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github/terraform-azurerm-set-diff-analyzer

Analyser la sortie JSON du plan Terraform pour le fournisseur AzureRM afin de distinguer les différences faussement positives (changements d'ordre uniquement dans les attributs de type Set) des modifications réelles de ressources. À utiliser lors de l'examen de la sortie du plan Terraform pour des ressources Azure comme Application Gateway, Load Balancer, Firewall, Front Door, NSG et d'autres ressources avec des attributs de type Set qui provoquent des différences parasites dues à des changements d'ordre internes.

Qu'est-ce que terraform-azurerm-set-diff-analyzer ?

Cet outil (skill) est conçu pour les développeurs et ingénieurs DevOps utilisant Terraform avec le fournisseur AzureRM. Il analyse la sortie JSON d'un `terraform plan` afin d'identifier et de filtrer les différences faussement positives causées par des changements d'ordre dans les attributs de type `Set`. De nombreuses ressources Azure, comme Application Gateway, Load Balancer ou NSG, exposent des attributs `Set` dont l'ordre interne peut varier sans impact fonctionnel, générant des bruits inutiles dans les révisions de plan. Ce skill s'intègre dans des flux de travail d'agents IA (Claude Code, Cursor, etc.) en lisant le JSON du plan, en détectant ces différences parasites et en ne rapportant que les modifications réelles, permettant ainsi aux utilisateurs de gagner du temps et d'éviter des erreurs d'interprétation.

Compatible avec~Claude Code~Codex CLI~Cursor

npx skills add https://github.com/github/awesome-copilot/tree/main/skills/terraform-azurerm-set-diff-analyzer

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Que fait terraform-azurerm-set-diff-analyzer ?

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

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github/add-educational-comments

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github/agentic-eval

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

github/agent-owasp-compliance

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

github/ai-prompt-engineering-safety-review

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.

github/appinsights-instrumentation

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github/apple-appstore-reviewer

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github/architecture-blueprint-generator

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github/arch-linux-triage

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github/arize-ai-provider-integration

Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.

github/arize-annotation

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.

github/arize-dataset

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

github/arize-evaluator

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.

github/arize-experiment

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.

github/arize-instrumentation

Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.

github/arize-link

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.

github/arize-prompt-optimization

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.

github/arize-trace

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.

github/aspire

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