「batch」の検索結果

Publish, deactivate, and delete AEM content programmatically via the Cloud Service Replication API (com.day.cq.replication). Covers single-path and bulk activation, preview-tier targeting with AgentFilter, synchronous/async options, ReplicationStatus checks, permission validation, workflow process steps, and replication event handling. Use when the user needs to publish pages, activate or deactivate content, check replication status, build workflow steps that replicate content, listen to replication events, query batch publication state, or work with Replicator, ReplicationOptions, or ReplicationStatusProvider in AEM Cloud Service.

日本語の概要は準備中です。原文の説明を表示しています。

Resize photos and videos to exact pixel dimensions or aspect ratios using Adobe tools. Use this skill whenever a user wants to resize, scale, or change the dimensions of an image or video file — including phrases like "resize this to 1920x1080", "make this 4K", "scale to 800x600", "change the aspect ratio to 16:9", "resize my video", "make the image smaller", "crop to square", "fit this to a specific size", "resize for print", "resize for web", "make it 300 DPI ready", "change canvas size", "resize a batch of photos", or any request specifying target dimensions (W×H, ratio, or named size like "4K", "HD", "A4"). Also triggers for: "make this fit a specific size", "resize to [any dimension]", "I need this at [WxH]", "scale my video down", "change resolution", "downscale", "upscale". NOT for social media platform sets (use adobe-create-social-variations for that). Uses image_crop_and_resize for photos, video_resize for videos.

日本語の概要は準備中です。原文の説明を表示しています。

Perform a full InDesign data merge from a CSV/TSV and an .indd template (or a PDF that gets converted to .indd automatically). Use this skill whenever the user wants to merge a data file with a layout template — including visiting cards, certificates, badges, catalogs, mailers, labels, invoices, or any per-row personalisation. Triggers on: "data merge", "InDesign merge", "merge my CSV with InDesign", "batch export PDF from template", "variable data InDesign", "personalise each row", or any request combining an .indd / PDF template with a data file. Use this skill even when only two of the three inputs (template, data, images) are mentioned — and even when the user never uses the phrase "data merge".

日本語の概要は準備中です。原文の説明を表示しています。

Manage Sanity content -- schema changes, data migrations, debugging queries, batch operations, and revalidation.

日本語の概要は準備中です。原文の説明を表示しています。

Clarify a concrete requirements decision, create one bounded static interview packet, or run an explicitly user-invoked frontier batch over a plan, decision, or idea; trigger on explicit grill/interview/refinement intent or a named material ambiguity, not on ordinary planning, implementation, research, test-plan, report-style review, or BUILD work.

日本語の概要は準備中です。原文の説明を表示しています。

Reduces LLM costs and improves response times through caching, model selection, batching, and prompt optimization. Provides cost breakdowns, latency hotspots, and configuration recommendations. Use for "cost reduction", "performance optimization", "latency improvement", or "efficiency".

日本語の概要は準備中です。原文の説明を表示しています。

Hunting skill for graphql vulnerabilities. Built from 12 public bug bounty reports across IDOR via node() / GID, mutation IDOR including AI/LLM features, cross-tenant IDOR, SSRF via argument, batching-DoS, query-cost-bypass, SQLi via argument, broken-object-level-authz, auth-bypass via unscoped mutations, and PII exposure from missing field-level authz. Use when hunting graphql on any target.

日本語の概要は準備中です。原文の説明を表示しています。

Advanced API exploitation methodology focused on business logic abuse and sophisticated attack patterns that bypass traditional security controls. Covers business logic bypass through API call chaining and workflow manipulation. Addresses GraphQL-specific attacks including batching for credential brute-force, query depth exploitation, and introspection abuse. Includes pagination exploitation for data exfiltration, webhook hijacking for SSRF and data interception, and resource exhaustion through algorithmic complexity attacks. Covers race conditions in API transactions using parallel request techniques. Provides comprehensive JWT manipulation including algorithm confusion, kid injection, jku/x5u abuse, and claim tampering. Details API key leakage detection across source repositories, client-side code, and error messages. Covers undocumented endpoint discovery through predictable naming, debug routes, and source map analysis. Tooling includes Arjun, ParamSpider, jwt_tool, and GraphQL Voyager. Designed for a...

