Azuresql Db Feedback

作者 microsoftc1e8167e4c1d无许可证收录于 2026年10月8日更新于 2026年10月8日

Reports a bug or files feedback about the azuresql-db-* agent skills themselves, or about the Azure SQL Database container (Private Preview). Use when the user says a skill or the container "did not work", hit an error, behaved unexpectedly, or is missing something; and when they say "report a bug", "file an issue", "open a GitHub issue", "request a feature", "give feedback", or "tell the team". Also use when you, the agent, had to deviate from an azuresql-db-* skill or work around a defect in one to finish a task: that is a bug worth reporting even if the task succeeded. Decides whether the problem belongs to the SKILL or to the CONTAINER, since they use different issue templates, then builds a complete prefilled GitHub issue from context you already have. Never submits anything without explicit confirmation from the user.

精选仅含说明Productivity & Workflow
AI 生成的概览

将技能或容器问题整理为完整、已脱敏的 GitHub 议题草稿,供用户确认后提交。

功能
该技能引导代理判断问题属于 azuresql-db-* 技能还是 Azure SQL Database 容器,然后收集相关事实,例如技能名称、代理运行环境、镜像标签、主机操作系统和复现命令。它会对 SA 密码、注册表凭据和连接字符串等机密信息进行脱敏,起草预填的 GitHub 议题,并在提交前完整展示给用户。提交会优先尝试 gh CLI,其次使用预填的 github.com 链接,且未经用户明确确认不会提交任何内容。
适用场景
当用户反馈某个 azuresql-db-* 技能或 Azure SQL Database 容器无法工作、报错、行为异常或缺少功能时,或当用户要求报告缺陷、提交议题或提供反馈时使用。当代理为完成任务不得不偏离技能说明或绕过某个缺陷时,也适用。
运行要求
仅为说明文档,不附带脚本。第一层提交方式需要 gh CLI 且 gh auth status 成功;第二层预填 github.com 链接方式无需任何凭据。构建任何链接前需读取 references/issue-fields.md,可选使用 Microsoft Learn MCP 服务器获取最新文档。

Report a problem with the skills, or with the container

The user should never have to assemble a bug report by hand. You are already holding what a good one needs: which skill you were following, what it told you to do, what you actually had to do, the image tag, the host, the runtime, the failing command, and the error. Your job is to turn that into a complete report and hand it to the user to submit.

Repository: microsoft/azure-sql-database-container (both the container and the skills live here).

The claims in this skill are about issue forms in microsoft/azure-sql-database-container, not about the engine, so no engine measurement applies to them. Verified on 2026-09-05: aka.ms/sql-agent-skills-feedback still answers 301 to https://github.com/microsoft/azure-sql-database-container/issues/new?template=skill_feedback.yml with the query string dropped, which is exactly why a prefilled report has to use the full github.com URL. The field ids, labels and dropdown values are read from the issue form definitions in this repository and mirrored in references/issue-fields.md [blocked], which you open before you build any URL.

The two rules that matter most

  1. Never create an issue without explicit confirmation. Show the user the full title and body first and wait for a clear yes. You may offer; you may never submit unasked. This applies to every path below, including the gh CLI.
  2. Redact before you send. Everything submitted is public. See Redaction.

Step 1: skill problem, or container problem?

Get this right first: they use different issue templates.

Ask yourself what actually failed.

The problemIt belongs toTemplate
A skill told the agent to do the wrong thing, or failed to say something it needed to saythe skillskill_feedback.yml
The wrong skill loaded, or no skill loaded when one should havethe skillskill_feedback.yml
A skill would not install, or the agent never picked it upthe skillskill_feedback.yml
You had to deviate from the skill or work around it to make the task succeedthe skillskill_feedback.yml
The skill said the right thing, the agent did the right thing, and the container still failed (will not start, query errors, engine behaves unexpectedly)the containerbug_report.yml
The container is missing a capability the user wantsthe containerfeature_request.yml

The test that settles it: if the instructions were correct and the engine still broke, it is a container bug. If the instructions were wrong, incomplete, or ignored, it is a skill bug. When genuinely torn, ask the user. Do not guess, and do not file both.

If the user is only reporting that something worked well, do not open an issue. Point them at Discussions and stop.

Step 2: gather the facts

Only run a command if you do not already know the answer.

For a skill problem (the important ones, and the ones we are otherwise blind to):

  • Which skill, by name (azuresql-db-rag, etc). If none loaded, say so: that is itself the bug.
  • Which agent you are (Claude Code, GitHub Copilot, Codex, Cursor). Skills behave differently across harnesses and we cannot see which one the user ran.
  • The instruction that was wrong or missing, quoted from the skill, and what actually worked instead. This is the single most valuable field in the whole report. Without it we cannot fix the skill.
  • The prompt the user gave you.

For a container problem: the image tag, the host OS (mapped to the exact dropdown value), the container runtime and version, what happened, and the commands that reproduce it. docker logs sqldb if the container is what broke.

Step 3: redaction (do this before you build anything)

Strip all of the following from every field, including logs and repro steps:

  • The SA password (MSSQL_SA_PASSWORD, -P <password>, the Password= field of a connection string). Replace with ***.
  • Registry credentials (the docker login username and password).
  • Full connection strings. Keep the shape, drop the secret: Server=localhost,1433;Database=appdb;User Id=sa;Password=***;TrustServerCertificate=true.
  • Any customer or application data, table contents, embeddings, or paths that identify the user or their employer.
  • Access tokens of any kind.

The repository is public and issues are world readable. A report that leaks the user's password is worse than no report at all.

