“dry”的搜索结果

Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, managed harness agent create, GitHub Copilot harness agent, create agent, update agent, add tool to agent, invoke agent, agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

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Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, managed harness agent create, GitHub Copilot harness agent, create agent, update agent, add tool to agent, invoke agent, agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

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Review safety-sensitive Mole changes using documented bug patterns. Check how cleanup targets are selected, whether commands have time limits, whether cancellation stops later work, and whether dry runs match actual deletion plans.

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• Reviewing cleanup and uninstall safety
• Checking cancellation and command time limits
• Comparing dry-run plans with actual deletion

Plan protein characterization experiments with Adaptyv's cloud laboratory, estimate costs, submit sequences, track progress, and retrieve measured results. Supports binding, thermal stability, expression, fluorescence, and enzyme activity workflows through the API and Python SDK.

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• Plan binding screens for candidate protein sequences
• Estimate experiment costs before submission
• Track laboratory progress and retrieve results

Prepare a Cherry Studio release by checking changes against the published version, writing English and Chinese release notes, updating version metadata, and creating a release branch after review to start the build workflow.

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• Preparing a patch, minor, or major release
• Preparing a beta or release candidate
• Writing release notes from final user-visible changes

Find merged or inactive Git branches, preview cleanup candidates, and delete selected branches after confirmation. Choose a comparison branch, protect important branches with configurable rules, and optionally include remote branches.

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• Previewing branch cleanup candidates
• Removing branches already merged into a release branch
• Finding branches with no recent commits

Expert knowledge for Azure Content Understanding in Foundry Tools development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. Use when designing Content Understanding analyzers, RAG document flows, agentic workflows, REST/SDK calls, or Foundry deployments, and other Azure Content Understanding in Foundry Tools related development tasks. Not for Azure Speech in Foundry Tools (use azure-speech), Azure AI Search (use azure-cognitive-search), Azure AI Document Intelligence (use azure-document-intelligence), Azure AI Video Indexer (use azure-video-indexer).

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字面の同一性ではなく意図（目的）の同一性に基づいてコードの共通化を判断するスキル。 DRY原則の誤適用（字面が同じだが意図が異なるコードの共通化）を検出し、正しい共通化判定を 支援する。コードレビュー、リファクタリング、新規実装時に重複コードの扱いを判断する場合に使用。 対象言語: 言語非依存（Rust, Java, TypeScript, Go, Python, Kotlin, Scala等すべて）。 トリガー：「重複コードを共通化したい」「DRYに従いたい」「似たコードがある」 「この2つの関数をまとめたい」「コードの重複を減らしたい」「共通化すべきか」 「リファクタリングで共通関数にしたい」といった重複コード・共通化関連リクエストで起動。

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architecture で選定したデータ基盤（Microsoft Fabric / Azure Databricks / Foundry IQ）を、事前確認 → plan と hash 承認 → デプロイ → セマンティック層 → runtime 検証 → 計算停止まで API で構築し、MCP 接続情報を Cowork / Copilot Studio / Foundry へ引き渡す。Dataverse は dataverse スキル、データ投入は data-migration スキルへ委譲する。

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How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

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DRY (Don't Repeat Yourself) review rules for code reviews: find the same knowledge — a rule, a constant, a parser, a validation, a protocol detail — implemented in several places that must change together, and tell it apart from code that merely looks alike. Use when reviewing a diff, a pull request or recent changes for duplication, on "DRY review", "is this duplicated", "copy-paste check", or when a review checklist asks for DRY. Pair it with the solid skill for design and with owasp-asvs for security.

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How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

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Pre-deployment security audit of Solidity smart contracts in a Foundry project. Combines static analysis (Slither, Aderyn), symbolic execution (Mythril), and property-based testing (forge fuzz + invariant tests with handlers) to catch reentrancy, access-control, oracle/price manipulation, and arithmetic bugs BEFORE deploying to an EVM chain. Also enforces key hygiene (no plaintext private keys, encrypted cast keystore) and a secure deploy workflow. Use when writing, reviewing, testing, or deploying Solidity/Foundry contracts, building a dApp, or working with forge/cast/anvil, MetaMask, or Web3/DeFi code.

