docs: add CLI reference page and remove CLI from API docs (#704)

.github/workflows/docs-publish.yml

@@ -81,6 +81,11 @@ jobs:
       - name: Generate API documentation
         run: uv run python tooling/docs-autogen/build.py
 
+      # -- Run docs-autogen unit tests ------------------------------------------
+
+      - name: Run CLI reference tests
+        run: uv run pytest tooling/docs-autogen/test_cli_reference.py -v --tb=short
+
       # -- Validate static docs ------------------------------------------------
 
       - name: Lint static docs (markdownlint)

.gitignore

@@ -455,7 +455,8 @@ pyrightconfig.json
 .claude/*
 !.claude/settings.json
 
-# Generated API documentation (built by tooling/docs-autogen/)
+# Generated documentation (built by tooling/docs-autogen/)
 docs/docs/api/
 docs/docs/api-reference.mdx
+docs/docs/reference/cli.md
 .venv-docs-autogen/

AGENTS.md

@@ -88,6 +88,7 @@ mkdir -p .bob && ln -s ../.agents/skills .bob/skills
 - Use `...` in `@generative` function bodies
 - Prefer primitives over classes
 - **Friendly Dependency Errors**: Wraps optional backend imports in `try/except ImportError` with a helpful message (e.g., "Please pip install mellea[hf]"). See `mellea/stdlib/session.py` for examples.
+- **CLI command docstrings**: Typer command functions in `cli/` follow an enriched convention with `Prerequisites:` and `See Also:` sections — these feed the auto-generated CLI reference page. See [`docs/docs/guide/CONTRIBUTING.md`](docs/docs/guide/CONTRIBUTING.md) for the full pattern. Regenerate after changes: `uv run poe clidocs`. Test the generator: `uv run pytest tooling/docs-autogen/test_cli_reference.py -v`. Full pipeline docs: [`tooling/docs-autogen/README.md`](tooling/docs-autogen/README.md).
 - **Backend telemetry fields**: All backends must populate `mot.usage` (dict with `prompt_tokens`, `completion_tokens`, `total_tokens`), `mot.model` (str), and `mot.provider` (str) in their `post_processing()` method. `mot.streaming` (bool) and `mot.ttfb_ms` (float | None) are set automatically in `astream()` — backends do not need to set them. Metrics are automatically recorded by `TokenMetricsPlugin` and `LatencyMetricsPlugin` — don't add manual `record_token_usage_metrics()` or `record_request_duration()` calls.
 
 ## 6. Commits & Hooks

cli/alora/commands.py

@@ -31,7 +31,25 @@ def alora_train(
     max_length: int = typer.Option(1024, help="Max sequence length"),
     grad_accum: int = typer.Option(4, help="Gradient accumulation steps"),
 ):
-    """Train an aLoRA or LoRA model on your dataset.
+    """Train an aLoRA or LoRA adapter on a labelled dataset.
+
+    Fine-tunes a base causal language model using a JSONL dataset of item/label
+    pairs. Supports both aLoRA (asymmetric LoRA) and standard LoRA adapters.
+
+    Prerequisites:
+        Mellea installed with adapter extras (``uv add mellea[adapters]``).
+        A CUDA, MPS, or CPU device available for training.
+
+    Output:
+        Saves adapter weights to the path specified by ``--outfile``. The output
+        directory contains an ``adapter_config.json`` and the trained weight
+        files, ready for upload or local inference.
+
+    Examples:
+        m alora train data.jsonl --basemodel ibm-granite/granite-3.3-2b-instruct --outfile ./adapter
+
+    See Also:
+        guide: advanced/lora-and-alora-adapters
 
     Args:
         datafile: JSONL file with item/label pairs for training.
@@ -79,7 +97,23 @@ def alora_upload(
         "processing if the model is invoked as an intrinsic.",
     ),
 ):
-    """Upload trained adapter to remote model registry.
+    """Upload a trained adapter to a remote model registry.
+
+    Pushes adapter weights to Hugging Face Hub, optionally packaging the adapter
+    as an intrinsic with an ``io.yaml`` configuration file.
+
+    Prerequisites:
+        Hugging Face CLI authenticated (``huggingface-cli login``).
+
+    Output:
+        Creates or updates a Hugging Face Hub repository at the name specified
+        by ``--name`` and uploads the adapter weight files.
+
+    Examples:
+        m alora upload ./adapter --name acme/my-alora
+
+    See Also:
+        guide: advanced/lora-and-alora-adapters
 
     Args:
         weight_path: Path to saved adapter weights directory.
@@ -140,6 +174,24 @@ def alora_add_readme(
 ):
     """Generate and upload an INTRINSIC_README.md for a trained adapter.
 
