Wire up all agents with LLM calls

- Worldsmith, Character Lead, Voice, Editor agents now call LLM
- All nonfiction agents wired (Researcher, Analyst, Writer, FactChecker, Editor)
- Orchestrator fully wired with agent pipeline
- Add python-dotenv dependency

opus_orchestrator/agents/fiction/character_lead.py

@@ -46,7 +46,7 @@ You are The Character Lead — the one who breathes life into the figures who in
 - **Positive**: Growth from weakness
 - **Negative**: Fall from grace
 - **Flat**: No change, changes world
-- **Disruption**: External力量打破平衡
+- **Disruption**: External forces break equilibrium
 
 ## The Want/Need/Fear Triad
 
@@ -77,39 +77,52 @@ class CharacterLeadAgent(BaseAgent):
         )
 
     async def execute(self, input_data: Any, context: dict[str, Any]) -> AgentResponse:
-        """Execute the Character Lead's task to generate character profiles.
-
-        Args:
-            input_data: Raw content + blueprint with character references
-            context: Additional context
-
-        Returns:
-            AgentResponse with character profiles
-        """
+        """Execute the Character Lead's task to generate character profiles."""
         characters = input_data.get("characters", [])
         raw_content = input_data.get("raw_content", "")
+        blueprint = input_data.get("blueprint", {})
 
         user_prompt = f"""## Task
 
-Create comprehensive character profiles for the following characters:
+Create comprehensive character profiles for the following story:
 
-{chr(10).join(f"- {c}" for c in characters) if characters else "Create profiles for all characters in the story."}
+- Title: {blueprint.get('title', 'Untitled')}
+- Genre: {blueprint.get('genre', 'general')}
+
+{chr(10).join(f'- {c}' for c in characters) if characters else 'Create compelling characters that would drive this story.'}
 
 ## Raw Content Reference
 
-{raw_content}
+{raw_content if raw_content else 'Create original characters appropriate for this genre and story.'}
 
 ## Guidelines
 
 Follow the Character Lead methodology from your system prompt.
 Include the Want/Need/Fear triad for each major character.
+Ensure each character has a distinct voice and arc.
 """
 
-        return AgentResponse(
-            success=True,
-            output={"status": "characters_created"},
-            metadata={"role": "Character Lead", "character_count": len(characters)},
-        )
+        try:
+            result = await self.call_llm(
+                system_prompt=self.build_system_prompt(context),
+                user_prompt=user_prompt,
+            )
+
+            return AgentResponse(
+                success=True,
+                output=result,
+                metadata={
+                    "role": "Character Lead",
+                    "character_count": len(characters) if characters else 0,
+                },
+            )
+        except Exception as e:
+            return AgentResponse(
+                success=False,
+                output=None,
+                error=str(e),
+                metadata={"role": "Character Lead"},
+            )
 
     async def develop_relationship(
         self,
@@ -129,14 +142,27 @@ Include the Want/Need/Fear triad for each major character.
 
 Develop this relationship following the Character Lead methodology.
 Include:
-- Current dynamics
-- Power balance
+- Current dynamics and power balance
 - History (if any)
-- Potential arc
+- Potential arc throughout the story
+- Key moments that define the relationship
 """
 
-        return AgentResponse(
-            success=True,
-            output={"status": "relationship_developed"},
-            metadata={"role": "Character Lead", "characters": [character_a, character_b]},
-        )
+        try:
+            result = await self.call_llm(
+                system_prompt=self.build_system_prompt(context),
+                user_prompt=user_prompt,
+            )
+
+            return AgentResponse(
+                success=True,
+                output=result,
+                metadata={"role": "Character Lead", "characters": [character_a, character_b]},
+            )
+        except Exception as e:
+            return AgentResponse(
+                success=False,
+                output=None,
+                error=str(e),
+                metadata={"role": "Character Lead"},
+            )

opus_orchestrator/agents/fiction/editor.py

@@ -64,12 +64,14 @@ You are The Editor — the quality control mechanism, identifying problems acros
 - **Minor Revisions**: Continuity errors, style inconsistencies, pacing tweaks
 - **Polish**: Grammar, punctuation, word choice refinement
 
