mcpbeat

Generate From Typespec

microsoft/generate-from-typespec

> Generate Python SDK code from a TypeSpec specification using the local emitter. Use this skill when the user wants to generate/regenerate a Python client from a TypeSpec spec, provides a GitHub URL or local path to a TypeSpec project, or says things like "generate from this spec", "emit Python from this tsp", "regenerate the SDK", or "compile this TypeSpec for Python".

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on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/microsoft/typespec --skill generate-from-typespec

The instruction itself

15 sections, as written by the author

Generate From TypeSpec Skill

Compiles a TypeSpec specification using the local @typespec/http-client-python

emitter and generates Python SDK code. Supports both branded (@azure-tools/typespec-python)

and unbranded (@typespec/http-client-python) generation.

Inputs

The caller must provide:

  • Spec path — either:
  • A local file path to a .tsp entry point (e.g., client.tsp, main.tsp)
  • A raw GitHub URL pointing to a TypeSpec project directory or file

(e.g., https://github.com/Azure/azure-rest-api-specs/tree/main/specification/.../Foundry/src/sdk-service-agentserver-contracts/client.tsp)

  • Flavorazure (branded) or unbranded. If not provided, ask the user.
  • Emitter output directory — the full resolved path where generated code

should be written (e.g., ~/Desktop/github/azure-sdk-for-python/sdk/agentserver/azure-ai-agentserver-responses).

If not provided, ask the user.

  • Additional options (optional) — any extra key=value emitter options the

user wants applied on top of the tspconfig options. These override tspconfig

values if there's a conflict (e.g., models-mode=typeddict).

Workflow

Step 1: Resolve the spec path

If the input is a GitHub URL:

  • Parse the URL to extract owner, repo, ref (branch/commit), and path.
  • Check if the repository is cloned locally (look under common locations like

~/Desktop/github/<repo-name>, ~/<repo-name>, etc.).

  • If found locally, check out the correct ref/commit if needed:
   cd <local-repo>
   git fetch origin <ref>
   git checkout <ref> -- <path-to-spec-dir>/
  • If not found locally, ask the user where the repo is cloned, or offer to clone it.

If the input is a local path:

Verify the file/directory exists. If the path points to a directory, look for

client.tsp or main.tsp as the entry point.

Step 2: Locate and parse tspconfig.yaml

Look for tspconfig.yaml in the same directory as the spec entry point, then

walk up parent directories until one is found.

# Starting from the spec file's directory, search for tspconfig.yaml
current_dir="<spec-dir>"
while [ "$current_dir" != "/" ]; do
  if [ -f "$current_dir/tspconfig.yaml" ]; then
    echo "Found: $current_dir/tspconfig.yaml"
    break
  fi
  current_dir=$(dirname "$current_dir")
done

Step 3: Extract Python emitter options from tspconfig.yaml

Parse the tspconfig.yaml and extract the options block for the Python emitter.

The emitter may appear under either name:

| Flavor | Emitter key in tspconfig.yaml |

| --------- | ------------------------------ |

| Branded | @azure-tools/typespec-python |

| Unbranded | @typespec/http-client-python |

Important cross-flavor rule: If the user requests a different flavor than

what's in the tspconfig, carry over ALL options from the tspconfig's Python

emitter block. For example:

  • tspconfig has options under @azure-tools/typespec-python but user wants

unbranded → use all those options, but emit under @typespec/http-client-python

  • tspconfig has options under @typespec/http-client-python but user wants

branded → use all those options, but emit under @azure-tools/typespec-python

If no Python emitter options exist in the tspconfig at all, ask the user for:

  • emitter-output-dir (required — where to write the generated code)
  • package-name (required)
  • namespace (optional — omit to let @clientNamespace decorators resolve naturally)

Step 4: Determine the flavor

Use this precedence:

  • If the user explicitly stated azure or unbranded, use that.
  • If the tspconfig has a flavor option set, mention it to the user and confirm.
  • If the tspconfig only has one Python emitter key, infer:
  • @azure-tools/typespec-pythonazure
  • @typespec/http-client-pythonunbranded
  • If still ambiguous, ask the user:

> "Should I generate as branded (azure flavor) or unbranded?"

