Results for “apa”

数据工程 — 数据平台从业者的认知操作系统, 覆盖把数据从源系统搬运成可靠 / 可查询 / 可信赖形态供分析 / ML / 数据产品消费的全生命周期 (生成 → 摄取 → 存储 → 转换 → 服务 + 安全/数据管理/DataOps/数据架构/编排/软件工程 六条暗流, Reis & Housley 框架): 摄取与集成 (批 + CDC 变更数据捕获 Debezium + EL 工具 Fivetran/Airbyte/Meltano/dlt + Kafka Connect + schema drift) / 存储与文件表格式 (对象存储数据湖 + 列存 Parquet/ORC/Arrow/Avro + 开放表格式 Apache Iceberg/Delta Lake/Apache Hudi + lakehouse + 分区/compaction) / 转换与建模 (ELT dbt/SQLMesh + Spark + 维度建模 Kimball + Inmon + Data Vault + 大宽表 OBT + 渐变维 SCD + 增量模型 + 语义/指标层) / 编排与工作流 (Apache Airflow/Dagster/Prefect/Mage/Kestra/Apache DolphinScheduler + DAG + 幂等 + 回填 backfill + 数据资产调度) / 批流与实时 (Apache Kafka/Apache Flink/Spark Structured Streaming/Kinesis/Pulsar/Redpanda + Lambda vs Kappa + watermark/窗口/exactly-once + 流式 SQL Materialize/RisingWave + 实时 OLAP ClickHouse/Apache Druid/Apache Pinot/StarRocks/Apache Doris) / 数仓与查询引擎 (Snowflake/BigQuery/Redshift/Databricks SQL/Trino/Presto/DuckDB/Polars + 存算分离 + MPP) / 数据质量测试与可观测性 (dbt tests/Great Expectations/Soda + 数据契约 + Monte Carlo data downtime + 新鲜度/量/schema 异常检测) / 数据治理编目与血缘 (DataHub/Amundsen/OpenMetadata/Unity Catalog + 列级血缘 + PII 分类 + 访问控制 + GDPR) / DataOps 与可靠性 (数据 CI/CD + 转换版本控制 + 环境隔离 + 幂等重处理 + 数据 SLA/SLO + 计算存储 FinOps) / 数据架构范式 (现代数据栈 + lakehouse + data mesh + data fabric + 去中心化 vs 中心化所有权) / 分析工程角色 (dbt 时代连接数据工程与分析的桥) — 不含 数据科学/ML 建模本身 (是下游消费者) / BI 仪表盘制作 (serving 下游) / 数据分析报表为终点 / 'data engineer = 跑 Hadoop 的' 过时窄化 / 通用后端应用开发 (平行学科) (Data Engineering — the cognitive operating system of practitioners who design, build, and operate the data platform: moving data from source systems into reliable, queryable, trustworthy form for analytics / ML / products, covering (a) the data engineering lifecycle (generation → ingestion → storage → transformation → serving, with the undercurrents security / data management / DataOps / data architecture / orchestration / software engineering — Reis & Housley framing), (b) ingestion & integration (batch + CDC change-data-capture with Debezium, EL tools Fivetran / Airbyte / Meltano / dlt, Kafka Connect, API + file + database sources, schema drift handling), (c) storage & file/table formats (object storage data lakes, columnar formats Parquet / ORC / Arrow / Avro, open table formats Apache Iceberg / Delta Lake / Apache Hudi, lakehouse architecture, partitioning / compaction / Z-ordering), (d) transformation & modeling (ELT with dbt / SQLMesh, Spark, dimensional modeling Kimball, Inmon CIF, Data Vault, One Big Table / wide tables, normalization vs denormalization, slowly changing dimensions, incremental models, the semantic / metrics layer), (e) orchestration & workflow (Apache Airflow, Dagster, Prefect, Mage, Kestra, Apache DolphinScheduler, DAGs, idempotency, backfills, data-aware / asset-based scheduling), (f) batch vs streaming & real-time (Apache Kafka, Apache Flink, Spark Structured Streaming, Kinesis / Pulsar / Redpanda, the Lambda vs Kappa debate, watermarks / windowing / exactly-once, streaming SQL Materialize / RisingWave, real-time OLAP ClickHouse / Apache Druid / Apache Pinot / StarRocks / Apache Doris), (g) warehouses & query engines (Snowflake, BigQuery, Redshift, Databricks SQL, Trino / Presto, DuckDB, Polars, decoupled storage & compute, MPP), (h) data quality, testing & observability (dbt tests, Great Expectations, Soda, data contracts, Monte Carlo / data downtime, freshness / volume / schema anomaly detection, unit / integration testing of pipelines), (i) data governance, catalog & lineage (DataHub, Amundsen, OpenMetadata, Unity Catalog, column-level lineage, PII / data classification, access control, GDPR / data privacy), (j) DataOps & reliability (CI/CD for data, version control of transformations, environments, idempotent reprocessing, SLAs / SLOs for data, cost / FinOps for compute & storage), (k) data architecture paradigms (modern data stack, data lakehouse, data mesh, data fabric, decentralized vs centralized ownership), (l) the analytics engineering role (the dbt-era bridge between data engineering and analysis); N

