and columns are sequential Data / AI / Action steps that process left to right — to run bulk record updates, generate insights, or test multi-turn AI conversations over CRM or Data Cloud (Data 360) data without Apex or Flow. Trigger keywords: Agentforce Grid, workbook, worksheet, grid column, data column, AI column, action column, bulk AI update, Flex Credits per row, metered compute. NOT for building the agent, its topics, or its actions themselves (use agentforce/* action and topic skills), NOT for single-record prompt design (use the prompt-builder skills), NOT for deployable metadata/Apex (Grid is a no-code Studio tool, not a metadata type), and NOT for AI governance/ethics policy (use admin/ai-ethics-and-governance-requirements).

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Use when configuring Workflow-based Outbound Messages to push SOAP payloads to external endpoints — endpoint setup, field selection, retry behavior, and delivery monitoring. NOT for the listener's session ID callback into Salesforce — use integration/outbound-messages-and-callbacks. NOT for JSON webhooks or Flow-triggered pushes — use integration/outbound-webhook-from-salesforce. Keywords: WorkflowOutboundMessage, outboundMessages, endpointUrl, includeSessionId, integrationUser, useDeadLetterQueue, apiVersion, SOAP Ack, .workflow file, approval outbound message action.

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Use this skill when setting up, configuring, or managing Salesforce Lightning Knowledge — enabling the feature, designing record types on Knowledge__kav, configuring Data Categories for organization and visibility control, setting up publishing workflows, and layering approval processes. Trigger keywords: Lightning Knowledge setup, Knowledge article record types, Data Category visibility, Knowledge publishing workflow, Knowledge__kav configuration. NOT for converting Classic Article Types to Lightning — use admin/knowledge-classic-to-lightning. NOT for bulk-loading articles from an external help center — use data/knowledge-article-import. NOT for sizing the taxonomy itself (how many groups, how deep) — use architect/knowledge-taxonomy-design.

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Interactively build a Narrative identity graph workflow from one or more first-party datasets and (optionally) third-party data sources. Confirms each input dataset is mapped to the Rosetta Stone graph edge attribute (mapping it via /generate-rosetta-stone-mappings if not), then composes and submits a workflow that unions every edge source and labels connected components. Use when: "build an identity graph", "generate an identity graph", "create an identity graph", "stitch these datasets into a graph", "make a graph workflow", "label connected components on these datasets", "I want a person graph / household graph / device graph". (narrative-identity)

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Author and submit a Narrative workflow from a natural-language intent. Picks the closest example from `assets/examples/`, adapts it to the user's case, walks the YAML against the spec, resolves the data plane, and submits via `narrative_workflows_create` only after the user has approved the rendered spec. Use when: "create a workflow that does X", "schedule a daily refresh of dataset Y", "wrap this NQL as a workflow", "build a pipeline that creates view A then refreshes view B", "submit this workflow YAML", "productionize this query as a recurring job". (narrative-common)

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FlutterSecureStorage (v10.x) for encrypted key-value storage of sensitive data using platform-native secure enclaves with biometric authentication support. Use this skill when storing OAuth tokens (access tokens, refresh tokens, ID tokens), API keys and secrets, user passwords or PINs, session tokens, encryption keys, private keys for cryptography, certificate data, biometric enrollment data, banking credentials, health data, or any sensitive information requiring platform-level encryption (iOS Keychain with Secure Enclave, Android Keystore with Hardware-backed keys). Supports Touch ID, Face ID, and Android biometric prompt integration. Handles biometric change invalidation (resetOnError), data migration from SharedPreferences/EncryptedSharedPreferences, Google Drive backup exclusion, automatic key rotation, configurable encryption algorithms (AES), and prevents data extraction even on rooted/jailbroken devices. Essential for authentication flows, secure credential management, or compliance requirements (PCI DSS, HIPAA, GDPR data protection).

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Patterns for loading PDE simulation datasets (PDEBench, PhiFlow, JAX-CFD) from HDF5 files. Handles layout detection (single tensor vs separate variables), spatial/temporal downsampling, multi-variable systems, HuggingFace and DaRUS data sources, and efficient PyTorch DataLoader creation. Use when preparing PDE data for neural operator training.

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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.

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Start AEM Workflows on AEM as a Cloud Service using all available triggering mechanisms. Use when starting workflows manually via the Timeline UI, programmatically via WorkflowSession.startWorkflow(), via the HTTP Workflow API, through Manage Publication, or passing initial metadata and payload to a workflow instance.

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Implement custom AEM Workflow Java components on AEM as a Cloud Service. Use when writing WorkflowProcess steps, ParticipantStepChooser implementations, registering services via OSGi DS R6 annotations, reading step arguments from MetaDataMap, accessing JCR payload via WorkflowSession adapter, reading and writing workflow metadata and variables, and handling errors with WorkflowException for retry behavior.

