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Copy file name to clipboardExpand all lines: roadmap.md
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@@ -276,7 +276,7 @@ Architecture support is now available in all workflow modes as optional commands
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-**Description**: Auto-generate OpenAPI/JSON Schema from plan.md into `contracts/` folder to make specifications "executable" and enable automated API validation
- Manual contract creation is sufficient for current use cases
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- Can be revisited after core functionality is stable and proven in production
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-**Description**: Automated detection of spec-code misalignment in `/analyze` command to catch divergences between documented requirements and actual implementation
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-**Status**: ❌ Not planned for implementation
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-**Rationale**:
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-**Rationale**:
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- Deferred to prioritize core workflow stability
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- Manual code reviews and `/analyze` command provide sufficient validation for now
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- Requires Schema Generation (above) as prerequisite
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- Embed generated diagrams directly in architecture.md sections for integrated documentation
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- Update `/architect map` to generate diagrams when reverse-engineering from existing codebases
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- Integrate diagram validation in `/architect review` command
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-**Benefits**:
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-**Benefits**:
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- Visual communication for stakeholders who prefer diagrams over text
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- Enables architecture validation through visual inspection
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- Improves architecture documentation accessibility for non-technical audiences
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- Supports standard Mermaid syntax for version control and diffing
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-**Implementation**:
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-**Implementation**:
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- Add diagram generation logic to `scripts/bash/setup-architecture.sh` and PowerShell equivalent
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- Create template snippets for each viewpoint's Mermaid diagram structure
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- Update `templates/architecture-template.md` with diagram placeholders
Copy file name to clipboardExpand all lines: templates/commands/trace.md
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The `/trace` command generates comprehensive AI session execution traces from implementation metadata and feature artifacts. Traces include a **human-friendly Summary** (Problem → Key Decisions → Final Solution) followed by detailed technical sections for tool integration and learning.
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**Purpose**:
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-**Narrative summary** - Quick scan of what happened (Problem/Decisions/Solution)
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- Document AI agent session for reusability
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- Capture decision-making patterns and problem-solving approaches
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Run `/trace` after completing `/implement` to capture the full execution session:
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```
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```text
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/implement → tasks_meta.json created
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↓
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/trace → Generate session trace
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```
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**Best Practices**:
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- Run after all tasks complete and quality gates pass
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- Generate before `/levelup` for richer context packets
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- Re-run to update trace as understanding evolves
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The generation script automatically extracts:
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#### Summary Section (NEW)
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Human-friendly 3-part narrative placed at the top:
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**Problem**:
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- Extracted from spec mission + all user stories
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- Synthesized into 1-2 sentence goal statement
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- Example: "Implement user authentication with JWT tokens and refresh token rotation"
Copy file name to clipboardExpand all lines: templates/trace-template.md
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@@ -9,31 +9,37 @@ Branch: {BRANCH_NAME}
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## Summary
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### Problem
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{PROBLEM_STATEMENT}
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Brief description of the initial goal extracted from spec mission and all user stories. This should be 1-2 sentences summarizing what needed to be implemented.
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**Example**:
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- "Implement user authentication system with JWT tokens and refresh token rotation"
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- "Implement blood coagulation cascade with Factor IX activation and proper enzyme sequencing"
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### Key Decisions
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{NUMBERED_DECISIONS_LIST}
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Chronological list of significant decisions made during implementation. Includes architecture, technology, testing, process, and integration decisions.
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**Example**:
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1. Chose React with TypeScript for type-safe component development
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2. Implemented TDD approach with 90%+ code coverage requirement
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3. Applied dual execution loop with 5 SYNC (micro-reviewed) and 3 ASYNC (agent-delegated) tasks
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4. Integrated issue tracking with 6 @issue-tracker references for traceability
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### Final Solution
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{OUTCOME_STATEMENT}
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Brief description of what was accomplished with key metrics (quality gate pass rate, user story completion, evidence).
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**Example**:
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- "Delivered User Authentication implementation with 8/8 quality gates passed (100% pass rate). All 4 user stories implemented with comprehensive validation. Documented in commit a1b2c3d with 6 supporting issue tracker references."
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---
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Summary of AI agent approach for implementing this feature, including mission statement, key architectural decisions, and implementation strategy.
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**Example**:
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- Mission: Implement user authentication with JWT tokens
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- Key Decisions: Use bcrypt for password hashing, implement refresh token rotation
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- Approach: Test-driven development with comprehensive security testing
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Documentation of problem-solving approaches, triage decisions, and technology choices made during implementation.
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**Triage Classification**:
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- SYNC (human-reviewed) tasks: {SYNC_COUNT}
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- ASYNC (agent-delegated) tasks: {ASYNC_COUNT}
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- Total tasks: {TOTAL_TASKS}
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**Technology Choices**:
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- List key technology decisions
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- Framework selections and rationale
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- Library choices and alternatives considered
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**Problem-Solving Approaches**:
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- Dual execution loop (SYNC/ASYNC) methodology
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- Task decomposition strategy
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- Integration patterns used
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Detailed execution metadata including quality gates, review status, and MCP tracking results.
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**Quality Gates**:
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- Passed: {GATES_PASSED}
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- Failed: {GATES_FAILED}
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- Total: {GATES_TOTAL}
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- Pass Rate: {PASS_RATE}%
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**Execution Modes**:
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- SYNC tasks (micro-reviewed): {SYNC_COUNT}
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- ASYNC tasks (macro-reviewed): {ASYNC_COUNT}
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**Review Status**:
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- Micro-reviewed: {MICRO_REVIEWED}
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- Macro-reviewed: {MACRO_REVIEWED}
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**MCP Tracking** (if applicable):
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- MCP job submissions: {MCP_JOBS}
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- Completion status: {MCP_STATUS}
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Identification of effective methodologies and patterns applicable to similar contexts.
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**Effective Methodologies**:
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- ASYNC delegation: {ASYNC_SUCCESS} tasks successfully delegated and validated
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- Micro-review workflow: {MICRO_SUCCESS} tasks validated through micro-reviews
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- Testing approach: {TESTING_APPROACH}
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**Pattern Examples**:
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1.**Pattern Name**: Brief description of what worked well
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- Context: When to apply this pattern
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- Benefits: Why this approach was effective
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- Reusability: Broader applicability
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**Applicable Contexts**:
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- Similar features requiring dual execution loop
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- Projects with SYNC/ASYNC task classification
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- Spec-driven development workflows
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References to implementation artifacts, commits, issues, and code paths for traceability.
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**Implementation Commit**: {COMMIT_SHA}
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- Message: {COMMIT_MESSAGE}
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- Date: {COMMIT_DATE}
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**Issue References**:
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- {ISSUE_1}
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- {ISSUE_2}
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- ... (all @issue-tracker references from artifacts)
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