「dry」の検索結果

Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, add Azure OpenAI backend, add AI Foundry model, test AI gateway, LLM policies, configure AI backend, token metrics, AI cost control, convert API to MCP, import OpenAPI to gateway.

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Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).

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。

Analyze actual Azure spend, bill changes, and AKS or AI costs. WHEN: "Azure cost breakdown", "most expensive resources", "why did my bill increase", "cost spike", "unexpected charge", "Cosmos DB charges", "Foundry model costs", "AKS cluster cost", "namespace cost", "enable AKS cost analysis", "idle AKS capacity". DO NOT USE FOR: forecasts, pricing estimates, rightsizing, commitments, or budgets.

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。

Shared loop-engineering reference for COG skills - the agent loop, deterministic verifiers, termination conditions, in-loop context management, and named patterns. Invoke when designing or debugging a skill that iterates (search-verify-retry, scan-until-dry, fetch-retry-gate).

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。

Review generated or changed production code before it ships, using Clean Code, SOLID, DRY, KISS, YAGNI, and LLM-specific failure-mode checks in any programming language. Best used reactively after an agent writes, edits, refactors, or fixes code, before presenting, committing, or merging the result. Use when the user asks "review this PR", "is this safe to merge?", "make this cleaner", "audit this code", "refactor this", "fix this bug", or after a coding agent produced implementation code. Can also guide writing when explicitly invoked before a risky edit. Invoke it on your own initiative the moment you finish writing, editing, or refactoring non-trivial production code, before presenting or committing — don't wait to be asked. DO NOT USE for factual/conceptual questions, CI/tooling config, git workflow, running/debugging tests, pure architecture discussion, prose writing, data analysis, or test-code review (use test-guard).

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。

Generate an AI creator talking to camera, saying an approved script, as one continuous track — H3 Max reference-to-video through the GooseWorks fal-proxy (bills the Ads agent). Plans takes on line boundaries under H3's 15s cap, dry-runs a cost estimate, generates the first take alone so its voice can be locked and passed to every later take, joins takes with measured 0.10s dissolves, and moves each line's timing onto the words actually spoken. Use for any format with a generated creator speaking a script (split-screen, screen inserts, talking-head ads).

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。

Generate Echidna/Medusa-compatible Solidity fuzz suites from Foundry or Hardhat projects. Trigger on "fizz", "generate fuzz suite", "build fuzz harness", "stateful fuzzing", "fuzzing harness", "property testing", and "invariant suite".

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。

onboarding workflow for creating datasets and applications in Viking AI Search. Supports one-time import from local files (JSON, JSONL, CSV) and MySQL databases, plus scheduled incremental sync for append-only JSONL files and MySQL. All sources are first exported to a bootstrap JSONL file; backend-driven schema inference handles detection, and optional background sync keeps the dataset up to date as new data arrives.

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。

Use when exploring the ai-agent-skills catalog to find, compare, and evaluate skills before installing. Always use --fields to limit output size and --dry-run before committing to an install.

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。

Use when installing skills from a shared ai-agent-skills library repo. Inspect with `--list` first, prefer `--collection`, and preview with `--dry-run` before installing.

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。

Use when regenerating README.md and WORK_AREAS.md in a managed library workspace. Always dry-run first to preview changes.

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。

Use when syncing or updating previously installed skills to their latest version. Always dry-run updates before applying, and check for breaking changes.

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。

Pull latest agent harness improvements from the remote kit repository. Shows a dry-run diff summary, waits for confirmation, then applies updates.

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。

Use when a task involves running a Scenario workflow through MCP, including anything the user calls a Scenario app, a saved pipeline, or a multi-step generation graph. Triggers include listing workflows, building workflow_run's inputs object, pricing a run with a dry run, approving or rejecting a stuck approval node, a run rejected for input length, or a workflows_list reply flooding the context. Creating or editing graphs is scenario-workflow-authoring. Keywords: workflow, app, approval gate.

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。

Fully automated review pipeline: CEO, Design, and Eng reviews with auto-decisions using 6 principles (completeness, boil lakes, pragmatic, DRY, explicit over clever, bias toward action). Only pauses for premise confirmation. Classifies decisions as Mechanical (silent) or Taste (surfaced at final gate).

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。

Edit, validate, debug, publish, and inspect ReachAI Workflow drafts through the Workflow AI Coding REST API. Use when asked to create or modify a workflow graph, add/update/delete nodes or edges, validate GraphSpec, dry-run or debug-run a workflow, inspect trace/run output, check release readiness, publish a validated draft, or work on PAGE_ASSISTANT workflows from Cursor/Codex.

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。

Import CSV or Sheets data into a CRM or ESP with mapping and dedup, approval-gated. "dry-run this import first"

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 knowledge for Azure Translator development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using translate/detect/dictionary APIs, Custom Translator, glossaries, document jobs, or Docker containers, and other Azure Translator related development tasks. Not for Azure AI Language (use azure-language-service), Azure Speech in Foundry Tools (use azure-speech), Azure AI Document Intelligence (use azure-document-intelligence), Azure AI Immersive Reader (use azure-immersive-reader).

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 knowledge for Azure AI Language development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using CLU, custom/health NER, CQA, sentiment/PII APIs, or Language/TA/health containers, and other Azure AI Language related development tasks. Not for Azure AI Search (use azure-cognitive-search), Azure AI Document Intelligence (use azure-document-intelligence), Azure Speech in Foundry Tools (use azure-speech), Azure Translator (use azure-translator).

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 knowledge for Azure Managed Grafana development including troubleshooting, decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when configuring MCP/AI Foundry dashboards, Prometheus/AKS or ADX data sources, private endpoints, or Enterprise upgrades, and other Azure Managed Grafana related development tasks. Not for Azure Monitor (use azure-monitor).

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 knowledge for Azure AI Immersive Reader development including best practices, limits & quotas, security, configuration, and integrations & coding patterns. Use when tuning read-aloud/translation, storing user prefs, Entra auth setup, JS SDK integration, or language support, and other Azure AI Immersive Reader related development tasks. Not for Azure AI Language (use azure-language-service), Azure AI Speech (use azure-speech), Azure Translator (use azure-translator), Azure AI services (use microsoft-foundry-tools).

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。

Generate, simulate, audit, and deploy a Uniswap v4 hook + test pool from a brief, on any Uniswap v4 chain (every testnet and mainnet) - pre-audited templates or a from-scratch freeform hook (flags auto-derived; static audit + dangerous-pattern scan + a behavioral forge test + fork sim gate the deploy). Dry-run by default; explicit arm: to broadcast; testnet default, mainnet behind a double opt-in; records the deploy to main. Every deployed hook inherits a mandatory 10 bps AeonFee protocol fee.

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。

Two-phase contributor rewards - plan builds a tier-priced payout from the repo's merged-PR ranking; send executes it on-chain via Bankr Wallet API with per-recipient idempotency and dry-run.

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。

Build a near-complete company list for any ICP by learning how your real customers label themselves, pulling every industry and keyword they use, judging every single row with a cheap AI model (Jev or gpt-5-nano), and running a Claygent lookalike loop until it runs dry. The method from the SCULPT 2026 talk "Build the perfect company list". Use when a database filter feels too narrow or too noisy, when someone says "find every company like our customers", "our TAM looks too small", or "why is this list full of junk".

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。