./planr/prd.md ./planr/tech-stack.md June 2025 capabilities Context Window: 200k Max Output Tokens: 100k ## Context for the Agent You are an autonomous AI developer with a large-context LLM. Your task is to read a Product Requirements Document and a technical stack description, then produce an optimized development roadmap that you yourself will follow to implement the application. ## Inputs - PRD file: `` - Tech-Stack file: `` - LLM context window (tokens): `` - Story-point definition: 1 story point = 1 day human effort = 1 second AI effort ## Output Required Return a roadmap in Markdown (no code fences, no bold) containing: 1. Phase 1 – Requirements Ingestion 2. Phase 2 – Development Planning (with batch list and story-point totals) 3. Phase 3 – Iterative Build steps for each batch 4. Phase 4 – Final Integration and Deployment readiness ## Operating Rules for the Agent 1. Load both input files fully before any planning. 2. Parse all user stories and record each with its story-point estimate. 3. Calculate total story points and compare to the capacity implied by ``. - If the full set fits, plan a single holistic build. - If not, create batches whose cumulative story points stay within capacity, grouping related dependencies together. 4. For every batch, plan the complete stack works: schema, backend, frontend, UX refinement, integration tests. 5. After finishing one batch, merge its code with the existing codebase and update internal context before starting the next. 6. In the final phase, perform wide-scope verification, performance tuning, documentation, and prepare for deployment. 7. Keep the development steps traceable: show which user stories appear in which batch and the cumulative story-point counts. 8. Do not use bold formatting and do not wrap the result in code fences. --- ## Template Starts Here Project: `` ### Phase 1 – Requirements Ingestion - Load `` and ``. - Summarize product vision, key user stories, constraints, and high-level architecture choices. ### Phase 2 – Development Planning - Parse all user stories. - Total story points: `` - Context window capacity: `` tokens - Batching decision: `` - Planned Batches: | Batch | Story IDs | Cumulative Story Points | |-------|-----------|-------------------------| | 1 | | | | 2 | | | | ... | ... | ... | ### Phase 3 – Iterative Build For each batch: 1. Load batch requirements and current codebase. 2. Design or update database schema. 3. Implement backend services and API endpoints. 4. Build or adjust frontend components. 5. Refine UX details and run batch-level tests. 6. Merge with main branch and update internal context. ### Phase 4 – Final Integration - Merge all batches into one cohesive codebase. - Perform end-to-end verification against all PRD requirements. - Optimize performance and resolve residual issues. - Update documentation and deployment instructions. - Declare the application deployment ready. End of roadmap. Save the generated roadmap to `./planr/roadmap.md`

AI-Optimized Development Roadmap Generator

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