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docs: replace static function lists with discovery instructions in skill
Replace hardcoded "Known aggregate/window functions with literal-only
arguments" lists with instructions to discover them dynamically by
searching the upstream crate source. Keeps a few examples as validation
anchors so the agent knows its search is working correctly.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
**If you find this pattern:** The argument is **Category B** — accept only the corresponding native Python type, not `Expr`. The function will error at planning time with a non-literal expression.
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Known aggregate functions with literal-only arguments:
To discover which aggregate functions have literal-only arguments, search the upstream aggregate crate for `get_scalar_value`, `validate_percentile_expr`, and `downcast_ref::<Literal>()` inside `accumulator()` methods. For example, you should expect to find `approx_percentile_cont` (percentile) and `string_agg` (delimiter) among the results.
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#### Technique 1b: Check `partition_evaluator()` for literal-only enforcement (window functions)
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@@ -152,11 +147,7 @@ fn partition_evaluator(
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**If you find this pattern:** The argument is **Category B** — accept only the corresponding native Python type, not `Expr`. The function will error at planning time with a non-literal expression.
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Known window functions with literal-only arguments:
To discover which window functions have literal-only arguments, search the upstream window crate for `get_scalar_value_from_args` inside `partition_evaluator()` methods. For example, you should expect to find `ntile` (n) and `lead`/`lag` (offset, default_value) among the results.
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#### Technique 2: Check the `Signature` for data type constraints
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