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How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

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Designs, reconciles, and reviews an end-to-end Drydock program for safely staging agent runtimes: immutable worktree provenance, disposable VM containment, typed effect brokerage, global body accounting, resurrection, subscription-capacity economics, context compaction, operator control, evidence, and promotion. Use when several Drydock contracts must compose into one architecture, implementation hypertree, diagram set, or release plan. NOT for launching Port Daddy, running an untrusted subject, replacing focused containment/resurrection/capacity audits, or treating a design packet as runtime authority.

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试运行一轮销售智能体流程，在正式使用前验证模板与智能体类型、检查预算预留，并查看各角色的指令和执行状态记录。

• 演示一轮销售自动化流程
• 验证模板与智能体类型
• 检查预算上限和预留流程

Prepare and launch nf-core/pacsomatic workflows for matched tumor and normal PacBio HiFi samples. Generate sample sheets and launch files from unaligned BAM inputs, run local checks, submit cluster launchers, and troubleshoot startup failures.

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• Prepare sample sheets for matched tumor and normal samples
• Review generated launch files with a dry run
• Submit a Nextflow launcher to a computing cluster

Review Python and BigQuery pipelines for excessive costs, duplicate writes on reruns, and hard-to-diagnose failures. Receive a structured assessment with specific function and line references, prioritized fixes, and rough estimates of the largest cost risks.

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• Check BigQuery pipeline code before production use
• Estimate job counts and cost risks in loops
• Review duplicate writes when rerunning failed jobs

Navigate a smart contract security knowledge base in a suggested reading order. Find guides covering audit preparation, vulnerability patterns, code searches, reproduction tests, reporting, case studies, and AI tools without losing track of the next topic.

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• Finding a starting point for learning smart contract auditing
• Following guides from preparation through reporting
• Locating audit case studies

Locate conversation logs for Claude Code or Codex CLI and replace the latest assistant response with a fixed agreement message. A dry-run option previews the target without writing changes to the log.

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• Replacing the latest assistant response in a conversation log
• Previewing the target response without changing files
• Locating session logs for Claude Code or Codex CLI

Clean up stored agent state in `.squad/` by compressing histories, pruning old logs, archiving stale decisions, and removing orphaned inbox files. Use it after long sessions or before starting work with many agents to reclaim context space.

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• Preparing for work that starts many agents
• Cleaning up after long sessions or sprints
• Managing oversized history files or stored state

Foundry IQ knowledge bases. WHEN: make local or Blob documents searchable; create/diagnose KBs; triage unsupported connectors or multi-source KB creation/reconfiguration (even with classic-search terms); connect existing KB to agents (including multi-source KBs); create/reuse a Search service; retrieve from an existing knowledge base with citations; no brand words needed. Read its procedure before query/target questions. NOT: other KB providers, repository-file search, classic Azure AI Search index/query/app work with no Foundry IQ KB or connector request, generic agent creation.

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Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes. Use for AI Foundry project management, versioned agents, and orchestration. Triggers: "AI Projects", "AIProjectClient", "Foundry project", "versioned agents", "evaluations", "datasets", "connections", "deployments .NET".

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Foundry IQ knowledge bases. WHEN: make local or Blob documents searchable; create/diagnose KBs; triage unsupported connectors or multi-source KB creation/reconfiguration (even with classic-search terms); connect existing KB to agents (including multi-source KBs); create/reuse a Search service; retrieve from an existing knowledge base with citations; no brand words needed. Read its procedure before query/target questions. NOT: other KB providers, repository-file search, classic Azure AI Search index/query/app work with no Foundry IQ KB or connector request, generic agent creation.

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