Step 4: draft and show

Write the report, then show it to the user in full and ask whether to file it. Say which fields you filled and which you left blank. If you could not determine a value confidently, leave it blank rather than guess: a wrong skill name or host OS sends triage down the wrong path.

Step 5: submit

Try these in order, stop at the first that works, and confirm with the user first either way.

Tier 1: the gh CLI, if gh auth status succeeds. The issue is authored by the user, so they get replies.

bash
gh issue create --repo microsoft/azure-sql-database-container \  --title "[Skill]: <one-line summary>" \  --label skills --label needs-triage --label User-filled --label via-skill \  --body-file <path-to-body>

Write the body to a file rather than passing it inline, so quoting and newlines survive.

Tier 2: a prefilled URL. No credentials, no setup, and the user sees exactly what will be submitted before anything happens. Use this whenever gh is not available; it always works.

Build the URL from the issue form's field ids and print it for the user to open. The field ids, the verbatim dropdown values, and worked examples for all three templates are in references/issue-fields.md [blocked]. Read that file before constructing a URL.

Validation rules

  • The user saw the exact title, body and labels before anything was submitted, and said yes.
  • The report went to the right template: a skill problem on the skills form, a container problem on the container form.
  • No SA password, registry credential or access token appears in any field. Re-read the body once more before submitting.
  • Every dropdown value is verbatim from the field reference, which you open before building any URL, or the field was left out.
  • A prefilled report uses a full github.com URL, never an aka.ms link, because those drop the query string.
  • Tier 1 succeeded when gh issue create printed the new issue URL; tier 2 succeeded when the user opened the URL and the form came up with the fields already filled in. If neither happened, the report was not filed: say so rather than implying it was.

Do not

  • Do not create, comment on, or close an issue without explicit user confirmation.
  • Do not include the SA password, registry credentials, or access tokens in any field. Ever.
  • Do not file a skill problem on the container template, or the reverse. They are triaged by different people.
  • Do not guess a dropdown value. Leave the field out if you are unsure; an omitted dropdown renders unselected, but a wrong one misroutes triage.
  • Do not use an aka.ms short link to carry a prefilled report. Those links drop query strings, so every field you filled in is silently lost. Use the full github.com URL. (The aka.ms links are for humans opening an empty form: skills feedback, container bug, container feature.)
  • Do not open an issue for something already on the Known limitations page, or already in open issues. Point the user there instead.
  • Do not nag. Offer once, take no for an answer, and move on.

References

  • references/issue-fields.md [blocked]: the exact field ids for all three templates, the verbatim dropdown values, URL construction and its length limit, and worked examples. Read this before building a prefilled URL.

Staying current

Authoritative, version-pinned references for the tools this skill uses (read the one you need):

If the Microsoft Learn MCP server is configured, use mcp__microsoft-learn__microsoft_docs_search or mcp__microsoft-learn__microsoft_docs_fetch to fetch the current version of any of these on demand. It is optional; when it is unavailable, the references above are authoritative.

来源与署名

来源:microsoft/azure-sql-database-container位于skills/azuresql-db-feedback提交c1e8167

许可证: 无许可证

内容归原作者所有。SourceWeft 从公开仓库中收录这些内容。

举报或申请下架

更多来自 microsoft/azure-sql-database-container 的技能

Azuresql Db Sidecar

microsoft

精选

将 Azure SQL 数据库容器作为边车服务添加到 Docker Compose 或 Dev Container 中。

DevOps & Cloud2026年10月8日

Azuresql Db Rag

microsoft

精选

Builds local vector search, RAG, embeddings, and semantic search on the Azure SQL Database container using the native VECTOR type and VECTOR_DISTANCE. Use when you need to store embeddings, do similarity search, top-k nearest neighbor, cosine distance, retrieval-augmented generation, "find similar documents", chatbot memory, or semantic lookup against a local SQL database. Use this instead of pgvector, FAISS, Chroma, Pinecone, or a separate vector store when the data already lives in (or can live in) Azure SQL. Covers the VECTOR(n) column type, inserting embeddings with CAST(CAST(? AS NVARCHAR(MAX)) AS VECTOR(n)) where the dimension is a literal, a pluggable embed() so only the endpoint changes for cloud, and a working CREATE VECTOR INDEX with the two errors that block it. Provisions appdb on master first so every script runs on a fresh container.

待分类2026年10月8日

Azuresql Db Import

microsoft

精选

使用 SqlPackage 将 .bacpac 或 .dacpac 导入本地 Azure SQL Database 容器。

DevOps & Cloud2026年10月8日

Azuresql Db From Sql Server

microsoft

精选

将本地 SQL Server Docker 环境迁移到 Azure SQL Database 容器,以实现与 Azure 一致的本地开发。

DevOps & Cloud2026年10月8日

Azuresql Db Faq

microsoft

精选

解答 Azure SQL 数据库容器(个人预览版)支持哪些功能,以及为何与云端服务不同。

Learning & Education2026年10月8日

Azuresql Db Ci

microsoft

精选

Runs integration tests against the Azure SQL Database container (Private Preview, local engine) in CI. Use when setting up GitHub Actions, Azure Pipelines, or GitLab CI to test against Azure SQL DB; when adding a database service container to a CI workflow; when tests need a real Azure SQL engine in the pipeline; or when you see "service container", "health-cmd", "ACR_USERNAME/ACR_PASSWORD", "MSSQL_SA_PASSWORD secret", or "integration test database". Also use when a workflow was about to pull the SQL Server image mcr.microsoft.com/mssql/server, in which case stop and use the Azure SQL Database engine image instead. Covers pulling from the private ACR with credentials, the service health check that runs sqlcmd inside the container so the runner needs no client tools, provisioning appdb before tests, and pointing the test connection string at the user database not master.

待分类2026年10月8日