Azure

Rules for analysing a single TIBCO flow group's architecture. Covers source file reading depth, Mermaid diagram rules, flow/sub-process rendering, processor mapping priority ladder, and the required multi-step store tool sequence.

Rules for optional cloud deployment and testing of Logic Apps Standard projects. Covers ARM/Bicep generation, deployment method, app settings management, cloud test execution, and reporting.

Rules for generating connections.json files for Logic Apps Standard. Covers serviceProviderConnections format, mandatory reference lookup, FileSystem mountPath rule, and connector parameter constraints.

Rules for generating ordered conversion task plans. Covers mandatory task ordering, required task types, descriptive task ID rules, optional vs non-optional tasks, output path conventions, and what each task must contain.

Rules for discovering, analysing, and classifying missing dependencies during TIBCO flow analysis. Covers JAR/Java decompilation, Maven dependency analysis, custom module classification, and what blocks migration.

Rules for detecting and grouping TIBCO integration artifacts into logical flow groups using a process-call chain strategy. Covers process-call chain rules, fallback grouping when no sources exist, and required output fields.

Creates .NET local functions (custom code) for Azure Logic Apps Standard. Covers exact NuGet packages, namespaces, csproj, MSBuild targets, VS Code config, function.json, and workflow InvokeFunction patterns for both .NET 8 and .NET Framework 4.7.2.

Rules for planning the migration of a TIBCO flow to Azure Logic Apps Standard. Covers workflow split policy, 1-flow-to-1-workflow rule, source coverage requirements, planning artifact store sequence, and design constraints.

Generate new Azure.Provisioning.* libraries OR regenerate existing ones. Use when introducing a brand-new provisioning library, adding new resource types, enum values, or API versions.

Validates that a proposed trip fits within the user's stated budget

Converts dates between formats and handles timezone differences

Patterns and techniques for mitigating breaking changes during Azure management-plane SDK migration from Swagger/AutoRest to TypeSpec. Covers SDK-side customizations (partial classes, CodeGenType, CodeGenSuppress) and TypeSpec decorator customizations (clientName, access, markAsPageable, alternateType, hierarchyBuilding).

Review Azure SDK management-plane migration PRs (Swagger/AutoRest -> TypeSpec). Checks customization quality, TypeSpec decorator usage, and migration-specific anti-patterns on top of the standard mgmt PR review.

Headless, bounded repair of custom-code build failures in an already-generated Azure SDK PR. Thin wrapper over the shared azure-sdk-mcp:azsdk_customized_code_update engine in custom-code-only scope (editScope: CustomCode); the skill owns the iterate-until-green loop, capped by a per-language `maxIterations` read from repair-config.yml. WHEN: Copilot cloud agent runs on a release-planner Auto SDK PR labeled `auto-sdk-build-fix` that fails to build because of custom (non-generated) code. DO NOT USE FOR: full TypeSpec migrations, spec edits, API design review, manual fixing. INVOKES: azure-sdk-mcp:azsdk_customized_code_update.

Review Azure SDK management-plane pull requests, check naming conventions, API compatibility, and code quality.

Analyze and resolve APIView review feedback on Azure SDK PRs. **UTILITY SKILL**. USE FOR: APIView comments, API review feedback, SDK API surface changes. DO NOT USE FOR: general code review, non-APIView feedback. INVOKES: azure-sdk-mcp:azsdk_apiview_get_comments, azure-sdk-mcp:azsdk_customized_code_update, azure-sdk-mcp:azsdk_typespec_delegate_apiview_feedback, azure-sdk-mcp:azsdk_run_typespec_validation, azure-sdk-mcp:azsdk_package_generate_code.