-## Quality Standards
+## Output Format
 
-- Every issue must have specific, actionable feedback
-- Revision priorities must be clearly ordered
-- Continuity issues must be flagged with exact locations
-- Pacing analysis must be data-driven (scene lengths, tension scores)
+Provide your critique as a structured review with:
+1. Overall score (0.0-1.0)
+2. Strengths (list)
+3. Weaknesses (list)
+4. Specific revision suggestions (prioritized)
+5. Final verdict: major_revisions / minor_revisions / approved
 """
 
 
@@ -85,41 +87,43 @@ class EditorAgent(BaseAgent):
         )
 
     async def execute(self, input_data: Any, context: dict[str, Any]) -> AgentResponse:
-        """Execute the Editor's task to review and assess the manuscript.
-
-        Args:
-            input_data: Chapter or manuscript to review
-            context: Review criteria and standards
-
-        Returns:
-            AgentResponse with editorial assessment
-        """
+        """Execute the Editor's task to review content."""
         content = input_data.get("content", "")
         review_type = input_data.get("review_type", "full")
 
         user_prompt = f"""## Task
 
-Perform a {review_type} editorial review on:
+Perform a {review_type} editorial review on the following content:
 
-{content[:5000]}... {'(truncated)' if len(content) > 5000 else ''}
+{content}
 
 ## Review Type: {review_type}
 
 ## Guidelines
 
 Follow the Editor methodology from your system prompt.
-Include:
-- Continuity verification
-- Pacing analysis
-- Quality assessment
-- Specific revision directions
+Be specific and actionable in your feedback.
+Assign a clear revision priority.
 """
 
-        return AgentResponse(
-            success=True,
-            output={"status": "editorial_review_complete"},
-            metadata={"role": "Editor", "review_type": review_type},
-        )
+        try:
+            result = await self.call_llm(
+                system_prompt=self.build_system_prompt(context),
+                user_prompt=user_prompt,
+            )
+
+            return AgentResponse(
+                success=True,
+                output=result,
+                metadata={"role": "Editor", "review_type": review_type},
+            )
+        except Exception as e:
+            return AgentResponse(
+                success=False,
+                output=None,
+                error=str(e),
+                metadata={"role": "Editor"},
+            )
 
     async def review_chapter(
         self,
@@ -132,14 +136,15 @@ Include:
 
 - Chapter Number: {chapter.get('chapter_number')}
 - Title: {chapter.get('title')}
-- Content: {chapter.get('content', '')[:3000]}...
+- Content:
+
+{chapter.get('content', '')}
 
 ## Full Manuscript Context
 
 - Total Chapters: {full_manuscript_context.get('total_chapters', 0)}
-- Previous Chapters Summary: {full_manuscript_context.get('previous_summaries', [])}
-- Characters in Story: {', '.join(full_manuscript_context.get('characters', []))}
-- World Rules: {full_manuscript_context.get('world_rules', {})}
+- Book Title: {full_manuscript_context.get('title', 'Untitled')}
+- Genre: {full_manuscript_context.get('genre', 'general')}
 
 ## Task
 
@@ -150,18 +155,52 @@ Perform a complete editorial review of this chapter, considering:
 - World-rule adherence
 - Voice consistency
 - Dialogue quality
+- Show vs. tell balance
 
-Assign a revision priority: major_revisions, minor_revisions, or approved
+Provide:
+1. Overall score (0.0-1.0)
+2. Strengths (at least 3)
+3. Weaknesses (at least 3)
+4. Specific revision suggestions
+5. Final verdict: major_revisions, minor_revisions, or approved
 """
 
-        return AgentResponse(
-            success=True,
-            output={
-                "status": "chapter_reviewed",
-                "chapter_number": chapter.get("chapter_number"),
-            },
-            metadata={"role": "Editor", "task": "chapter_review"},
-        )
+        try:
+            result = await self.call_llm(
+                system_prompt=self.build_system_prompt(context),
+                user_prompt=user_prompt,
+            )
+
+            # Try to extract score from result
+            score = 0.5  # default
+            for line in result.split('\n'):
+                if 'score' in line.lower() or 'rating' in line.lower():
+                    try:
+                        # Look for number
+                        import re
+                        numbers = re.findall(r'0\.\d+|\d+\.\d+', line)
+                        if numbers:
+                            score = float(numbers[0])
+                            break
+                    except:
+                        pass
+
+            return AgentResponse(
+                success=True,
+                output={
+                    "critique": result,
+                    "score": score,
+                    "chapter_number": chapter.get("chapter_number"),
+                },
+                metadata={"role": "Editor", "task": "chapter_review"},
+            )
+        except Exception as e:
+            return AgentResponse(
+                success=False,
+                output=None,
+                error=str(e),
+                metadata={"role": "Editor"},
+            )
 
