Skill

create-azure-agent

Deploy a skill as an Azure AI Foundry hosted agent

Install

Run in your terminal

npx claudepluginhub richfrem/agent-plugins-skills --plugin agent-scaffolders

Tool Access

This skill is limited to using the following tools:

BashWriteRead

Supporting Assets

View in Repository

acceptance-criteria.md

evals/evals.json

evals/results.tsv

fallback-tree.md

references/acceptance-criteria.md

references/fallback-tree.md

references/hitl-interaction-design.md

references/pattern-decision-matrix.md

references/patterns/action-forcing-output-with-deadline-attribution.md

references/patterns/adversarial-objectivity-constraint.md

references/patterns/anti-pattern-vaccination.md

references/patterns/anti-symptom-triage.md

references/patterns/artifact-embedded-execution-audit-trail.md

references/patterns/artifact-generation-xss-compliance-gate.md

references/patterns/artifact-lifecycle.md

references/patterns/artifact-state-interrogative-routing.md

references/patterns/asynchronous-benchmark-metric-capture.md

references/patterns/audience-segmented-information-filtering.md

references/patterns/category-calibrated-benchmark-anchoring.md

references/patterns/category-semantic-deferred-tool-binding.md

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Stats

Parent Repo Stars1

Parent Repo Forks1

Last CommitMar 28, 2026

Actions

View Source View Plugin View on GitHub View README

Steps

If $ARGUMENTS provides a skill directory, resolve and validate the path

Follow the create-azure-agent phased workflow: confirm the target skill, gather Azure configuration (subscription, resource group, region, naming preferences), then run scaffold_azure_agent.py to generate Bicep templates and the Python Azure AI Projects SDK deployment wrapper

Summarize generated files in the skill's azure_deployment/ directory

Instruct on reviewing .bicep parameters and running az deployment group create

Edge Cases

If $ARGUMENTS is empty: ask for the target skill directory before proceeding

If Azure credentials are not configured: instruct user to run az login first

Azure AI Foundry enforces a 128-tool limit — scaffold generates a focused worker agent

Offer to run /agent-scaffolders:audit-plugin to validate the skill before deploying

Follow the create-azure-agent skill workflow to generate Azure AI Foundry deployment wrappers for an existing agent skill.

Inputs

$ARGUMENTS — optional path to the skill directory to deploy. Omit to start with discovery.

Steps

If $ARGUMENTS provides a skill directory, resolve and validate the path
Follow the create-azure-agent phased workflow: confirm the target skill, gather Azure configuration (subscription, resource group, region, naming preferences), then run scaffold_azure_agent.py to generate Bicep templates and the Python Azure AI Projects SDK deployment wrapper
Summarize generated files in the skill's azure_deployment/ directory
Instruct on reviewing .bicep parameters and running az deployment group create

Output

azure_deployment/azure_agent.py (Azure AI Projects SDK orchestration script) and azure_deployment/main.bicep (Cosmos DB, AI Search, and Foundry Project infrastructure).

Edge Cases

If $ARGUMENTS is empty: ask for the target skill directory before proceeding
If Azure credentials are not configured: instruct user to run az login first
Azure AI Foundry enforces a 128-tool limit — scaffold generates a focused worker agent
Offer to run /agent-scaffolders:audit-plugin to validate the skill before deploying