Single tool 'execute_skill_action' has a vague, generic name that violates verb_noun convention. Description is marketing-speak ("deterministic, zero-dependency AI Document Intelligence") rather than actionable guidance for LLM tool selection. Input schema is minimal: only 'query_payload' (string) with no format, length, or content constraints. No output schema documented, the tool echoes back a JSON blob with no structure guarantee. No error handling guidance, no parameter validation rules, no recovery paths. The actual business logic (skew angle correction) is hidden in client.py and never exposed as a tool, only a stub that echoes input exists.

• Rename tool to 'correct_document_skew' or 'estimate_skew_angle' to follow verb_noun convention and clearly signal the action performed.
• Replace marketing description with actionable guidance: 'Analyzes text line segments from a scanned document and estimates the rotation skew angle in degrees. Returns recommended rotation correction and confidence score. Call this before deskewing document images.'
• Define query_payload schema explicitly. Either accept a structured JSON object with 'text_line_segments' array (each with x1, y1, x2, y2 fields) OR document the exact string format expected (e.g., JSON-encoded array of segments).
• Document output schema in tool definition: 'Returns object with estimated_skew_degrees (float), recommended_rotation_degrees (float), needs_deskew (boolean), sample_lines_count (int), confidence (0-1 float), status (string: SUCCESS|INSUFFICIENT_DATA|NO_HORIZONTAL_LINES)'.
• Wire the actual ScannedDocumentSkewOrientationAngleCorrector logic into the tool handler. Parse query_payload as JSON, call estimate_skew_angle(), and return structured result instead of echoing input.