ml-docs

ML Docs

Context: $ARGUMENTS

Quick start

• Look up a specific function/class: → Step 1, then Step 2
• Understand a concept or workflow: → Step 1, pick a topic URL, fetch it
• Compare options across libraries: → run Step 1 for each library, fetch in parallel

When to use

• User asks how to use any function, class, or method from a supported library
• User asks "what parameters does X take" or "what does Y return"
• User needs working code examples for a task (groupby, cross-validation, fine-tuning, etc.)
• User asks about a concept: broadcasting, autograd, attention, sparse matrices, etc.
• User wants to know which library/function to use for a task
• User hits an error and needs to check expected behavior from the official docs

Step 1 — Resolve the documentation URL

Run process.py resolve to get the prioritized list of URLs to fetch:

uv run ${CLAUDE_SKILL_DIR}/scripts/process.py resolve \
  --library <library-name-or-alias> \
  --query "<function, class, or topic>"

The script returns a JSON object with fetch_in_order — a list of URLs ranked by specificity. Start with priority 1. If it returns 404 or empty content, move to priority 2, then 3.

Library aliases accepted: See references/guide.md for the full alias table.

Examples:

uv run ${CLAUDE_SKILL_DIR}/scripts/process.py resolve --library pandas --query "DataFrame.groupby"
uv run ${CLAUDE_SKILL_DIR}/scripts/process.py resolve --library torch --query "autograd"
uv run ${CLAUDE_SKILL_DIR}/scripts/process.py resolve --library sklearn --query "cross_val_score"
uv run ${CLAUDE_SKILL_DIR}/scripts/process.py resolve --library transformers --query "pipeline"
uv run ${CLAUDE_SKILL_DIR}/scripts/process.py resolve --library scipy --query "hypothesis testing"

To list all supported libraries:

uv run ${CLAUDE_SKILL_DIR}/scripts/process.py list

Step 2 — Fetch and synthesize

Use WebFetch on the URLs returned by Step 1, in order. Stop at the first URL that contains useful content. Synthesize into a direct answer including:

1. What it does — one sentence
2. Signature — function/class signature with parameter types if available
3. Key parameters — name, type, default, what it controls
4. Return value — type and meaning
5. Working example — copy-pasteable, minimal, correct
6. Common gotchas — anything non-obvious from the docs (deprecations, defaults that surprise)

Do not paste raw documentation. Synthesize it.

Step 3 — Cross-library questions

When the question spans multiple libraries (e.g. "how do I use a PyTorch model with scikit-learn's cross-validation"), run Step 1 for each library in parallel, fetch both, then synthesize a combined answer that shows how they integrate.

Gotchas

• Keras vs tf.keras: Keras 3.x (keras.io) is standalone and backend-agnostic. tf.keras is the older TF-bundled version. If the user has TF < 2.16, they likely have tf.keras. Check which one they're importing before advising.

• PyTorch torch. vs torch.nn.functional: Many operations exist in both places with different call conventions. torch.nn.Conv2d is a module (stateful), F.conv2d is a function. The docs are on different pages — resolve for the right one.

• scikit-learn class paths: API URLs use the full dotted path, e.g. sklearn.linear_model.LogisticRegression, not just LogisticRegression. Include the module prefix in the --query argument for direct API lookups.

• Pandas 2.x breaking changes: DataFrame.append is removed in 2.0. df.swaplevel behavior changed. If the user shows old code, check the 2.0 migration guide: https://pandas.pydata.org/docs/whatsnew/v2.0.0.html

• Hugging Face pipeline task names: They changed between versions. Always fetch the current docs rather than recalling task names from memory (e.g. "text-generation" vs "text2text-generation").

• spaCy model names: en_core_web_sm/md/lg/trf are not installed by default. The docs show nlp = spacy.load("en_core_web_sm") but users need python -m spacy download en_core_web_sm first. Always mention this.

• OpenCV Python bindings: The Python docs at docs.opencv.org are C++ first. Prefer fetching the Python tutorials (/tutorial_py_*) over the raw C++ API pages.

• PySpark version: API paths differ between Spark 3.x and older versions. The default URL targets latest. If the user specifies a version, adjust the URL.

• Dask DataFrame is NOT Pandas: Dask DataFrames don't support all Pandas operations. Always check the Dask API index rather than assuming Pandas parity.

• LightGBM vs XGBoost parameter names: They use different names for the same concept (e.g. num_leaves in LightGBM vs max_leaves in XGBoost). When helping with both, always fetch both sets of parameter docs.

Examples

Example 1: Look up a Pandas function

User: "How do I use pandas pivot_table?"

uv run ${CLAUDE_SKILL_DIR}/scripts/process.py resolve --library pandas --query "DataFrame.pivot_table"

Fetch priority-1 URL → synthesize parameters, example, gotchas about aggfunc defaulting to mean.

Example 2: PyTorch training loop

User: "How does autograd work in PyTorch?"

uv run ${CLAUDE_SKILL_DIR}/scripts/process.py resolve --library torch --query "autograd"

Fetch the autograd topic page → explain requires_grad, .backward(), zero_grad(), with example.

Example 3: Hugging Face fine-tuning

User: "How do I fine-tune BERT with the Trainer API?"

uv run ${CLAUDE_SKILL_DIR}/scripts/process.py resolve --library transformers --query "trainer"
uv run ${CLAUDE_SKILL_DIR}/scripts/process.py resolve --library transformers --query "bert"

Fetch both in parallel → synthesize a complete fine-tuning workflow.

Example 4: Statistical test

User: "How do I run a t-test in Python?"

uv run ${CLAUDE_SKILL_DIR}/scripts/process.py resolve --library scipy --query "hypothesis testing"

Fetch stats topic page → show scipy.stats.ttest_ind and ttest_rel with example and interpretation.

Example 5: Unknown library scope

User: "How do I do X?"

Run:

uv run ${CLAUDE_SKILL_DIR}/scripts/process.py list

Scan the list for the relevant library. If the task spans multiple (e.g. plot a pandas DataFrame with Seaborn), resolve for both, fetch in parallel, synthesize combined answer.

Reference docs

Read references/guide.md for:

• Full alias table for all 19 libraries
• Direct topic URL index (skip the script for common topics)
• Library selection guidance (which library to use for a given task)