「batch」の検索結果

Build custom Python data sources for Apache Spark using the PySpark DataSource API — batch and streaming readers/writers for external systems. Use this skill whenever someone wants to connect Spark to an external system (database, API, message queue, custom protocol), build a Spark connector or plugin in Python, implement a DataSourceReader or DataSourceWriter, pull data from or push data to a system via Spark, or work with the PySpark DataSource API in any way. Even if they just say "read from X in Spark" or "write DataFrame to Y" and there's no native connector, this skill applies.

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Builds a custom lead scoring model for a business. Takes ICP definition, historical win/loss data, CRM export. Analyzes which attributes correlate with closed-won deals. Generates lead-scoring-model.md with scoring dimensions, point values, thresholds, CRM implementation guide, and validation methodology. Can also score a batch of current leads against the model.

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Implements GitHub or GitLab issues via parallel subagents with review gates between task batches. Use when resolving multi-step issues end-to-end.

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Delegates tasks to Qwen CLI via delegation-core for Alibaba's models. Use when delegation-core selects Qwen or large-context batch processing is needed.

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Delegates tasks to the MiniMax CLI (mmx) via delegation-core. Use when delegation-core selects MiniMax or large-context batch work is needed.

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Use when you need full-text PDFs for a list of DOIs, such as a meta-analysis screening set. Batch-downloads open-access copies via Unpaywall, PMC, OpenAlex and Crossref, lists paywalled papers for manual access, and can convert PDFs to Markdown.

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Use when one validated cohort analysis must be repeated across many exposure/outcome pairs. Generates one R/Python script per combination from a single methodology template, changing only the variables, and aggregates the results into a summary matrix.

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Estimate a covariance / correlation / precision matrix incrementally with precise. Use when data arrives as a stream and you want the matrix updated per observation, or when you want an online (partial_fit) drop-in for sklearn.covariance, which is batch-only.

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Use DocArray for multimodal Pydantic-style documents, typed DocList and DocVec batches, serialization and local storage, FastAPI payloads, and vector retrieval indexes.

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Use ColabFold for protein structure prediction workflows: validate FASTA/CSV/A3M inputs, generate MSAs, plan batch predictions, inspect outputs, and troubleshoot optional AlphaFold, MMseqs2, JAX, GPU, and OpenMM dependencies.

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API attack hunting (REST / GraphQL / gRPC) - BOLA/IDOR, BFLA, mass assignment, excessive data exposure, auth/JWT, introspection + batching, rate-limit abuse. OWASP API Top 10. Wiki-first, FIND schema output.

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Expert Core Data guidance (iOS/macOS): stack setup, fetch requests & NSFetchedResultsController, saving/merge conflicts, threading & Swift Concurrency, batch operations & persistent history, migrations, performance, and NSPersistentCloudKitContainer/CloudKit sync.

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Build automated PII detection and redaction pipelines using spaCy NER, Microsoft Presidio, and AWS Macie integration. Includes confidence scoring, custom entity type definitions, batch processing workflows, and multi-format document scanning for structured and unstructured data sources.

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Use when generating or refactoring Spring Boot 4 JPA entities, repositories, queries, projections, persistence tests, entity relationships, embeddables, IDs, or Hibernate mappings. Covers Jakarta Persistence 3.2 imports, Hibernate 7 entity modeling, new-state detection, N+1 prevention, projections, keyset pagination, batch writes, and common agent mistakes.

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Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

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Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster.

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Bulk grading workflows for Canvas LMS assignments using rubrics. Covers single grading, batch grading, and code execution strategies with safety-first dry runs.

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Translate video subtitles (SRT, VTT, TXT) between languages. Supports batch translation and maintains timing synchronization. Use when creating multilingual subtitles, expanding to international audiences, or translating video transcripts.

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Generate QR codes with URLs and UTM tracking. Exports PNG/SVG with captions. Use for single codes, batch generation, or marketing campaigns with tracking parameters.

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Batch update Yoast SEO metadata (titles, descriptions, focus keyphrases) in WordPress via GraphQL. Use when the user wants to update SEO metadata, optimize titles, fix meta descriptions, or manage Yoast SEO fields across multiple posts. Supports preview mode, progress tracking, and resume capability.

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Fetch Twitter/X post content by URL using jina.ai API to bypass JavaScript restrictions. Use when Claude needs to retrieve tweet content including author, timestamp, post text, images, and thread replies. Supports individual posts or batch fetching from x.com or twitter.com URLs.

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Optimize slow PostgreSQL inserts in Python using psycopg2. Use when: (1) Row-by-row inserts are taking too long over network, (2) executemany() isn't providing speedup, (3) Migrating large datasets to PostgreSQL, (4) Network latency making individual INSERT statements impractical. The key is using execute_values() from psycopg2.extras instead of executemany() or individual execute() calls.

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Fix psycopg2 "could not receive data from server: Operation timed out" or "connection already closed" errors when using Cloud SQL with long-running Python scripts. Use when: (1) psycopg2.OperationalError after a period of no DB activity, (2) DB connection works initially but fails after a non-DB phase (API calls, file processing, CDX scans), (3) Cloud SQL managed PostgreSQL kills idle connections. The fix is to defer DB connection opening until needed, or add reconnection logic for long-running batch processes.

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Set up Cloud Run Jobs with CloudSQL connection. Use when: (1) Deploying long-running batch jobs that need database access, (2) Errors like "Cloud SQL Proxy not found" in container, (3) "password authentication failed" despite correct credentials, (4) Job creation fails with permission errors. Covers socket path configuration, IAM permissions, and common gotchas.

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