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BB azure-openai-to-responses

Migrate Python apps from Azure OpenAI Chat Completions to the Responses API. Covers AzureOpenAI/AsyncAzureOpenAI client migration to the v1 endpoint, streaming, tools, structured output, multi-turn, EntraID auth, and model compatibility checks. Python-focused, Azure OpenAI-specific. USE FOR: migrate to responses API, switch from chat completions, openai responses, upgrade openai SDK, responses API migration, move from completions to responses, gpt-5 migration, azure openai python migration, chat completions to responses, AzureOpenAI to OpenAI client, python azure openai upgrade. DO NOT USE FOR: building new apps from scratch (start with responses directly), Node/TypeScript/C#/Java/Go migrations (this skill is Python-only), Azure infrastructure setup (use azure-prepare), deploying models (use microsoft-foundry).

The skillemall take

This skill guides migrating Python apps from Azure OpenAI Chat Completions to the Responses API. Promises coverage of AzureOpenAI/AsyncAzureOpenAI client migration, streaming, tools, structured output, multi-turn conversations, EntraID auth, and model compatibility checks.

Five modules and one script in the repo. Quality score 83, process score 67. No critical errors, links intact. Grade B means the instructions work but have gaps—likely edge cases or examples need refinement. Not tested on models.

Use it if you're migrating an existing Chat Completions project and can adapt to your codebase. For greenfield projects, start with Responses directly.

microsoft/ai-agents-for-beginners Agent Skills author: microsoft MIT 5 files · 1 script body ≈ 5 738 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Migrate Python apps from Azure OpenAI Chat Completions to the Responses API.

As a process B 67/100 · Nearly there — weak spots: result and completion

IntegrationAzureGitHubInfrastructureAI and agentsSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
B
67/100
Nearly there
Result and completion w 14
40
When it triggers w 12
50
Failures and branches w 10
55
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: ai-agents-for-beginners

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5738 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 120, 154, 172, 194, 232, 234): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 67/100

  • 40Result and completion. Does not say what the result is
  • 50When it triggers. No condition that starts the skill
  • 55Failures and branches. 1 branches
  • 60Tools and files. Uses tools (bash, web, git, python) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 5738 tokens
  • 85Steps. 92 steps, 3 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 13 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +3Description length 822: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +4Structure: 36 headings
  • +3Step-by-step instructions: 92 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.