feat: add SectionPlan node for manuscript section planning

custom_nodes/research/__init__.py

@@ -12,7 +12,8 @@ class ResearchExtension(ComfyExtension):
         from custom_nodes.research.evidence_assemble import ClaimEvidenceAssemble
         from custom_nodes.research.style_profile import StyleProfileExtract
         from custom_nodes.research.reference_paper_select import ReferencePaperSelect
-        return [PaperSearch, PaperClaimExtract, ClaimEvidenceAssemble, StyleProfileExtract, ReferencePaperSelect]
+        from custom_nodes.research.section_plan import SectionPlan
+        return [PaperSearch, PaperClaimExtract, ClaimEvidenceAssemble, StyleProfileExtract, ReferencePaperSelect, SectionPlan]
 
 
 async def comfy_entrypoint() -> ComfyExtension:

custom_nodes/research/section_plan.py

@@ -0,0 +1,89 @@
+"""SectionPlan node - generate section narrative plan."""
+import json
+from typing_extensions import override
+from comfy_api.latest import ComfyNode, io
+
+
+class SectionPlan(io.ComfyNode):
+    """Generate a narrative plan for a manuscript section based on claims and style."""
+
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="SectionPlan",
+            display_name="Section Plan",
+            category="Research",
+            inputs=[
+                io.String.Input(
+                    "section_type",
+                    display_name="Section Type",
+                    default="abstract",
+                ),
+                io.String.Input(
+                    "claims",
+                    display_name="Claims (JSON)",
+                    default="[]",
+                    multiline=True,
+                ),
+                io.String.Input(
+                    "style_profile",
+                    display_name="Style Profile (JSON)",
+                    default="{}",
+                    multiline=True,
+                ),
+            ],
+            outputs=[
+                io.String.Output(display_name="Section Plan (JSON)"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, section_type: str, claims: str, style_profile: str) -> io.NodeOutput:
+        try:
+            claims_list = json.loads(claims) if claims else []
+            style = json.loads(style_profile) if style_profile else {}
+        except json.JSONDecodeError:
+            claims_list = []
+            style = {}
+
+        # Generate plan based on section type
+        plan = {
+            "section_type": section_type,
+            "paragraphs": [],
+            "estimated_length": "",
+        }
+
+        if section_type.lower() == "abstract":
+            plan["paragraphs"] = [
+                {"order": 1, "purpose": "Background and problem statement", "key_claims": []},
+                {"order": 2, "purpose": "Proposed method", "key_claims": []},
+                {"order": 3, "purpose": "Key results", "key_claims": []},
+                {"order": 4, "purpose": "Conclusion and significance", "key_claims": []},
+            ]
+            plan["estimated_length"] = "150-250 words"
+        elif section_type.lower() == "introduction":
+            plan["paragraphs"] = [
+                {"order": 1, "purpose": "Hook - why this matters", "key_claims": []},
+                {"order": 2, "purpose": "Background and related work", "key_claims": []},
+                {"order": 3, "purpose": "Gap in existing approaches", "key_claims": []},
+                {"order": 4, "purpose": "Our contribution", "key_claims": []},
+            ]
+            plan["estimated_length"] = "400-800 words"
+        elif section_type.lower() == "methods":
+            plan["paragraphs"] = [
+                {"order": 1, "purpose": "Overview of approach", "key_claims": []},
+                {"order": 2, "purpose": "Dataset and preprocessing", "key_claims": []},
+                {"order": 3, "purpose": "Model architecture", "key_claims": []},
+                {"order": 4, "purpose": "Training procedure", "key_claims": []},
+                {"order": 5, "purpose": "Evaluation metrics", "key_claims": []},
+            ]
+            plan["estimated_length"] = "600-1000 words"
+
+        # Associate claims with paragraphs
+        for i, claim in enumerate(claims_list[:4]):
+            if i < len(plan["paragraphs"]):
+                plan["paragraphs"][i]["key_claims"].append(claim.get("text", ""))
+
+        plan["style_notes"] = style.get("abstract_pattern", "standard")
+
+        return io.NodeOutput(section_plan=json.dumps(plan, indent=2))