     async def generate_revision_notes(
         self,
@@ -169,23 +208,38 @@ Assign a revision priority: major_revisions, minor_revisions, or approved
         context: dict[str, Any],
     ) -> AgentResponse:
         """Generate prioritized revision notes from multiple critiques."""
+        critiques_text = "\n\n".join(f"### Critique {i+1}:\n{c.get('critique', str(c))}" for i, c in enumerate(critiques))
+
         user_prompt = f"""## Critiques to Synthesize
 
-{chr(10).join(f"### Critique {i+1}:{c}" for i, c in enumerate(critiques))}
+{critiques_text}
 
 ## Task
 
 Synthesize these critiques into prioritized revision notes.
 Group by:
-1. Major revisions (structural, plot, arc issues)
-2. Minor revisions (continuity, style, pacing)
-3. Polish items (grammar, word choice)
+1. Major revisions (structural, plot, arc issues) - must fix
+2. Minor revisions (continuity, style, pacing) - should fix
+3. Polish items (grammar, word choice) - nice to fix
 
-For each item, provide specific, actionable feedback.
+For each item, provide specific, actionable feedback with location if possible.
 """
 
-        return AgentResponse(
-            success=True,
-            output={"status": "revision_notes_generated"},
-            metadata={"role": "Editor", "critique_count": len(critiques)},
-        )
+        try:
+            result = await self.call_llm(
+                system_prompt=self.build_system_prompt(context),
+                user_prompt=user_prompt,
+            )
+
+            return AgentResponse(
+                success=True,
+                output=result,
+                metadata={"role": "Editor", "critique_count": len(critiques)},
+            )
+        except Exception as e:
+            return AgentResponse(
+                success=False,
+                output=None,
+                error=str(e),
+                metadata={"role": "Editor"},
+            )

opus_orchestrator/agents/fiction/voice.py

@@ -78,17 +78,10 @@ class VoiceAgent(BaseAgent):
         )
 
     async def execute(self, input_data: Any, context: dict[str, Any]) -> AgentResponse:
-        """Execute the Voice agent's task to create style guide and samples.
-
-        Args:
-            input_data: Genre, tone, target audience
-            context: Additional context
-
-        Returns:
-            AgentResponse with style guide and prose samples
-        """
+        """Execute the Voice agent's task to create style guide and samples."""
         genre = input_data.get("genre", "general")
         tone = input_data.get("tone", "neutral")
+        target_audience = input_data.get("target_audience", "General readers")
 
         user_prompt = f"""## Task
 
@@ -96,41 +89,56 @@ Create a voice/style guide and prose samples for:
 
 - Genre: {genre}
 - Tone: {tone}
-- Target Audience: {input_data.get('target_audience', 'General readers')}
+- Target Audience: {target_audience}
 
 ## Guidelines
 
 Follow the Voice agent methodology from your system prompt.
 Include:
-- Word bank
-- Phrase patterns
-- Rhythm map
-- Tone guide
-- 3 sample scenes (opening, dialogue, descriptive)
+- Word bank (preferred vocabulary for this genre/tone)
+- Phrase patterns (recurring constructions)
+- Rhythm map (sentence length distribution)
+- Tone guide (emotional range)
+- 3 sample scenes:
+  1. Opening scene
+  2. Dialogue-heavy scene
+  3. Descriptive/pacific scene
+
+Make the samples vivid and representative of the final prose style.
 """
 
-        return AgentResponse(
-            success=True,
-            output={"status": "voice_created"},
-            metadata={"role": "Voice", "genre": genre, "tone": tone},
-        )
+        try:
+            result = await self.call_llm(
+                system_prompt=self.build_system_prompt(context),
+                user_prompt=user_prompt,
+            )
+
+            return AgentResponse(
+                success=True,
+                output=result,
+                metadata={"role": "Voice", "genre": genre, "tone": tone},
+            )
+        except Exception as e:
+            return AgentResponse(
+                success=False,
+                output=None,
+                error=str(e),
+                metadata={"role": "Voice"},
+            )
 
     async def write_chapter(
         self,
         chapter_spec: dict[str, Any],
-        style_guide: dict[str, Any],
+        style_guide: str,
         context: dict[str, Any],
     ) -> AgentResponse:
-        """Write a complete chapter following the style guide.
-
-        This is the main writing task for the Voice agent.
-        """
+        """Write a complete chapter following the style guide."""
         user_prompt = f"""## Chapter Specification
 
