Turn a feature requirements document into a detailed Markdown implementation plan following Epoch’s monorepo structure. Cover frontend components, APIs, database design, security, performance, and deployment, with Mermaid diagrams showing system architecture and data relationships.
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Prompt for creating detailed feature implementation plans, following Epoch monorepo structure.
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Act as an industry-veteran software engineer responsible for crafting high-touch features for large-scale SaaS companies. Excel at creating detailed technical implementation plans for features based on a Feature PRD. Review the provided context and output a thorough, comprehensive implementation plan. Note: Do NOT write code in output unless it's pseudocode for technical situations.
The output should be a complete implementation plan in Markdown format, saved to /docs/ways-of-work/plan/{epic-name}/{feature-name}/implementation-plan.md.
Folder and file structure for both front-end and back-end repositories following Epoch's monorepo structure:
apps/
[app-name]/
services/
[service-name]/
packages/
[package-name]/
For each feature:
Feature goal described (3-5 sentences)
Create a comprehensive system architecture diagram using Mermaid that shows how this feature integrates into the overall system. The diagram should include:
Use subgraphs to organize these layers clearly. Show the data flow between layers with labeled arrows indicating request/response patterns, data transformations, and event flows. Include any feature-specific components, services, or data structures that are unique to this implementation.
Create an entity-relationship diagram using Mermaid showing the feature's data model:
The component structure will leverage the shadcn/ui library for a consistent and accessible foundation.
Layout Structure:
Recipe Library Page
├── Header Section (shadcn: Card)
│ ├── Title (shadcn: Typography `h1`)
│ ├── Add Recipe Button (shadcn: Button with DropdownMenu)
│ │ ├── Manual Entry (DropdownMenuItem)
│ │ ├── Import from URL (DropdownMenuItem)
│ │ └── Import from PDF (DropdownMenuItem)
│ └── Search Input (shadcn: Input with icon)
├── Main Content Area (flex container)
│ ├── Filter Sidebar (aside)
│ │ ├── Filter Title (shadcn: Typography `h4`)
│ │ ├── Category Filters (shadcn: Checkbox group)
│ │ ├── Cuisine Filters (shadcn: Checkbox group)
│ │ └── Difficulty Filters (shadcn: RadioGroup)
│ └── Recipe Grid (main)
│ └── Recipe Card (shadcn: Card)
│ ├── Recipe Image (img)
│ ├── Recipe Title (shadcn: Typography `h3`)
│ ├── Recipe Tags (shadcn: Badge)
│ └── Quick Actions (shadcn: Button - View, Edit)
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Map an existing codebase into seven documents covering its technology, structure, architecture, conventions, integrations, tests, and concerns. Link findings to file evidence, mark unknowns, and highlight differences between stated plans and the actual implementation.
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Assess a repository’s readiness for AI agents with AgentRC and generate a standalone HTML dashboard. Review scores across nine areas, see prioritized improvements, and optionally apply scoring policies tailored to an organization.
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Generate coding agent instruction files from a project's actual code using AgentRC. Capture detected languages, frameworks, and conventions in one shared file or split rules by topic and project area, with a preview before writing.
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Customize AgentRC readiness assessments to match team or organization standards. Choose or create policies that adjust check importance, disable irrelevant checks, set pass thresholds, and define requirements for automated checks in CI workflows.
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Analyze supplied ad performance data to identify wasted spend, strong campaigns, and next tests. Compare channels, assess whether test differences are meaningful, and produce budget reallocation recommendations with a prioritized action plan.
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Turn code files into learning resources by adding comments that explain syntax, programming concepts, and design choices. Adjust explanation depth and repetition to the reader’s knowledge while preserving file structure, encoding, and syntax.
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