「yaml」の検索結果

Map dbt `schema.yml` / `properties.yml` models and sources into ktx semantic-layer overlays and column notes. Covers `sources:` vs `models:`, column `data_tests` (not_null, unique, accepted_values, relationships), and how bundle-time writes complement manifest backfill from git sync. Load when the WorkUnit's `skillNames` includes `dbt_ingest` or when raw files are dbt YAML under `models/` / `sources/`.

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Map a MetricFlow semantic_model or metric into ktx semantic layer sources. Covers the MetricFlow to ktx primitive table, `extends:` inheritance flattening, metric-type handling (simple / derived / ratio / cumulative / conversion), `model: ref('x')` resolution, and four worked examples. Load when the turn contains `.yml`/`.yaml` files with top-level `semantic_models:` or `metrics:`.

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Pre-deployment validation for Azure readiness. Run deep checks on configuration, infrastructure (Bicep or Terraform), RBAC role assignments, managed identity permissions, and prerequisites before deploying. WHEN: validate my app, check deployment readiness, run preflight checks, verify configuration, check if ready to deploy, validate azure.yaml, validate Bicep, test before deploying, troubleshoot deployment errors, validate Azure Functions, validate function app, validate serverless deployment, verify RBAC roles, check role assignments, review managed identity permissions, what-if analysis, validate Container Apps deployment.

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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, create agent, update agent, add tool to agent, invoke agent, agent.yaml, agent insights, pull agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, prompt optimizer, 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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Creates and structures SKILL.md files for AI coding agents, including YAML frontmatter, trigger phrases, directive instructions, decision trees, code examples, and verification checklists. Use when the user asks to write a new skill, create a skill file, author agent capabilities, generate skill documentation, or define a skill template for Claude Code agents.

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Capture a user's real writing voice from 5-20 prior samples, store a local voice.yaml fingerprint, and enforce it on newsjack drafts so AI tells disappear. Measures voice with named stylometry lenses (Burrows's Delta function-word vector, MATTR lexical diversity, sentence-length burstiness, Biber Dimension-1 register, opener-POS profile, punctuation rates) and gates drafts against the fingerprint as bands, not vibes.

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Text-to-speech voice-over generation from YAML speaker notes using Azure Speech SDK with SSML pronunciation control, or an offline Piper engine that needs no credentials

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PowerPoint slide deck generation and management using python-pptx with YAML-driven content and styling

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Generate customer-card PowerPoint content YAML from Design Thinking canonical artifacts and build using the shared PowerPoint skill pipeline

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Author and validate durable data and ML feasibility studies using the Feasibility Study Interchange Profile, constrained YAML authority, UUID URN identity, lifecycle lineage, and evidence traceability. Use when assessing whether available data and technical evidence support a proposed outcome.

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First contact with Crabbox: run your repository's tests inside a disposable Docker or Podman container on your own machine, no account and no cloud spend, then stop the box. Use when someone asks what Crabbox is or how to try it, wants a throwaway sandbox for a repo with no crabbox.yaml yet, or is about to run crabbox init here; hand off to the crabbox skill for config that already exists, leased remote machines, jobs, secrets, or artifacts.

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Configures and runs LLM evaluation using Promptfoo framework. Use when setting up prompt testing, creating evaluation configs (promptfooconfig.yaml), writing Python custom assertions, implementing llm-rubric for LLM-as-judge, or managing few-shot examples in prompts. Triggers on keywords like "promptfoo", "eval", "LLM evaluation", "prompt testing", or "model comparison".

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Installs, diagnoses, repairs, and personalizes the official Tencent IMA skill (a wrapper around upstream ima-skill, not a replacement), including fan-out search across IMA knowledge bases with priority boosting. Use when the user mentions IMA / 腾讯 IMA / ima-skill, needs IMA API key setup, 知识库搜索 / 笔记搜索, imports URLs into a KB to extract content, or hits "Skipped loading skill(s) due to invalid SKILL.md" (ima-skill's missing-YAML-frontmatter bug).