Create, get, update, abandon, and link SDK PRs to release plan work items for Azure SDK releases. **UTILITY SKILL**. USE FOR: "create release plan", "get release plan", "update release plan", "update API spec in release plan", "update SDK details in release plan", "abandon release plan", "link SDK PR to plan", "namespace approval", "check release plan status". DO NOT USE FOR: SDK code generation, pipeline troubleshooting, API review feedback. INVOKES: azure-sdk-mcp:azsdk_create_release_plan, azure-sdk-mcp:azsdk_get_release_plan, azure-sdk-mcp:azsdk_get_release_plan_for_spec_pr, azure-sdk-mcp:azsdk_update_release_plan, azure-sdk-mcp:azsdk_update_api_spec_pull_request_in_release_plan, azure-sdk-mcp:azsdk_update_sdk_details_in_release_plan, azure-sdk-mcp:azsdk_abandon_release_plan, azure-sdk-mcp:azsdk_link_sdk_pull_request_to_release_plan, azure-sdk-mcp:azsdk_link_namespace_approval_issue.

Authors and modifies Azure TypeSpec (.tsp) API specifications. USE FOR: any TypeSpec/tsp change — api versions (add, bump, preview, stable, promote), resources, operations, models, properties, decorators, visibility, constraints, breaking changes, LRO, suppressions, operationId, spread model. Covers ARM resource-manager and data-plane services. DO NOT USE FOR: SDK generation, releasing SDK packages, or single MCP tool calls. INVOKES: azure-sdk-mcp:azsdk_typespec_generate_authoring_plan, azure-sdk-mcp:azsdk_run_typespec_validation.

Create a new evaluation dataset or add cases to an existing one for the Azure SDK QA bot evaluation. WHEN: "add eval dataset item", "add a test case", "new evaluation dataset", "create dataset", "add question to dataset", "curate eval data", "promote staging cases", "upload dataset asset", "new scenario dataset". DO NOT USE FOR: running evaluations, pipeline troubleshooting, knowledge-graph indexing.

Run Azure SDK QA bot evaluations on curated datasets locally, including a single test case. WHEN: "run evaluation", "run eval", "evaluate the bot", "run perf evaluation", "run basic evaluation", "run a single test case", "evaluate one question", "run all scenarios", "score the bot", "run evals locally". DO NOT USE FOR: preparing or uploading datasets, pipeline troubleshooting, knowledge-graph indexing.

Analyze markdown files for token efficiency and reduce context-window bloat. **UTILITY SKILL**. DO NOT USE FOR: code optimization, general file editing, non-markdown files. TRIGGERS: optimize markdown, reduce tokens, token count, token bloat, too many tokens, make concise, shrink file, file too large, optimize for AI, token efficiency, verbose markdown, reduce file size. INVOKES: waza CLI.

**WORKFLOW SKILL** — Iteratively improve skill frontmatter compliance using the Ralph loop pattern. WHEN: "run sensei", "sensei help", "improve skill", "fix frontmatter", "skill compliance", "frontmatter audit", "score skill", "check skill tokens". INVOKES: token counting tools, test runners, git commands. FOR SINGLE OPERATIONS: use token CLI directly for counts/checks.

Write Agent Skills that comply with the agentskills.io specification. WHEN: "create a skill", "new skill", "write a skill", "skill template", "skill structure", "review skill", "skill PR", "skill compliance", "SKILL.md format", "skill frontmatter", "skill best practices". DO NOT USE FOR: improving existing skills (use sensei), general documentation. INVOKES: waza CLI.

Create, get, update, abandon, and link SDK PRs to release plan work items for Azure SDK releases. **UTILITY SKILL**. USE FOR: "create release plan", "get release plan", "update release plan", "update API spec in release plan", "update SDK details in release plan", "abandon release plan", "link SDK PR to plan", "namespace approval", "check release plan status". DO NOT USE FOR: SDK code generation, pipeline troubleshooting, API review feedback. INVOKES: azure-sdk-mcp:azsdk_create_release_plan, azure-sdk-mcp:azsdk_get_release_plan, azure-sdk-mcp:azsdk_get_release_plan_for_spec_pr, azure-sdk-mcp:azsdk_update_release_plan, azure-sdk-mcp:azsdk_update_api_spec_pull_request_in_release_plan, azure-sdk-mcp:azsdk_update_sdk_details_in_release_plan, azure-sdk-mcp:azsdk_abandon_release_plan, azure-sdk-mcp:azsdk_link_sdk_pull_request_to_release_plan, azure-sdk-mcp:azsdk_link_namespace_approval_issue.

