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docs: drop write-dataframe-code skill
A separate PR covers the same contributor-facing material (TPC-H pattern index, plan-comparison workflow, docstring conventions), so this skill is redundant. Remove the skill directory and the corresponding references in `AGENTS.md`, including the plan-comparison section that pointed at it. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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.ai/skills/write-dataframe-code/SKILL.md

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AGENTS.md

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@@ -38,26 +38,6 @@ Currently available skills:
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- [`check-upstream`](.ai/skills/check-upstream/SKILL.md) — audit upstream
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Apache DataFusion features (functions, DataFrame ops, SessionContext
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methods, FFI types) not yet exposed in the Python bindings.
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- [`write-dataframe-code`](.ai/skills/write-dataframe-code/SKILL.md)
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contributor-facing guide for writing idiomatic DataFrame code inside this
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repo (TPC-H pattern index, plan-comparison diagnostic, docstring
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conventions). Layers on top of the user-facing [`SKILL.md`](SKILL.md).
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## Plan-comparison diagnostic
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When translating a SQL query to a DataFrame — TPC-H, a benchmark, or an
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answer to a user question — correctness is gated by the answer-file
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comparison in `examples/tpch/_tests.py`, but plan-level equivalence is a
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separate question. Two surface-different DataFrame forms that resolve to
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the same optimized logical plan are effectively the same query.
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As an ad-hoc check, compare `ctx.sql(reference_sql).optimized_logical_plan()`
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against the DataFrame's `optimized_logical_plan()`. Use `LogicalPlan.__eq__`
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for structural equality and `LogicalPlan.display_indent()` for readable
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diffs. This is a diagnostic, not a gate — a mismatch does not mean the
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DataFrame form is wrong, only that the two forms are not literally the same
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plan. The [`write-dataframe-code`](.ai/skills/write-dataframe-code/SKILL.md)
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skill has the full workflow.
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## Pull Requests
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