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**WORKFLOW SKILL** - Evaluate AI agent skills using structured benchmarks with YAML specs, fixture isolation, and pluggable validators. USE FOR: run waza, waza help, run eval, run benchmark, evaluate skill, test agent, generate eval suite, init eval, compare results, score agent, agent evaluation, skill testing, cross-model comparison. DO NOT USE FOR: improving skill frontmatter (use waza dev), creating new skills from scratch (use skill-creator), token counting or budget checks (use waza tokens). INVOKES: Copilot SDK executor, mock engine, code/regex validators. FOR SINGLE OPERATIONS: use waza run directly for a single benchmark.

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Scholarly research companion using Knows sidecar spec (.knows.yaml). Generates, validates, reviews, queries, and compares structured research-paper sidecars, and fetches them from knows.academy. Use for academic literature search, survey synthesis, paper authoring assistance, and peer review with token-efficient claim/evidence/relation access.

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A fixture whose description uses a YAML folded scalar, which parses with an embedded trailing newline unless normalized before use in Markdown tables.

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Scaffold an isolated CLOUD dev box per agent (via crabbox + Daytona) for any codebase — the parallel-safe counterpart to dev-local-setup. Each agent gets its own full stack (own DB + dev server) and an in-box browser for e2e, so concurrent loops never collide on ports/state. Sets up the snapshot image, .crabbox.yaml, an idempotent setup.sh (also boots the stack locally), and a cbx.sh wrapper. Use when the user says "set up crabbox", "give each agent its own box", "add cloud testing", "make this repo testable in the cloud / on Daytona", "parallel-test this", or when setup-codebase-harness needs true per-agent isolation.

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Generates draft requirements from Simulink models. Use when drafting or updating requirement artifacts from a model. Prefers Requirements Toolbox (.slreqx) when available; falls back to structured YAML.

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CloudBase declarative deployment from a cloudbaserc config (声明式部署, 配置式部署, cloudbaserc 部署) through the deployBuild / deployPlan / deployApply MCP tools. Use when deploying database, functions, app, hosting, or gateway resources described in cloudbaserc.json/yaml as a single desired-state config, when a user wants to build static hosting artifacts locally first (deployBuild), or wants a dry-run plan before applying (deployPlan), or when handling multi-environment deploys via mode / envOverrides. Covers build-plan-apply flow (deployBuild local build → deployPlan dry-run → deployApply confirm=true), hosting build-output neutralization, envId resolution priority, only/skip filtering, concurrency, and continueOnError. Prefer deployBuild (when hosting declares a buildCommand) and deployPlan before deployApply; do not confuse with per-resource tcb CLI deploy or single-function deploy.

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Bash 编写规范。当用户操作 .sh、Dockerfile、Makefile、.yml、.yaml 文件， 或在 Markdown 中编写 bash/shell 代码块时触发。 包含注释规范、文件写入方式、Heredoc 引号规则、权限路径、脚本规范等。

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当用户操作 .sh、Dockerfile、Makefile、.yml、.yaml 文件，或在 Markdown 中编写 bash 代码块时触发。提供 Bash 编写规范。

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Reviews and migrates deprecated wildcard (*) values in Power Pages Web API fields site settings to least-privilege explicit Dataverse columns. Use whenever a user mentions Web API wildcard or select-all remediation, fields settings containing *, data-exposure review, wildcard deprecation readiness, or Web API failures after wildcard retirement. Applies to both traditional sites using HTML, CSS, JavaScript, Liquid, and downloaded YAML, and SPA sites using React, Vue, Angular, Astro, or TypeScript. The agent must inspect every source Web API call and consumer, report exact fixes for every wildcard, report every already-explicit configuration, apply approved edits, and verify no wildcard remains.

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Creates or edits a Power Apps Canvas App through the Canvas Authoring MCP coauthoring session. Handles staged experience design, direct and partitioned targeted edits, planned multi-screen changes, responsive layout, and compile-error convergence. Trigger on requests to create, build, generate, modify, update, change, fix, or edit a Canvas App or .pa.yaml files.

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Manage Home Assistant configuration safely and fast — edit and deploy YAML (automations, blueprints, scripts, scenes, templates, MQTT), validate with ha core check, deploy via git or rapid scp, reload-vs-restart correctly, verify changes from logs, traces and entity state, and build Lovelace dashboards. Use for any Home Assistant config, automation, template, or dashboard work over SSH/hass-cli/MCP.

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。