Automate updating the emitter package dependencies in eng/emitter-package.json for the Azure SDK for Python repository, then open a PR. Use this skill when the user wants to update @azure-tools/typespec-python (or any other dependency in emitter-package.json) to the latest version, create a PR for an emitter-package version bump, or manage emitter-package.json updates.

Emit the azure-ai-projects Python SDK from TypeSpec, apply post-emitter fixes, and create a Pull Request. WHEN: "emit SDK from TypeSpec", "generate azure-ai-projects SDK", "update azure-ai-projects from TypeSpec", "emit from TypeSpec", "regenerate azure-ai-projects". DO NOT USE FOR: other Azure SDK packages, manual code edits without TypeSpec. INVOKES: PostEmitter.ps1 script, git commands, gh CLI for PR creation.

Update CHANGELOG.md by comparing public APIs between the current branch and the latest released version on PyPI. WHEN: "update changelog", "generate changelog", "add changelog entry", "what changed in this version". DO NOT USE FOR: other Azure SDK packages. INVOKES: PyPI API, GitHub API (for tags), file operations.

Drafts concise team status updates with progress, plans, and blockers.

Create and test a classify-and-route Azure AI Content Understanding pipeline for packets that contain multiple document types (e.g. invoice + bank statement + loan application in one PDF). Walks per-type schema authoring → outer classifier wiring → batch test → category-aware stdout summary using the typed ContentUnderstandingClient. Use when the user has mixed-document packets.

Iteratively author and test a custom Azure AI Content Understanding analyzer for a folder of **document** files (PDFs, page images) of a single type. Walks layout extraction → schema authoring → validation → batch test → agent review → refine cycle using the typed ContentUnderstandingClient. Document modality only — audio, video, and image analyzers are planned for a later update. Use when the user wants to author a custom analyzer for invoices, contracts, forms, or any other single-type document set.

Domain knowledge for Azure AI Content Understanding. Use this skill to answer questions about Content Understanding concepts, analyzers, field schemas, API operations, and SDK usage. Always consult official documentation before answering.

Run a specific sample for the Azure AI Content Understanding SDK. Use when users want to run a particular sample like sample_analyze_url.py or sample_analyze_invoice.py.

Scaffold a new Azure Functions project, or add a new function/trigger to an existing project without re-initializing it

Scaffold a new Azure Functions project, or add a new function/trigger to an existing project without re-initializing it

Build, scaffold, extend, deploy, and troubleshoot Azure Functions serverless agents and event-driven AI agents using the Azure Functions serverless agents runtime. Use when the user says serverless agent, serverless agents, Azure Functions agent, scheduled agent, morning briefing, daily digest, timer agent, inbox summary, email or Teams briefing, background AI workflow, connector-triggered agent, event-driven AI automation, HTTP/chat agent, webhook-style agent, or Azure Functions hosted agent.

Build, scaffold, extend, deploy, and troubleshoot Azure Functions serverless agents and event-driven AI agents using the Azure Functions serverless agents runtime. Use when the user says serverless agent, serverless agents, Azure Functions agent, scheduled agent, morning briefing, daily digest, timer agent, inbox summary, email or Teams briefing, background AI workflow, connector-triggered agent, event-driven AI automation, HTTP/chat agent, webhook-style agent, or Azure Functions hosted agent.

Use in Agent mode when designing, running, or reporting real end-to-end tests for Azure Functions Skills across GitHub Copilot, Claude Code, and Codex plugin/setup/chat flows.