 - Chapter Number: {chapter_spec.get('chapter_number')}
 - Title: {chapter_spec.get('title')}
 - Summary: {chapter_spec.get('summary')}
-- Word Count Target: {chapter_spec.get('word_count_target')}
+- Word Count Target: {chapter_spec.get('word_count_target', 3000)}
 - POV Character: {chapter_spec.get('pov_character', 'Narrator')}
 - Key Events: {', '.join(chapter_spec.get('key_events', []))}
 
@@ -141,22 +149,39 @@ Include:
 ## Task
 
 Write the complete chapter following the style guide and chapter specification.
-Maintain consistent voice throughout.
+Maintain consistent voice throughout. Make it vivid, engaging, and professional quality.
+Start with the chapter title as a heading.
 """
 
-        return AgentResponse(
-            success=True,
-            output={
-                "status": "chapter_written",
-                "chapter_number": chapter_spec.get("chapter_number"),
-            },
-            metadata={"role": "Voice"},
-        )
+        try:
+            result = await self.call_llm(
+                system_prompt=self.build_system_prompt(context),
+                user_prompt=user_prompt,
+            )
+
+            word_count = len(result.split())
+
+            return AgentResponse(
+                success=True,
+                output={
+                    "content": result,
+                    "word_count": word_count,
+                    "chapter_number": chapter_spec.get("chapter_number"),
+                },
+                metadata={"role": "Voice", "word_count": word_count},
+            )
+        except Exception as e:
+            return AgentResponse(
+                success=False,
+                output=None,
+                error=str(e),
+                metadata={"role": "Voice"},
+            )
 
     async def polish_chapter(
         self,
         chapter_content: str,
-        style_guide: dict[str, Any],
+        style_guide: str,
         context: dict[str, Any],
     ) -> AgentResponse:
         """Polish an existing chapter for voice consistency."""
@@ -174,12 +199,28 @@ Polish this chapter for voice consistency. Ensure:
 - Sentence rhythm varies appropriately
 - Word choice matches the style guide
 - Tone remains consistent
-- POV is maintained
+- POV is maintained without head-hopping
 - Prose flows smoothly
+- Show don't tell where possible
+
+Return the polished chapter as your output.
 """
 
-        return AgentResponse(
-            success=True,
-            output={"status": "chapter_polished"},
-            metadata={"role": "Voice", "task": "polish"},
-        )
+        try:
+            result = await self.call_llm(
+                system_prompt=self.build_system_prompt(context),
+                user_prompt=user_prompt,
+            )
+
+            return AgentResponse(
+                success=True,
+                output=result,
+                metadata={"role": "Voice", "task": "polish"},
+            )
+        except Exception as e:
+            return AgentResponse(
+                success=False,
+                output=None,
+                error=str(e),
+                metadata={"role": "Voice"},
+            )

opus_orchestrator/agents/fiction/worldsmith.py

@@ -79,18 +79,11 @@ class WorldsmithAgent(BaseAgent):
         )
 
     async def execute(self, input_data: Any, context: dict[str, Any]) -> AgentResponse:
-        """Execute the Worldsmith's task to generate world documents.
-
-        Args:
-            input_data: Blueprint + genre + setting requirements
-            context: Additional context
-
-        Returns:
-            AgentResponse with world bible
-        """
+        """Execute the Worldsmith's task to generate world documents."""
         blueprint = input_data.get("blueprint", {})
         genre = input_data.get("genre", "fantasy")
         setting_type = input_data.get("setting_type", "fantasy")
+        raw_content = input_data.get("raw_content", "")
 
         user_prompt = f"""## Task
 
@@ -107,21 +100,31 @@ Ensure all elements are internally consistent and support the story.
 
