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"""Skill: Generate a product requirements document (PRD) / feature spec.""" |
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from video_processor.agent.skills.base import ( |
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AgentContext, |
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Artifact, |
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Skill, |
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register_skill, |
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) |
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class PRDSkill(Skill): |
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name = "prd" |
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description = "Generate a product requirements document (PRD) / feature spec" |
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def execute(self, context: AgentContext, **kwargs) -> Artifact: |
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stats = context.query_engine.stats() |
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entities = context.query_engine.entities() |
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relationships = context.query_engine.relationships() |
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relevant_types = {"requirement", "feature", "constraint"} |
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filtered = [ |
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e for e in context.planning_entities if getattr(e, "type", "").lower() in relevant_types |
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] |
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parts = [ |
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"You are a product manager. Using the following knowledge " |
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"graph context, generate a product requirements document.", |
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"", |
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"## Knowledge Graph Overview", |
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stats.to_text(), |
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"", |
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"## Entities", |
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entities.to_text(), |
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"", |
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"## Relationships", |
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relationships.to_text(), |
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"", |
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"## Relevant Planning Entities", |
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] |
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for e in filtered: |
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parts.append(f"- [{getattr(e, 'type', 'unknown')}] {e}") |
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if not filtered: |
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parts.append( |
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"(No pre-filtered entities; derive requirements from the full context above.)" |
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) |
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parts.append( |
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"\nGenerate a PRD with:\n" |
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"1. Problem Statement\n" |
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"2. User Stories\n" |
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"3. Functional Requirements\n" |
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"4. Non-Functional Requirements\n" |
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"5. Acceptance Criteria\n" |
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"6. Out of Scope\n\n" |
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"Return ONLY the markdown." |
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) |
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prompt = "\n".join(parts) |
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response = context.provider_manager.chat(messages=[{"role": "user", "content": prompt}]) |
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return Artifact( |
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name="Product Requirements Document", |
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content=response, |
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artifact_type="prd", |
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format="markdown", |
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) |
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register_skill(PRDSkill()) |
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