Verify prerequisites and set up your environment for Azure Functions development

Verify prerequisites and set up your environment for Azure Functions development

Analyze local Azure Functions workspace code and configuration for common issues. Produces structured JSON findings for integration with the doctor CLI command. This skill targets local workspaces only — use azure-functions-diagnostics for deployed Azure resources.

ACR integration for AKS Automatic. Teaches attaching an ACR, image reference conventions (digest pinning, no :latest), and pull-secret-free authentication via the managed identity.

Writing idiomatic, safe, and reviewable Bicep templates for Azure resources.

Non-blocking cluster status peek — run at the end of Phases 3, 4, 5 to check AKS provisioning progress without hanging.

Voice, tone, and interaction patterns for Kickstart agents.

Configure Infrastructure phase playbook — launch the dedicated Kickstart cluster-setup view, which collects and creates the Azure resources (subscription, resource group, AKS Automatic cluster, ACR) and hands the results back to the chat.

Deploy phase playbook — build, push, apply with Azure CLI and kubectl.

Design phase playbook — propose target architecture on AKS Automatic.

Discovery phase playbook — collect application details.

Run azure-cosmos integration/customer-workflow tests locally, closely following the CI pipeline (build+install, then failsafe `verify` with a test profile) using one consistent JDK for both steps. USE WHEN: asked to run cosmos integration tests, customer-workflow tests (fi-customer-workflows / fi-sm-customer-workflows), reproduce a CI test failure locally, or run a specific cosmos test profile via Maven. Covers the same-JDK build/test requirement, the two-step build/test split, profile→group→file mapping, and the required account env vars. NOT FOR: unit tests only, Spark/Kafka connector tests, or non-cosmos modules.

Interactive wizard that walks service teams through creating a package-specific skill for their Azure SDK package. Scans the package, detects customization patterns, scaffolds a SKILL.md with references, and validates with vally lint. The skill is placed inside the package's .github/skills/ directory so find-package-skill discovers it automatically. WHEN: create package skill; add service skill; bootstrap skill for package; new package skill; skill for my SDK package; write skill for search; write skill for cosmos.

**UTILITY SKILL** — Must be consulted before making any changes to packages under sdk/. Discovers and loads package-specific domain knowledge that prevents common mistakes. WHEN: add feature to package; fix bug in package; modify package code; regenerate from typespec; change client library.

Domain knowledge for Azure AI Content Understanding. Use this skill to answer questions about Content Understanding concepts, analyzers, field schemas, API operations, and Java SDK usage. Always consult official documentation before answering.

Run a specific sample for the Azure AI Content Understanding Java SDK. Use when users want to run a particular sample like Sample02_AnalyzeUrl or Sample03_AnalyzeInvoice.

Guide SDK users through setting up their Java environment for Azure AI Content Understanding. Use this skill when users need help installing the SDK, configuring Azure resources, deploying required models, setting environment variables, or running samples.

Generate code from TypeSpec via tsp-client (update, sync, generate). Requires a tsp-location.yaml in the current working directory. Supports updating the commit hash before running.

Suppress generated Java classes that duplicate openai-java models, using @@alternateType in TypeSpec and manual serialization bridges. Use after dup-classes has identified actionable duplicates.

Guide users through installing and configuring the DocumentDB MCP server for Azure DocumentDB. Use this skill when a user wants to wire the DocumentDB MCP server into an agentic client (Claude Code, Claude Desktop, Cursor, Copilot CLI, Gemini CLI, VS Code) and define a `CONNECTION_PROFILES` entry, or when they hit MCP connection / auth / profile errors.

Optimize MongoDB client connection configuration (pools, timeouts, patterns) for Azure DocumentDB. Use this skill when working on functions that instantiate or configure a MongoDB client (e.g., calling `connect()`), configuring connection pools, troubleshooting connection errors (ECONNREFUSED, timeouts, pool exhaustion), optimizing connection-related performance issues. Includes scenarios like building serverless functions, creating API endpoints, optimizing high-traffic applications, or debugging connection failures.

