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Microsoft AzureKnown publisherAzure/gpt-rag-mcp/.github/skills/documentation-consistency/SKILL.md

documentation-consistency

Skill22 starsChanged 3 months agoMITView on GitHub

Keeps GPT-RAG MCP service and umbrella documentation aligned with shipped behavior. Use for capabilities, configuration keys, deployment steps, endpoints, defaults, operations, or breaking changes.

We split this skill into the rules it gives an agent

5 rules. 0 are common, 3 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 10 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Search this repository for the capability, configuration key, endpoint, parameter, and previous terminology.

    line 9
  2. Very specific:

    Update service setup and Inspector guidance in README.md and operational diagnosis in TROUBLESHOOTING.md.

    line 11
  3. Very specific:

    If umbrella GPT-RAG deployment, configuration, or user experience changes, search and update the docs branch of Azure/GPT-RAG in the same …

    line 13
  4. Distinctive:

    Keep repository READMEs focused.

    line 16
  5. Distinctive:

    Verify examples against executable defaults and current protocol contracts.

    line 18

“Common” is measured: kept by at least 6 in 10 of the 10 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

GPT-RAG MCP documentation consistency

  1. Identify the user, orchestrator, or operator behavior that changed.
  2. Search this repository for the capability, configuration key, endpoint, parameter, and previous terminology.
  3. Update service setup and Inspector guidance in README.md and operational diagnosis in TROUBLESHOOTING.md.
  4. If umbrella GPT-RAG deployment, configuration, or user experience changes, search and update the docs branch of Azure/GPT-RAG in the same coordinated change.
  5. Keep repository READMEs focused; link to published GPT-RAG documentation instead of duplicating broad product guidance.
  6. Verify examples against executable defaults and current protocol contracts.
  7. Report every documentation file, branch, or pull request in the implementation handoff.

A visible change is incomplete until documentation is updated or the search demonstrates that no documentation is affected.

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  • Other versions10 projects use a version of this.

    Word for word · 0

    None found.

    Edited · 4

    1. Azure/agent-landing-zone★ 1.2k16% same
    2. Azure/GPT-RAG★ 1.2k16% same
    3. Azure/agent-app-orchestrator★ 10511% same
    4. Azure/gpt-rag-orchestrator★ 10511% same

    Wrote their own version · 6

    1. Azure/agent-app-ingestion★ 188same name
    2. Azure/gpt-rag-ingestion★ 188same name
    3. wallstop/unity-helpers★ 176same name
    4. Azure/bicep-ptn-aiml-landing-zone★ 19same name
    5. Azure/agent-app-ui★ 10same name
    6. Azure/gpt-rag-ui★ 10same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • Used alongsideProjects with it also use deployment-operations, iac-validation and 10 more.

    Other files people use alongside this one.

    Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
    • Microsoft AzureSkill

      deployment-operations

      Safely investigates and operates AI Landing Zone preflight, What-If, azd, Azure DevOps, and jumpbox bootstrap paths. Use for approved deployment operations or incident diagnosis.

      Also in 3 projects that use this

    • Microsoft AzureSkill

      iac-validation

      Builds an evidence-based validation plan for AI Landing Zone Copilot assets, Bicep, parameters, scripts, and Azure deployment behavior. Use after implementation or during regression investigation.

      Also in 3 projects that use this

    • +10 more from Azure
  • In this repository6 other files it gives its agents.

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