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Supports medical device quality investigations through CAPA, a process for corrective and preventive action. Helps identify root causes, plan actions, verify results, and report overdue cases, recurrence rates, and other program metrics.

• Investigating recurring quality problems
• Planning corrective actions after audit findings
• Checking whether corrective actions prevented recurrence

Package workspace skills and their required service integrations into one named, installable capability. Create a manifest that references existing skills, install the capability, and report which integrations still need to be connected.

• Bundle multiple skills for a shared outcome
• Declare required external service integrations
• Create a manifest that references existing skills

Helps add Apache 2.0 license headers to new Apache Beam files using the correct comment format and placement. Covers source files, configuration, documentation, and local checks for missing license notices.

• Adding license headers to new source and test files
• Checking license placement in YAML frontmatter
• Investigating missing headers with RAT

Migrate Java projects from FastExcel 1.3 to Apache Fesod by updating Maven or Gradle dependencies, package imports, and entry class calls. Handles both legacy package namespaces and identifies conditional helper class changes.

• Migrating an existing Java Excel application to Fesod
• Updating Maven or Gradle dependencies
• Cleaning up partially migrated package imports

Automate Apaleo tasks through Rube MCP, a connection for agent tools. The skill guides tool discovery, checks the service connection, and runs operations using current input requirements, with support for bulk workflows.

• Discovering available Apaleo operations
• Checking the connection before running tasks
• Running multiple operations in bulk

Automate Apaleo tasks through Rube MCP, a connection for agent tools. The skill guides tool discovery, checks the service connection, and runs operations using current input requirements, with support for bulk workflows.

• Discovering available Apaleo operations
• Checking the connection before running tasks
• Running multiple operations in bulk

Customize an installed workspace capability, a bundle of agent tools and guidance. It updates the configuration to add MCP servers, which connect agents to tools, or change skills, integrations, and instructions while preserving existing settings.

• Add an MCP server to an installed capability
• Update existing skills and integrations
• Revise instructions for a capability's agent

Navigate Apache Beam contributions from finding an issue to submitting a pull request, a proposed code change, and responding to reviews. Covers environment setup, tests, formatting, licensing requirements, and closer evaluation of changes to core components.

• Prepare a first Apache Beam contribution
• Organize a pull request and its related issue
• Check tests and formatting before submission

Manage capa CLI configuration — `capabilities.yaml` / `capabilities.json`, skills, MCP servers, tools, hooks, sub-agents, rules, plugins, AGENTS.md / CLAUDE.md, security options, tool exposure modes, `capa wrap` shadow workspaces, `--passthrough` native writes, registries (including Claude marketplaces), activity traces, and the local Web UI. Use whenever the user edits the capabilities file, runs any `capa` command (init, install, add, wrap, clean, sh, start/stop/restart/status, auth, upgrade, cache, registry), wires up an MCP server, adds a skill from GitHub / GitLab / a registry / a remote URL / a local path, configures secrets via `${VarName}` placeholders, installs lifecycle hooks, defines sub-agents, or troubleshoots a failed install. Use even when the user only names a fragment ("add a skill", "wire up brave search", "wrap cursor", "block this phrase in skills") without saying "capa" — if a `capabilities.yaml` or `capabilities.json` lives at the project root, this is almost certainly the right skill. If the project does NOT yet have a capabilities file, point at the `bootstrap` skill instead.

