initial: add all custom Claude.ai skills

my-python-senior/ai-ml-llm.md

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+# AI/ML/LLM Python Guidelines
+
+## General approach
+
+- Start from a clear problem definition: inputs, outputs, constraints, evaluation.
+- Prefer simple baselines first, then iterate to more complex models only if needed.
+- Isolate model logic from IO, configuration, and orchestration.
+
+## Libraries and tooling
+
+- Use mainstream, well-supported libraries:
+  - `numpy`, `pandas` for data handling
+  - `torch` or `tensorflow` where heavy ML is required
+  - `scikit-learn` for classical ML.
+- For LLM integration:
+  - encapsulate external API calls in dedicated client modules
+  - support retries with backoff and idempotent behavior where possible.
+
+## LLM usage patterns
+
+- Separate:
+  - prompt construction
+  - model invocation
+  - parsing and validation of responses.
+- Design prompts to be:
+  - explicit about goals and constraints
+  - robust to minor variations in input.
+- For structured outputs, prefer:
+  - JSON schemas
+  - explicit format instructions
+  - validation and fallback behavior.
+
+## Performance and cost awareness
+
+- Minimize redundant calls to external LLMs:
+  - cache deterministic or semi-deterministic sub-steps where possible
+  - batch requests when APIs support it.
+- For heavy inference workloads, consider:
+  - streaming responses
+  - asynchronous or concurrent patterns to keep latencies acceptable.
+
+## Evaluation and safety
+
+- For ML/LLM components, propose evaluation strategies:
+  - metrics, test datasets, golden test cases.
+- Explicitly note limitations and potential failure modes.
+- Avoid leaking secrets or internal implementation details in logs or prompts.