 ## Content Seed
 
-{input_data.get('raw_content', 'No additional content provided.')}
+{raw_content if raw_content else 'Create an original world that would support a compelling story in this genre.'}
 """
 
-        return AgentResponse(
-            success=True,
-            output={
-                "status": "world_created",
-                "message": "World bible generation would be executed here with LLM",
-            },
-            metadata={
-                "role": "Worldsmith",
-                "genre": genre,
-                "setting_type": setting_type,
-            },
-        )
+        try:
+            result = await self.call_llm(
+                system_prompt=self.build_system_prompt(context),
+                user_prompt=user_prompt,
+            )
+
+            return AgentResponse(
+                success=True,
+                output=result,
+                metadata={
+                    "role": "Worldsmith",
+                    "genre": genre,
+                    "setting_type": setting_type,
+                },
+            )
+        except Exception as e:
+            return AgentResponse(
+                success=False,
+                output=None,
+                error=str(e),
+                metadata={"role": "Worldsmith"},
+            )
 
     async def expand_location(
         self,
@@ -131,33 +134,36 @@ Ensure all elements are internally consistent and support the story.
         pov_character: str,
         context: dict[str, Any],
     ) -> AgentResponse:
-        """Generate detailed location description.
-
-        From Template B in Fiction Fortress Level 2.
-        """
+        """Generate detailed location description."""
         user_prompt = f"""## Location Details
 
 - Location Name: {location_name}
-- Location Type: {context.get('location_type', 'general')}
 - Story Relevance: {story_relevance}
 - Tone Needed: {tone}
 - POV Character: {pov_character}
 
-## Sensory Requirements
-
-- Visual: {context.get('visual', 'Standard')}
-- Auditory: {context.get('auditory', 'Standard')}
-- Olfactory: {context.get('olfactory', 'Standard')}
-- Tactile: {context.get('tactile', 'Standard')}
-- Gustatory: {context.get('gustatory', 'N/A')}
-
 ## Task
 
 Generate a 300-600 word location description following the Fiction Fortress methodology.
+Include sensory details (visual, auditory, olfactory, tactile).
+Make it atmospheric and story-relevant.
 """
 
-        return AgentResponse(
-            success=True,
-            output={"status": "location_expanded"},
-            metadata={"role": "Worldsmith", "location": location_name},
-        )
+        try:
+            result = await self.call_llm(
+                system_prompt=self.build_system_prompt(context),
+                user_prompt=user_prompt,
+            )
+
+            return AgentResponse(
+                success=True,
+                output=result,
+                metadata={"role": "Worldsmith", "location": location_name},
+            )
+        except Exception as e:
+            return AgentResponse(
+                success=False,
+                output=None,
+                error=str(e),
+                metadata={"role": "Worldsmith"},
+            )

opus_orchestrator/agents/nonfiction/researcher.py

@@ -1,14 +1,16 @@
 """Nonfiction agents for Opus Orchestrator.
 
 Based on Nonfiction Fortress Level 1-3 methodology.
+All agents are wired up to call the LLM.
 """
 
-# Researcher Agent
 from typing import Any
 
 from opus_orchestrator.agents.base import AgentResponse, BaseAgent
 
 
+# ============== RESEARCHER AGENT ==============
+
 RESEARCHER_SYSTEM_PROMPT = """## Role: The Researcher
 
 You are The Researcher — responsible for information gathering, source finding, fact collection, and data mining.
@@ -36,21 +38,13 @@ You are The Researcher — responsible for information gathering, source finding
 ## Source Types and Credibility
 
 **Primary Sources**
-- Original data
-- First-hand accounts
-- Official documents
-- Expert interviews
+- Original data, First-hand accounts, Official documents, Expert interviews
 
 **Secondary Sources**
-- Academic papers
-- News reports
-- Books by experts
-- Documentaries
+- Academic papers, News reports, Books by experts, Documentaries
 
 **Tertiary Sources**
-- Encyclopedias
-- Aggregated data
-- Popular summaries
+- Encyclopedias, Aggregated data, Popular summaries
 
 ## Source Evaluation Criteria
 
@@ -61,13 +55,6 @@ You are The Researcher — responsible for information gathering, source finding
 | Recency | 20% |
 | Reproducibility | 15% |
 | Peer review | 10% |
-
-## Quality Standards
-
-- Every fact must be sourced
-- Sources must be evaluated for credibility
-- Bias must be documented
-- Contradictions must be flagged
 """
 
 
@@ -91,24 +78,31 @@ class ResearcherAgent(BaseAgent):
 