+    Uses an LLM to auto-generate documentation for a trained adapter based on
+    the training data and model configuration, then uploads it to the Hugging
+    Face Hub repository.
+
+    Prerequisites:
+        Hugging Face CLI authenticated (``huggingface-cli login``).
+        An LLM backend available for README generation.
+
+    Output:
+        Generates a README.md file, displays it for confirmation, and uploads
+        it to the Hugging Face Hub repository specified by ``--name``.
+
+    Examples:
+        m alora add-readme data.jsonl --basemodel ibm-granite/granite-3.3-2b-instruct --name acme/my-alora
+
+    See Also:
+        guide: advanced/lora-and-alora-adapters
+
     Args:
         datafile: JSONL file with item/label pairs used to train the adapter.
         basemodel: Base model ID or path.

cli/decompose/decompose.py

@@ -236,12 +236,28 @@ def run(
         ),
     ] = False,
 ) -> None:
-    """Runs the ``m decompose`` CLI workflow and writes generated outputs.
+    """Break a complex task into ordered, executable subtasks.
 
-    Reads user queries from a file or interactive input, runs the decomposition
-    pipeline for each task job, and writes one JSON file, one rendered Python
-    program, and any generated validation modules under a per-job output
-    directory.
+    Reads user queries from a file or interactive input, runs the LLM-driven
+    decomposition pipeline for each task job, and writes one JSON file, one
+    rendered Python script, and any generated validation modules under a per-job
+    output directory.
+
+    Prerequisites:
+        Mellea installed (``uv add mellea``). An Ollama instance running locally,
+        or an OpenAI-compatible endpoint configured via ``--backend-endpoint``.
+
+    Output:
+        Creates a directory ``<out-dir>/<out-name>/`` containing a JSON
+        decomposition result file, a ready-to-run Python script, and any
+        generated validation modules. One directory per task job.
+
+    Examples:
+        m decompose run --out-dir ./output --input-file tasks.txt
+
+    See Also:
+        guide: guide/m-decompose
+        guide: how-to/refactor-prompts-with-cli
 
     Args:
         out_dir: Existing directory under which per-job output directories are

cli/eval/commands.py

@@ -4,22 +4,38 @@
 
 import typer
 
-eval_app = typer.Typer(name="eval")
+eval_app = typer.Typer(name="eval", help="LLM-as-a-judge evaluation pipelines.")
 
 
 def eval_run(
     test_files: list[str] = typer.Argument(
         ..., help="List of paths to json/jsonl files containing test cases"
     ),
-    backend: str = typer.Option("ollama", "--backend", "-b", help="Generation backend"),
-    model: str = typer.Option(None, "--model", help="Generation model name"),
+    backend: str = typer.Option(
+        "ollama",
+        "--backend",
+        "-b",
+        help="Inference backend for generating candidate responses (e.g. ollama, openai)",
+    ),
+    model: str = typer.Option(
+        None,
+        "--model",
+        help="Model name/id for the generation backend; uses backend default if omitted",
+    ),
     max_gen_tokens: int = typer.Option(
         256, "--max-gen-tokens", help="Max tokens to generate for responses"
     ),
     judge_backend: str = typer.Option(
-        None, "--judge-backend", "-jb", help="Judge backend"
+        None,
+        "--judge-backend",
+        "-jb",
+        help="Inference backend for the judge model; reuses --backend if omitted",
+    ),
+    judge_model: str = typer.Option(
+        None,
+        "--judge-model",
+        help="Model name/id for the judge; uses judge backend default if omitted",
     ),
-    judge_model: str = typer.Option(None, "--judge-model", help="Judge model name"),
     max_judge_tokens: int = typer.Option(
         256, "--max-judge-tokens", help="Max tokens for the judge model's judgement."
     ),
@@ -29,14 +45,34 @@ def eval_run(
     output_format: str = typer.Option(
         "json", "--output-format", help="Either json or jsonl format for results"
     ),
-    continue_on_error: bool = typer.Option(True, "--continue-on-error"),
+    continue_on_error: bool = typer.Option(
+        True,
+        "--continue-on-error",
+        help="Skip failed test cases instead of aborting the entire run",
+    ),
 ):
     """Run LLM-as-a-judge evaluation on one or more test files.
 
     Loads test cases from JSON/JSONL files, generates candidate responses using
     the specified generation backend, scores them with a judge model, and writes
     aggregated results to a file.
 