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Start AEM Workflows on AEM as a Cloud Service using all available triggering mechanisms. Use when starting workflows manually via the Timeline UI, programmatically via WorkflowSession.startWorkflow(), via the HTTP Workflow API, through Manage Publication, or passing initial metadata and payload to a workflow instance.

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[BETA] Implement custom AEM Workflow Java components on AEM as a Cloud Service. This skill is in beta. Verify all outputs before applying them to production projects. Use when writing WorkflowProcess steps, ParticipantStepChooser implementations, registering services via OSGi DS R6 annotations, reading step arguments from MetaDataMap, accessing JCR payload via WorkflowSession adapter, reading and writing workflow metadata and variables, and handling errors with WorkflowException for retry behavior.

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반복하는 로컬 Hermes 작업 절차를 공개하고 재사용할 수 있는 ECC 스킬과 릴리스 자료로 정리합니다. 개인정보와 인증 정보를 제거하고 기기별 경로를 바꿉니다.

• 반복 작업을 공개 스킬로 정리합니다
• 공개용 인수인계 자료를 준비합니다
• 비공개 데이터 없이 야간 작업을 문서화합니다

전자 의무 기록의 진료 기록, 처방, 검사 결과 화면을 설계합니다. 약물 상호작용 경고, 변경 이력, 의료 현장에서 입력하기 쉬운 화면의 설계 지침을 정리합니다.

• 진료 흐름 설계
• 약물 상호작용 경고 구현
• 검사 결과의 이상 수치 표시

Android와 KMP 프로젝트의 모듈 구조와 의존 관계를 정리합니다. 업무 로직·화면·데이터 접근을 분리해 설계하고 구현 예시로 각 계층의 역할을 설명합니다.

• Android와 KMP 프로젝트 구조 설계
• 화면 코드에서 업무 로직 분리
• 기기와 서버의 데이터 접근 설계

여러 에이전트의 작업을 순서대로 연결하고 이전 결과를 다음 단계에 전달합니다. 직접 구성한 절차나 기본 제공 절차로 코드 분석, 수정, 테스트와 데이터 변환을 진행합니다.

• 코드 분석으로 개선 계획을 만듭니다
• 우선순위에 따라 리팩터링합니다
• 테스트 부족 확인과 단위 테스트 생성에 씁니다

Build Python workflows for filtering, grouping, joining, and reshaping table data with Polars. Get guidance on migrating pandas code and using lazy evaluation, which optimizes a processing plan before execution, for large datasets.

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• Filtering and summarizing large CSV files
• Migrating pandas processing code to Polars
• Joining and reshaping multiple tables

Use PyTDC to discover therapeutic machine-learning datasets, create task-appropriate training and test splits, and evaluate predictions against curated benchmarks. Also score selected molecules with supported scoring functions, with explicit download plans and bounded outputs.

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• Find datasets for therapeutic machine-learning tasks
• Plan and create task-appropriate data splits
• Evaluate predictions with benchmark-specific metrics

Understand and extend GitNexus taint analysis, which tracks data from input sources to potentially dangerous operations. Use its implementation guidance to investigate missed or incorrect findings, add detection models, and validate analysis changes.

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• Investigating missed or incorrect data-flow findings
• Adding input sources, dangerous operations, or sanitizers
• Reviewing analysis that follows data across functions

Helps design ClickHouse tables, write analytical queries, and build data ingestion workflows. Covers batch inserts, precomputed summaries, and performance monitoring for teams working with large datasets and time-based activity analysis.

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• Designing tables for large analytical datasets
• Improving slow aggregation queries
• Analyzing user retention and conversion steps

Prepare Python SDK releases by updating versions, package metadata, pinned dependencies, lockfiles, and build workflows. The skill covers release process changes and publication verification, helping agents handle packaging work within the repository’s release procedures.

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• Preparing a Python SDK release
• Updating pinned dependency versions
• Changing packaging metadata and build workflows

Create and improve Playwright end-to-end tests for Studio changes. Check complete user actions, including whether settings affect requests, data survives reloads, tools return expected results, and workflows pass data correctly.

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• Testing whether agent settings affect actual requests
• Checking that saved data survives page reloads
• Verifying tool outputs and workflow data flow

Audit, create, and fix structured data, the labels that help search systems understand web pages. Get JSON-LD code for articles, products, and other page types, plus validation findings and guidance on where to place it.

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• Adding structured data to articles or product pages
• Investigating structured data errors in Search Console
• Auditing existing markup and prioritizing fixes