日本語の概要は準備中です。原文の説明を表示しています。

Generic GraphQL bugs (IDOR, mass assignment, introspection, batching abuse — see hunt-graphql)

日本語の概要は準備中です。原文の説明を表示しています。

Expert patterns for HubSpot CRM integration including OAuth authentication, CRM objects, associations, batch operations, webhooks, and custom objects. Covers Node.js and Python SDKs. Use when: hubspot, hubspot api, hubspot crm, hubspot integration, contacts api.

日本語の概要は準備中です。原文の説明を表示しています。

High-quality voice synthesis with 18 personas, 32 languages, sound effects, batch processing, and voice design using ElevenLabs API.

日本語の概要は準備中です。原文の説明を表示しています。

Send SMS and make voice calls via Dialpad API. Supports single/batch SMS, voice calls with TTS, and caller ID selection.

日本語の概要は準備中です。原文の説明を表示しています。

Advanced filesystem operations - listing, searching, batch processing, and directory analysis for Clawdbot

日本語の概要は準備中です。原文の説明を表示しています。

Store, verify, and manage AI data on the Ethereum blockchain (Base network) using the ERCData standard. Use when an agent needs to store data fingerprints on-chain, verify data integrity, create audit trails, manage access control for private data, or interact with the ERCData smart contract. Supports public and private storage, EIP-712 verification, snapshots, and batch operations.

日本語の概要は準備中です。原文の説明を表示しています。

End-to-end MegaETH development playbook (Feb 2026). Covers wallet operations, token swaps (Kyber Network), eth_sendRawTransactionSync (EIP-7966) for instant receipts, JSON-RPC batching, real-time mini-block subscriptions, storage-aware contract patterns (Solady RedBlackTreeLib), MegaEVM gas model, WebSocket keepalive, bridging from Ethereum, and debugging with mega-evme. Use when building on MegaETH, managing wallets, sending transactions, or deploying contracts.

日本語の概要は準備中です。原文の説明を表示しています。

Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).

日本語の概要は準備中です。原文の説明を表示しています。

Apply recognition-primed decision making to agents operating under time pressure, uncertainty, and ambiguous cues. Use for situation assessment, RPD-style action selection, and decision-support design in operational settings. NOT for batch optimization, static planning with full information, or tasks where exhaustive search is cheap and harmless.

日本語の概要は準備中です。原文の説明を表示しています。

Apply recognition-primed decision making to agents operating under time pressure, uncertainty, and ambiguous cues. Use for situation assessment, RPD-style action selection, and decision-support design in operational settings. NOT for batch optimization, static planning with full information, or tasks where exhaustive search is cheap and harmless.

日本語の概要は準備中です。原文の説明を表示しています。

Expert in background job processing with Bull/BullMQ (Redis), Celery, and cloud queues. Implements retries, scheduling, priority queues, and worker management. Use for async task processing, email campaigns, report generation, batch operations. Activate on "background job", "async task", "queue", "worker", "BullMQ", "Celery". NOT for real-time WebSocket communication, synchronous API calls, or simple setTimeout operations.

日本語の概要は準備中です。原文の説明を表示しています。

Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Compares the version-pinned A2A, MCP, and JSON-RPC role boundaries covered by this bundle; OpenAI and LangChain-specific APIs require their own current sources. Teaches when to use rigid schemas versus free-form content with validation, typed versus untyped boundaries, and streaming versus batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

日本語の概要は準備中です。原文の説明を表示しています。

Create a GitHub PR with an engineered description that matches the team's established format — numbered problem-fix sections, technical tables, file-by-file breakdowns, commit lists, and verification checklists. Use this skill whenever the user asks to create a PR, ship code, prepare a merge request, write a PR description, or says anything like "ship to boss", "pr perfect", "make a PR", "prepare PR for merge", "create pull request", "engineer the PR", "PR description", or "ready to push". Also trigger when the user has finished a batch of work and says things like "let's wrap this up", "time to merge", "push this to main", or "get this ready for review". The skill handles everything: commit analysis, description generation, branch creation, and PR submission via gh CLI.

日本語の概要は準備中です。原文の説明を表示しています。