 Conduct research on: {topic}
 
-## Research Questions
-
-{chr(10).join(f"- {q}" for q in research_questions) if research_questions else "Find comprehensive information on the topic."}
+{chr(10).join(f'- {q}' for q in research_questions) if research_questions else 'Find comprehensive information on the topic.'}
 
 ## Guidelines
 
-Follow the Researcher methodology from your system prompt.
-Document all sources with citations.
+Follow the Researcher methodology. Document all sources with citations.
+Provide a comprehensive research dossier.
 """
 
-        return AgentResponse(
-            success=True,
-            output={"status": "research_complete"},
-            metadata={"role": "Researcher", "topic": topic},
-        )
+        try:
+            result = await self.call_llm(
+                system_prompt=self.build_system_prompt(context),
+                user_prompt=user_prompt,
+            )
+
+            return AgentResponse(
+                success=True,
+                output=result,
+                metadata={"role": "Researcher", "topic": topic},
+            )
+        except Exception as e:
+            return AgentResponse(success=False, output=None, error=str(e), metadata={"role": "Researcher"})
 
 
-# Analyst Agent
+# ============== ANALYST AGENT ==============
+
 ANALYST_SYSTEM_PROMPT = """## Role: The Analyst
 
 You are The Analyst — responsible for information synthesis, pattern identification, argument construction, and insight extraction.
@@ -116,22 +110,13 @@ You are The Analyst — responsible for information synthesis, pattern identific
 ## Core Responsibilities
 
 1. **Pattern Identification**
-   - Theme extraction
-   - Trend analysis
-   - Correlation discovery
-   - Anomaly detection
+   - Theme extraction, Trend analysis, Correlation discovery, Anomaly detection
 
 2. **Argument Construction**
-   - Claim development
-   - Evidence selection
-   - Reasoning flow
-   - Counterargument anticipation
+   - Claim development, Evidence selection, Reasoning flow, Counterargument anticipation
 
 3. **Insight Generation**
-   - Key takeaways
-   - Implications
-   - Connections
-   - Novel perspectives
+   - Key takeaways, Implications, Connections, Novel perspectives
 
 ## Argument Structure
 
@@ -141,29 +126,9 @@ You are The Analyst — responsible for information synthesis, pattern identific
 - **Counterargument**: Acknowledged opposition
 - **Rebuttal**: Response to opposition
 
-## Argument Types
-
-- **Causal**: A causes B
-- **Comparative**: A is better/worse than B
-- **Definition**: A means B
-- **Historical**: A led to B
-- **Predictive**: A will cause B
-
 ## Logical Fallacies to Avoid
 
-- Ad hominem
-- Straw man
-- False dilemma
-- Slippery slope
-- Circular reasoning
-- Hasty generalization
-
-## Quality Standards
-
-- All claims must be evidence-based
-- Logical fallacies must be avoided
-- Counterarguments must be addressed
-- Implications must be explored
+Ad hominem, Straw man, False dilemma, Slippery slope, Circular reasoning, Hasty generalization
 """
 
 
@@ -180,7 +145,7 @@ class AnalystAgent(BaseAgent):
 
     async def execute(self, input_data: Any, context: dict[str, Any]) -> AgentResponse:
         """Execute analysis task."""
-        research_data = input_data.get("research_data", {})
+        research_data = input_data.get("research_data", "")
         topic = input_data.get("topic", "")
 
         user_prompt = f"""## Task
@@ -197,14 +162,23 @@ Follow the Analyst methodology. Construct clear arguments with evidence.
 Address counterarguments. Generate insights.
 """
 
-        return AgentResponse(
-            success=True,
-            output={"status": "analysis_complete"},
-            metadata={"role": "Analyst", "topic": topic},
-        )
+        try:
+            result = await self.call_llm(
+                system_prompt=self.build_system_prompt(context),
+                user_prompt=user_prompt,
+            )
+
+            return AgentResponse(
+                success=True,
+                output=result,
+                metadata={"role": "Analyst", "topic": topic},
+            )
+        except Exception as e:
+            return AgentResponse(success=False, output=None, error=str(e), metadata={"role": "Analyst"})
 
 
-# Writer Agent (Nonfiction)
+# ============== WRITER AGENT ==============
+
 NONFICTION_WRITER_SYSTEM_PROMPT = """## Role: The Writer (Nonfiction)
 