+    Prerequisites:
+        Mellea installed (``uv add mellea``). At least one inference backend
+        available (Ollama by default). A separate judge backend/model is
+        recommended but optional (defaults to the generation backend).
+
+    Output:
+        Writes evaluation results to ``<output-path>.<output-format>`` (default
+        ``eval_results.json``). The file contains per-test-case scores, judge
+        verdicts, and aggregate statistics.
+
+    Examples:
+        m eval run tests.jsonl --backend ollama --model granite3.3:2b
+
+    See Also:
+        guide: evaluation-and-observability/evaluate-with-llm-as-a-judge
+
     Args:
         test_files: Paths to JSON/JSONL files containing test cases.
         backend: Generation backend name.

cli/fix/commands.py

@@ -16,7 +16,21 @@ def fix_async(
         False, "--dry-run", help="Report locations without modifying files"
     ),
 ):
-    """Fix async calls (aact, ainstruct, aquery) for the await_result default change.
+    """Fix async calls for the await_result default change.
+
+    Scans Python source files for ``aact``, ``ainstruct``, and ``aquery`` calls
+    and applies an automated migration to restore blocking behaviour after the
+    ``await_result`` default changed from ``True`` to ``False``.
+
+    Prerequisites:
+        Mellea installed (``uv add mellea``).
+
+    Output:
+        Modifies Python source files in place (unless ``--dry-run``). Prints a
+        summary of fixed call sites with file paths and line numbers.
+
+    Examples:
+        m fix async src/ --dry-run
 
     Args:
         path: File or directory to scan.
@@ -83,7 +97,20 @@ def fix_genslots(
         False, "--dry-run", help="Report locations without modifying files"
     ),
 ):
-    """Rewrite old genslot imports and class names to genstub equivalents.
+    """Rewrite genslot imports and class names to genstub equivalents.
+
+    Scans Python source files and replaces deprecated ``GenerativeSlot`` imports
+    and class references with their ``GenerativeStub`` replacements.
+
+    Prerequisites:
+        Mellea installed (``uv add mellea``).
+
+    Output:
+        Modifies Python source files in place (unless ``--dry-run``). Prints a
+        summary of rewritten references with file paths and line numbers.
+
+    Examples:
+        m fix genslots src/ --dry-run
 
     Args:
         path: File or directory to scan.

cli/m.py

@@ -23,8 +23,15 @@ def callback() -> None:
     """Mellea command-line tool for LLM-powered workflows.
 
     Provides sub-commands for serving models (``m serve``), training and uploading
-    adapters (``m alora``), decomposing tasks into subtasks (``m decompose``), and
-    running test-based evaluation pipelines (``m eval``).
+    adapters (``m alora``), decomposing tasks into subtasks (``m decompose``),
+    running test-based evaluation pipelines (``m eval``), and applying automated
+    code migrations (``m fix``).
+
+    Prerequisites:
+        Mellea installed (``uv add mellea``).
+
+    See Also:
+        guide: getting-started/quickstart
     """
 
 

cli/serve/app.py

@@ -221,7 +221,27 @@ def serve(
     host: str = typer.Option("0.0.0.0", help="Host to bind to"),
     port: int = typer.Option(8080, help="Port to bind to"),
 ):
-    """Serve a FastAPI endpoint for a given script."""
+    """Serve a Mellea program as an OpenAI-compatible HTTP endpoint.
+
+    Loads a Python script containing a Mellea generative function and exposes it
+    via a FastAPI server implementing the OpenAI chat completions API. The server
+    accepts ``POST /v1/chat/completions`` requests.
+
+    Prerequisites:
+        Mellea installed (``uv add mellea``). The target script must define at
+        least one generative function.
+
+    Output:
+        Starts a long-running HTTP server on the specified host and port.
+        The ``/v1/chat/completions`` endpoint accepts OpenAI-format chat
+        completion requests and returns ``ChatCompletion`` JSON responses.
+
+    Examples:
+        m serve my_app.py --port 9000
+
+    See Also:
+        guide: integrations/m-serve
+    """
     module = load_module_from_path(script_path)
     route_path = "/v1/chat/completions"
 

docs/docs/advanced/lora-and-alora-adapters.md

@@ -158,4 +158,5 @@ affect other sessions.
 
 **See also:** [Intrinsics](./intrinsics) |
 [The Requirements System](../concepts/requirements-system) |
-[Write Custom Verifiers](../how-to/write-custom-verifiers)
+[Write Custom Verifiers](../how-to/write-custom-verifiers) |
+[CLI Reference](../reference/cli)