「copilot」の検索結果

Close defects and incidents with a BUG RECEIPT and VERIFIED, PARTIAL, or BLOCKED status after diagnosis, repair, or recovery.

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 unfamiliar code, mapping architecture, finding symbols or relationships, tracing callers, callees, data flow or dependencies, assessing impact, auditing dead or complex code, or handling explicit Codebase Memory requests. Otherwise skip tasks confined to a supplied known file, tiny one-file check, exact literal, configuration value, error string, or non-code text.

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。

Cloud design patterns for distributed systems architecture covering 42 industry-standard patterns across reliability, performance, messaging, security, and deployment categories. Use when designing, reviewing, or implementing distributed system architectures.

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-level browser automation, debugging, and performance analysis using Chrome DevTools MCP. Use for interacting with web pages, capturing screenshots, analyzing network traffic, and profiling performance.

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。

Triage and resolve CentOS issues using RHEL-compatible tooling, SELinux-aware practices, and firewalld.

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。

Containerize an ASP.NET Core project by creating Dockerfile and .dockerfile files customized for the project.

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。

Issue Planning and Automation prompt that generates comprehensive project plans with Epic > Feature > Story/Enabler > Test hierarchy, dependencies, priorities, and automated tracking.

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When user is asking for guidance for which role to assign to an identity given desired permissions, this agent helps them understand the role that will meet the requirements with least privilege access and how to apply that role.

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 a map of all files relevant to a task before making 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。

Read artifacts from the shared bench — the workspace where desks leave findings, verdicts, and work products for each other and the operator.

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。

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

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。

Cross-references C# Web API controllers/DTOs against their TypeScript/JavaScript consumers (React, Angular, Vue, Svelte, Node.js, or hand-written/auto-generated HTTP clients like Fetch, Axios, NSwag) to catch contract drift in both directions: backend changes that break client applications (renamed/removed JSON keys, new required parameters, status code shifts) and frontend code sending fields the backend no longer reads. Works directly against source code, not exported OpenAPI spec files. Use when the user asks to check for breaking API changes, verify frontend/backend contract sync, or audit a DTO/controller change against its TypeScript/JS consumers before merging. Not for generating new API code from a spec (see openapi-to-application-code) or scaffolding new endpoints (see aspnet-minimal-api-openapi).

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。

Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.

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 Azure resources used in the app (IaC files and/or resources in a target rg) and optimize costs - creating GitHub issues for identified optimizations.

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Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.

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。

Verify supply chain integrity for AI agent plugins, tools, and dependencies. Use this skill when: - Generating SHA-256 integrity manifests for agent plugins or tool packages - Verifying that installed plugins match their published manifests - Detecting tampered, modified, or untracked files in agent tool directories - Auditing dependency pinning and version policies for agent components - Building provenance chains for agent plugin promotion (dev → staging → production) - Any request like "verify plugin integrity", "generate manifest", "check supply chain", or "sign this plugin"

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。

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.

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Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.

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。

Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.

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 an AWS Well-Architected Framework review of the current workload IaC and architecture, generating findings and GitHub issues for improvements.

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。

Reusable investigation patterns for AWS CloudWatch: Logs Insights query templates, alarm-to-deployment correlation, blast-radius narrowing decision tree, and PromQL-style metric query patterns for structured incident triage.

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。

Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.

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。

Write, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.

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。

Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.

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。