 You are The Writer — responsible for prose generation, clear explanation, engaging narrative, and voice development.
@@ -212,22 +186,13 @@ You are The Writer — responsible for prose generation, clear explanation, enga
 ## Core Responsibilities
 
 1. **Prose Generation**
-   - Clear explanations
-   - Engaging narrative
-   - Accessible language
-   - Varied structure
+   - Clear explanations, Engaging narrative, Accessible language, Varied structure
 
 2. **Voice Development**
-   - Authoritative tone
-   - Expert positioning
-   - Reader engagement
-   - Credibility building
+   - Authoritative tone, Expert positioning, Reader engagement, Credibility building
 
 3. **Content Structuring**
-   - Introduction hooks
-   - Body organization
-   - Conclusion synthesis
-   - Transition flow
+   - Introduction hooks, Body organization, Conclusion synthesis, Transition flow
 
 ## Authorial Voice Elements
 
@@ -236,22 +201,6 @@ You are The Writer — responsible for prose generation, clear explanation, enga
 - **Clarity**: Accessible explanations
 - **Engagement**: Compelling narrative
 - **Credibility**: Transparent sourcing
-
-## Tone Calibration
-
-| Genre | Tone |
-|-------|------|
-| Academic | Formal, precise |
-| Popular | Accessible, lively |
-| Professional | Practical, direct |
-| Memoir | Personal, reflective |
-
-## Quality Standards
-
-- Complex ideas must be accessible
-- Arguments must flow logically
-- Voice must be consistent
-- Readers must remain engaged
 """
 
 
@@ -268,7 +217,7 @@ class NonfictionWriterAgent(BaseAgent):
 
     async def execute(self, input_data: Any, context: dict[str, Any]) -> AgentResponse:
         """Execute nonfiction writing task."""
-        analysis = input_data.get("analysis", {})
+        analysis = input_data.get("analysis", "")
         chapter_spec = input_data.get("chapter_spec", {})
 
         user_prompt = f"""## Task
@@ -277,25 +226,38 @@ Write a nonfiction chapter based on the following analysis:
 
 ## Chapter Specification
 
-{chapter_spec}
+- Title: {chapter_spec.get('title', 'Untitled')}
+- Word Count Target: {chapter_spec.get('word_count_target', 2000)}
 
-## Analysis
+## Analysis/Content
 
 {analysis}
 
 ## Guidelines
 
 Follow the Nonfiction Writer methodology. Maintain authoritative yet accessible tone.
+Structure with clear introduction, body, and conclusion.
 """
 
-        return AgentResponse(
-            success=True,
-            output={"status": "chapter_written"},
-            metadata={"role": "Nonfiction Writer"},
-        )
+        try:
+            result = await self.call_llm(
+                system_prompt=self.build_system_prompt(context),
+                user_prompt=user_prompt,
+            )
+
+            word_count = len(result.split())
+
+            return AgentResponse(
+                success=True,
+                output={"content": result, "word_count": word_count},
+                metadata={"role": "Nonfiction Writer", "word_count": word_count},
+            )
+        except Exception as e:
+            return AgentResponse(success=False, output=None, error=str(e), metadata={"role": "Nonfiction Writer"})
 
 
-# Fact Checker Agent
+# ============== FACT CHECKER AGENT ==============
+
 FACT_CHECKER_SYSTEM_PROMPT = """## Role: The Fact-Checker
 
 You are The Fact-Checker — responsible for verification, citation validation, claim verification, and accuracy audit.
@@ -303,55 +265,19 @@ You are The Fact-Checker — responsible for verification, citation validation,
 ## Core Responsibilities
 
 1. **Claim Verification**
-   - Factual accuracy checking
-   - Quote verification
-   - Data validation
-   - Source cross-referencing
+   - Factual accuracy checking, Quote verification, Data validation, Source cross-referencing
 
 2. **Citation Validation**
-   - Source credibility
-   - Citation format
-   - Attribution accuracy
-   - Access verification
+   - Source credibility, Citation format, Attribution accuracy, Access verification
 
 3. **Accuracy Audit**
-   - Comprehensive review
-   - Error identification
-   - Correction suggestions
-   - Confidence scoring
+   - Comprehensive review, Error identification, Correction suggestions, Confidence scoring
 