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Review OpenClaw frontend code and rendered screens for departures from Carapace design rules, accessibility issues, responsive defects, and inconsistent copy. Produce evidence-based JSON and Markdown reports that distinguish documented violations from judgment-based suggestions and guide narrowly scoped fixes.

• Check compliance with Carapace design rules
• Review desktop and mobile interfaces
• Audit accessibility and brand copy

Build and test Apache Beam with Gradle, the system that manages its project tasks. Find relevant configuration files, inspect library dependencies, run formatting checks, publish locally, and investigate build failures.

• Build or test a specific Apache Beam module
• Inspect library dependencies and version conflicts
• Run formatting and pre-commit checks

Use this skill to configure and manage Field Service Lightning workforce capacity — covering ServiceResourceCapacity for individual resource caps, WorkCapacityLimit for territory-level demand throttling, and reporting strategies for capacity vs. utilization analysis. NOT for resource skills, certifications, or preference setup — use admin/fsl-resource-management. NOT for work rules and service objectives — use admin/fsl-scheduling-policies. NOT for Omni-Channel agent capacity — use architect/omni-channel-capacity-model.

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Apply Carapace styling to existing OpenClaw interfaces through shared CSS, themes, and semantic tokens—named values for visual roles. Reuse existing components and check interaction states, accessibility, and browser screenshots for consistent presentation.

• Align existing OpenClaw screens with Carapace styling
• Reuse shared CSS and existing interface components
• Review focus states, status colors, and accessible names

Develop and troubleshoot Apache Beam's YAML SDK. Understand its source files, run data processing pipelines locally or on Dataflow, test changes, and investigate provider issues or mismatched record fields.

• Find source files for Beam YAML changes
• Run a YAML pipeline locally or on Dataflow
• Run unit tests and external service integration tests

Uses capa to identify malware capabilities from a binary: running rule-based detection over disassembly, mapping matched capabilities to MITRE ATT&CK and MBC, and interpreting results to focus deeper analysis. Activates for requests to run capa, generate a capability report, or map a sample's behaviors to ATT&CK automatically.

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Create academic Word documents with APA, Chicago, IEEE, or MLA citation formatting. Organize abstracts, equations, figure and table references, footnotes, and bibliographies, with support for two-column journal layouts in a single .docx file.

• Formatting a manuscript in APA style
• Preparing a two-column IEEE conference paper
• Adding numbered equations and figure references to a thesis

Prepare an Apache bRPC release with tracked progress, version updates, release candidate checks, and draft release notes and emails. The workflow leaves signing, publishing, and other external release actions to the release manager.

• Prepare version files and a release branch
• Resume release work from a saved progress record
• Verify local or uploaded release packages

Develop and verify changes to Apache Beam's Python SDK. Set up a virtual environment, run tests, build installable packages or containers, and run data processing pipelines with the modified SDK.

• Set up an Apache Beam Python SDK development environment
• Run unit and integration tests for SDK changes
• Build SDK packages and container images

Build Apache Beam connectors that read and write external data. This guide covers Java implementation, schema support for structured records, access from Python and Go, Managed API integration, and testing.

• Add a connector for an external data system
• Make a Java connector available to Python and Go
• Integrate a connector with Beam YAML and the Managed API

Look up one genetic variant in BioBank Japan and get a concise summary of its associations with diseases and traits. Accepts rsIDs or genome coordinates, converts the query as needed, and can save complete results.

• Check disease and trait associations for one variant
• Look up a variant using an rsID or genome coordinates
• Save complete association results for further analysis

Review Apache Pinot changes against naming and API conventions, including public names, method signatures, REST endpoints, JSON fields, and documentation. Use it when adding or renaming elements exposed to other code or users.

• Reviewing public class, method, or field names
• Checking new enum constants or DataType values
• Reviewing REST endpoints and JSON fields

Review Apache Pinot changes affecting frequently executed code or making performance claims in a pull request. Relevant areas include operators, segment readers, aggregation functions, and locking inside loops that process individual rows.

• Review changes to frequently executed processing code
• Review performance improvement claims in a pull request
• Review locking inside per-row loops

Review Apache Pinot changes for correctness in null handling, numeric types, type-based processing, and resource lifecycles. Use it to examine changes to calculations, aggregations, window functions, and segment cleanup or persistence.

• Reviewing changes to null handling
• Checking branches based on data or index types
• Reviewing numeric processing in aggregations and window functions