Full-text search best practices for Azure DocumentDB using the `createSearchIndexes` command and `$search` aggregation stage — BM25 keyword scoring, fuzzy search (`maxEdits`), phrase search with `slop`, custom analyzers (keyword + lowerCase + asciiFolding + edgeGram) for prefix / ID matching, `pathHierarchy` tokenizer for hierarchical IDs, multi-field search indexes, and hybrid (BM25 + vector) retrieval via Reciprocal Rank Fusion. Use when building search experiences, adding typo tolerance, matching phrases or prefixes on IDs / SKUs / part numbers, or combining lexical and semantic retrieval on the same collection.

Vector search best practices for Azure DocumentDB using `cosmosSearch` — choosing between DiskANN / HNSW / IVF, creating indexes, tuning `lBuild` / `lSearch` / `maxDegree`, Product Quantization (up to 16,000 dims), half-precision (fp16) indexing, and normalizing embeddings for cosine similarity. Use when building RAG / semantic-search applications, creating a vector index, tuning recall/latency, or reducing vector-index memory footprint.

Index-type selection and shape guidance for Azure DocumentDB — when to use single-field, compound (ESR), multikey, wildcard, hashed, 2dsphere, TTL, and vector indexes; query-pattern → index-shape cookbook; per-collection index budget; DocumentDB-specific preference for `textSearch` over community `$text`. Use when designing or reviewing indexes, choosing an index type for a query pattern, or deciding whether an additional index is worth the write cost.

Generate read-only DocumentDB/MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate queries for Azure DocumentDB, wants to filter/query/aggregate data, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT analyze or optimize existing queries — use documentdb-query-optimizer for that. Requires DocumentDB MCP server.

Horizontal sharding (partitioning) for Azure DocumentDB collections — when to shard vs stay single-shard, how to pick a shard key for read-heavy vs write-heavy workloads, the logical/physical shard mental model, scaling out vs scaling up, hot-partition diagnosis, and the `sh.shardCollection` / `sh.reshardCollection` commands. Use when deciding whether to shard a collection, choosing or changing a shard key, sizing a cluster, or troubleshooting uneven storage / throughput across physical shards.

Storage configuration guidance for Azure DocumentDB — when and how to use Premium SSD v2 high-performance storage, IOPS/bandwidth caps that are gated by compute tier (not disk size), Premium SSD v2 limitations (no CMK, migration paths, disk-hydration sequencing), and storage capacity change limits. Use when picking a storage type at cluster creation, sizing for I/O-intensive workloads, migrating from Premium SSD to Premium SSD v2, or sequencing compute/storage/HA changes on a Premium SSD v2 cluster.

Route gh-aw workflow design/create/debug/upgrade requests to the right prompts.

Re-record the ai-projects live test suite, push only the recordings whose playback passes to the Azure/azure-sdk-assets repo, and open a PR for the updated assets.json. Use when test recordings are stale, a recorded test fails in playback in CI, or new recorded tests need fresh recordings. Wraps TEST_MODE=record / TEST_MODE=playback runs, dev-tool test-proxy push, and a single-file assets.json PR via the gh CLI.

Migrate Azure/azure-sdk-for-js to a user-specified minimum Node.js version while preserving any existing references above the target.

**UTILITY SKILL** — Must be consulted for SDK development workflow tasks. Routes agents to the correct tools and documentation for building, testing, linting, formatting, provisioning, deploying, and other package development tasks. WHEN: "build package", "run tests", "lint code", "format code", "run checks", "prepare samples", "publish samples", "apply customization", "run migrations", "bump version", "extract API", "update snippets", "check API compatibility", "manage test recordings", "provision test resources", "deploy test resources", "create test resources", "set up test environment", "authenticate to Azure", "login to Azure", "release package", "increment version", "troubleshoot CI", "fix CI failure", "CredScan", "credential scan", "push recordings", "asset sync", "update recordings".