Step 5: Find the TypeSpec compiler

Look for the tsp CLI in the spec repo's node_modules:

# Check spec repo root for compiler
< spec-repo-root > /node_modules/@typespec/compiler/cmd/tsp.js

If not found, fall back to the global tsp command, or check the typespec

monorepo's compiler:

~/Desktop/github/typespec/packages/compiler/cmd/tsp.js

Step 6: Construct and run the compile command

Build the tsp compile command using:

  • Entry point: The resolved .tsp file from Step 1
  • --emit: Always the local emitter path:

~/Desktop/github/typespec/packages/http-client-python

  • --option flags: One for each option from the tspconfig, prefixed with

the local emitter name @typespec/http-client-python (regardless of

what the tspconfig called it). Any additional options provided by the user

are appended last and override tspconfig values if there's a conflict.

Template:

< tsp-cli-path > compile < entry-point.tsp > --emit ~/Desktop/github/typespec/packages/http-client-python \
  --option "@typespec/http-client-python.<key1>=<value1>" \
  --option "@typespec/http-client-python.<key2>=<value2>" \
  ...

Option mapping rules:

| tspconfig key | CLI --option key | Notes |

| -------------------- | -------------------- | ------------------------------------------------- |

| emitter-output-dir | emitter-output-dir | Resolve {output-dir}, {service-dir} variables |

| package-mode | package-mode | Usually dataplane or mgmt |

| package-name | package-name | |

| namespace | namespace | Omit if not in tspconfig — see note below |

| api-version | api-version | |

| flavor | flavor | Set to azure for branded, omit for unbranded |

| generate-test | generate-test | |

| generate-sample | generate-sample | |

| models-mode | models-mode | e.g., dpg, msrest, typeddict |

| Any other option | Pass through as-is | |

Namespace note: Do NOT pass --namespace unless it is explicitly set in the

tspconfig or by the user. When omitted, the emitter lets TCGC resolve

@clientNamespace decorators correctly. Passing a namespace when @clientNamespace

is used in the spec can cause incorrect directory nesting.

emitter-output-dir: Always use the value provided by the user (Input #3).

Ignore the emitter-output-dir from the tspconfig — it typically contains

unresolvable template variables like {output-dir} and {service-dir}.

Step 7: Run the compilation

cd <spec-directory>
<constructed-compile-command>

Set a timeout of at least 180 seconds — compilation can take a few minutes.

Check the output:

  • Warnings only → success
  • Errors → report to the user with the full error output

Step 8: Verify and clean up

After successful compilation:

  • Show the generated directory structure:
   find < output-dir > -type d | sort
  • Verify the output matches expectations (e.g., TypedDict if models-mode=typeddict).
  • If the generation overwrote files in an existing package, warn the user and

offer to revert non-generated files:

   cd <sdk-repo>
   git diff --name-status <package-dir>/ | grep -v "<expected-generated-path>"

Notes

The --namespace trap

When a TypeSpec uses @clientNamespace to map types into a different namespace,

TCGC resolves the namespace. If you also pass --namespace, TCGC tries to

replace the root of the @clientNamespace value with the flag, which can produce

doubled prefixes like azure.azure.ai.projects.... **Only pass --namespace

when the tspconfig explicitly sets it.**

Branded vs unbranded emitter names

The local emitter is always @typespec/http-client-python on the CLI --emit

and --option flags. The flavor option controls branded behavior:

  • Branded: --option "@typespec/http-client-python.flavor=azure"
  • Unbranded: omit the flavor option entirely

Common additional options the user may request

| User request | Option to add |

| -------------- | --------------------------------------------------------------- |

| TypedDict only | --option "@typespec/http-client-python.models-mode=typeddict" |

| No tests | --option "@typespec/http-client-python.generate-test=false" |

| No samples | --option "@typespec/http-client-python.generate-sample=false" |

How to use it

Copy the folder

Take microsoft/generate-from-typespec from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.