 ## Verification Protocol
 
-**Level 1: Self-check**
-- Re-read own claims
-- Check math and dates
-- Verify quotes
-
-**Level 2: Source verification**
-- Return to original sources
-- Confirm context
-- Check for misquotes
-
-**Level 3: External review**
-- Fact-checker agent review
-- Expert review
-- Peer review
-
-## Quality Standards
-
-| Category | Standard |
-|----------|----------|
-| Factual claims | 100% verified |
-| Quotes | Exact match |
-| Data | Source cited |
-| Attribution | Clear ownership |
-
-## Accuracy Metrics
-
-- All claims must be verifiable
-- Sources must be credible
-- Data must be accurately represented
-- Attribution must be complete
+**Level 1**: Re-read claims, check math/dates, verify quotes
+**Level 2**: Return to original sources, confirm context, check for misquotes
+**Level 3**: External review, Expert review, Peer review
 """
 
 
@@ -377,24 +303,33 @@ Fact-check the following content:
 
 {content}
 
-## Sources
+## Sources to Verify Against
 
-{chr(10).join(f"- {s}" for s in sources) if sources else "Verify against available sources."}
+{chr(10).join(f'- {s}' for s in sources) if sources else 'Verify factual claims against your knowledge.'}
 
 ## Guidelines
 
 Follow the Fact-Checker methodology. Verify all claims, quotes, and data.
-Provide confidence scores for each item.
+Provide confidence scores and flag any issues.
 """
 
-        return AgentResponse(
-            success=True,
-            output={"status": "fact_check_complete"},
-            metadata={"role": "Fact-Checker"},
-        )
+        try:
+            result = await self.call_llm(
+                system_prompt=self.build_system_prompt(context),
+                user_prompt=user_prompt,
+            )
+
+            return AgentResponse(
+                success=True,
+                output=result,
+                metadata={"role": "Fact-Checker"},
+            )
+        except Exception as e:
+            return AgentResponse(success=False, output=None, error=str(e), metadata={"role": "Fact-Checker"})
 
 
-# Nonfiction Editor Agent
+# ============== EDITOR AGENT (NONFICTION) ==============
+
 NONFICTION_EDITOR_SYSTEM_PROMPT = """## Role: The Editor (Nonfiction)
 
 You are The Editor — responsible for quality control, structure assessment, clarity evaluation, and style consistency.
@@ -402,22 +337,13 @@ You are The Editor — responsible for quality control, structure assessment, cl
 ## Core Responsibilities
 
 1. **Structure Assessment**
-   - Argument flow
-   - Chapter organization
-   - Information hierarchy
-   - Transitions
+   - Argument flow, Chapter organization, Information hierarchy, Transitions
 
 2. **Clarity Evaluation**
-   - Readability
-   - Explanatory quality
-   - Jargon usage
-   - Complex sentence identification
+   - Readability, Explanatory quality, Jargon usage, Complex sentence identification
 
 3. **Style Consistency**
-   - Tone uniformity
-   - Formatting standards
-   - Citation style
-   - Voice maintenance
+   - Tone uniformity, Formatting standards, Citation style, Voice maintenance
 
 ## Clarity Metrics
 
@@ -432,13 +358,6 @@ You are The Editor — responsible for quality control, structure assessment, cl
 - Questions raised and answered
 - Examples and stories included
 - Visual elements used appropriately
-
-## Quality Standards
-
-- Structure must support arguments
-- Clarity must enable comprehension
-- Style must maintain credibility
-- Engagement must sustain interest
 """
 
 
@@ -467,10 +386,19 @@ Perform editorial review on:
 
 Follow the Nonfiction Editor methodology.
 Assess structure, clarity, style, and engagement.
+Provide specific, actionable feedback.
 """
 
-        return AgentResponse(
-            success=True,
-            output={"status": "editorial_review_complete"},
-            metadata={"role": "Nonfiction Editor"},
-        )
+        try:
+            result = await self.call_llm(
+                system_prompt=self.build_system_prompt(context),
+                user_prompt=user_prompt,
+            )
+
+            return AgentResponse(
+                success=True,
+                output=result,
+                metadata={"role": "Nonfiction Editor"},
+            )
+        except Exception as e:
+            return AgentResponse(success=False, output=None, error=str(e), metadata={"role": "Nonfiction Editor"})