Domain knowledge for Azure AI Content Understanding. Use this skill to answer questions about Content Understanding concepts, analyzers, field schemas, API operations, and JavaScript SDK usage. Always consult official documentation before answering.

Run a specific sample for the Azure AI Content Understanding JavaScript SDK. Use when users want to run a particular sample like analyzeUrl.js or analyzeInvoice.js.

Guide SDK users through setting up their JavaScript / Node.js environment for Azure AI Content Understanding. Use this skill when users need help installing the SDK, configuring Azure resources, deploying required models, setting environment variables, or running samples.

Interactive wizard that walks service teams through creating a package-specific skill for their Azure SDK package. Scans the package, detects customization patterns, scaffolds a SKILL.md with references, and validates with vally lint. The skill is placed inside the package's .github/skills/ directory so find-package-skill discovers it automatically. WHEN: create package skill; add service skill; bootstrap skill for package; new package skill; skill for my SDK package; write skill for search; write skill for cosmos.

Bootstrap a GitHub repository for Git-Ape CI/CD: Entra app registration, OIDC federated credentials, RBAC role assignments, GitHub environments (azure-deploy/azure-destroy), required secrets, and scaffold Actions workflow files — plus enterprise-wide distribution via a `.github-private` repo (managed-settings.json plugin standards + custom agents). USE FOR: first-time Git-Ape setup, new subscription onboarding, multi-environment (dev/staging/prod) setup, configure OIDC, federated credentials, RBAC setup, GitHub environments, scaffold workflow files, rolling Git-Ape out org/enterprise-wide. DO NOT USE FOR: deploying resources (use git-ape), drift detection alone, secret rotation.

Detect configuration drift between deployed Azure resources and stored deployment state. Compare actual Azure configuration against desired state in .azure/deployments/, identify differences, and guide user through reconciliation options. Use when checking for manual changes, policy remediations, or unauthorized modifications.

Run an Azure Deployment Stack create (subscription scope) for a prepared Git-Ape deployment artifact and write state.json (schemaVersion 1.0). Use locally so the result matches the CI deploy workflow.

Validate Git-Ape CLI tool installation (az, gh, jq, git), versions, and auth sessions. Shows platform-specific install commands for anything missing. USE FOR: check Git-Ape prerequisites, what do I need to install for Git-Ape, verify Git-Ape CLI tools, az: command not found, gh: command not found, jq: command not found, git: command not found, az missing, gh missing, jq missing, git missing, fresh machine setup for Git-Ape, dev container setup for Git-Ape, before running git-ape-onboarding, az login required, gh auth login, auth expired, not logged in, outdated az version, minimum az version, upgrade az. DO NOT USE FOR: Anything else. This skill is narrowly scoped to prerequisites checks for Git-Ape's CLI tools and auth sessions. Do not use it for any other purpose.

Assess ARM template resources for Azure Policy compliance. Analyse the template, query existing subscription assignments via `az policy assignment list`, identify unassigned built-in and custom policies (CIS, NIST, FedRAMP), and emit a two-part report: template-fixable gaps (Part 1) and subscription-level policy assignments (Part 2). USE FOR: recommending Azure Policy assignments for an ARM template, auditing a subscription against CIS/NIST/general best practices, deciding which initiatives to assign at sub or management-group scope, distinguishing template-fixable vs platform-level governance gaps. DO NOT USE FOR: per-resource security configuration assessment (use azure-security-analyzer), RBAC role recommendations (use azure-role-selector), CAF naming abbreviations (use azure-naming-research), or pricing estimates (use azure-cost-estimator). INVOKES: az policy assignment list, az policy set-definition list, microsoft_docs_search, microsoft_docs_fetch.

Estimate monthly costs for Azure resources by querying the Azure Retail Prices API. Parses ARM templates to identify resources, SKUs, and regions, then looks up real retail pricing. Produces a per-resource cost breakdown with monthly totals. Use during template generation or when user asks about costs.

Run preflight validation on ARM templates before deployment. Performs what-if analysis, permission checks, and generates a structured report with resource changes (create/modify/delete). Use before any deployment to preview changes and catch issues early.

Run post-deployment integration tests for Azure resources. Verify Function Apps, Storage Accounts, Databases, App Services are healthy and accessible. Use after successful Azure deployment.

Orchestrates the creation and validation of a new ASIM schema parser. Use this skill when asked to create a new ASIM parser.

Validates ASIM parser filtering parameters by running filter tests against a Log Analytics workspace. Pure PowerShell using az CLI for authentication — no Python or Azure SDK packages required. Use this skill after creating or modifying an ASIM parser to verify that its filtering parameters work correctly.

Reviews pull requests for ASIM parser changes and summarizes suggestions. This is not to be called by asim-parser-creator-orchestrator or when creating ASIM parsers in general. This is strictly for reviewing pull requests after necessary workflows have ran.

Run the Solutions Analyzer scripts: mapper, documentation generator, ASIM browser. Use when: running solution analyzer, generating CSVs, generating docs, refreshing caches, force-refresh, invalidating caches, generating ASIM browser, running mapper, running collect_table_info, running collect_asim_fields.

MANDATORY guidelines for ANY edit to ANY file under Tools/Solutions Analyzer/ — including the mapper (map_solutions_connectors_tables.py), doc generator (generate_connector_docs.py), interactive docs (generate_interactive_docs.py), ASIM browser, collect_table_info, collect_asim_fields, upload_to_kusto, compare_runs, solution_analyzer_overrides.csv, filter_field_resolution.yaml, or any other script/config in that folder. Use when: editing/modifying/refactoring/fixing/renaming/tweaking ANY logic in those files, even one-line fixes such as renaming a classification, escaping a character, adjusting a regex, adding an override row, suppressing a false positive, or changing a constant. Covers: keeping script-docs in sync, README Version History changelog rules (required for feature/behavior changes; optional for small bug fixes), CSV output sync with upload_to_kusto.py, static/interactive index synchronization, and markdown/HTML entity page synchronization.

This creates the parameterized version of the ASIM schema parser. You should already have the parameter-less version of the parser to help facilitate the parameter parser creation.

This starts the process of creating a new ASIM schema parser by generating the initial version of the parser based on the requirements gathered. Use this skill when you have gathered all necessary information for the new ASIM parser and are ready to create the initial version of the parser.

Packages the created and validated ASIM schema parser into a GitHub PR for the Azure-Sentinel repository. Use this skill when asked to package a parser into a GitHub PR.

Pair with a kars cluster and offload heavy tasks to governed cloud sandboxes with GPU / foundation-model inference / Azure AI services, or communicate with other agents over end-to-end encrypted AgentMesh. Triggers on natural-language intents like "offload to the cloud", "run this on Azure", "ask my cluster to…", "send a message to agent X", "who is on the mesh", "check my inbox", "is my offload done".

Behavioral governance for OpenClaw agents via AGT — tool-level policy, inter-agent trust, audit logging.

Spawn secure isolated sub-agent sandboxes, delegate tasks via AGT mesh, receive results, and destroy sub-agents. Uses the kars_spawn, kars_mesh_send, kars_mesh_inbox, and kars_spawn_destroy tools.

Query and inspect Foundry prompt agents and invoke Foundry tools via the Responses API. OpenClaw is the orchestrator — Foundry provides managed AI services.

Python code execution via Azure AI Foundry Responses API with code_interpreter tool. Data analysis, charts, and math in a managed sandbox.

Manage persistent conversations via Foundry Conversations API. Create conversations, add messages, and maintain history across sessions.

Query model deployments, connections, and indexes in the Foundry project. Discover available models and infrastructure.

Evaluate agent quality using Foundry OpenAI Evals API. Create evaluations, run them against models, and analyze results.