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docs: Consolidate extension documentation and fix /spec.trace references
- Remove non-existent /spec.trace from core workflow
- Move trace references to /levelup.trace (where it actually exists)
- Remove duplicate Architect extension overview from Get Started section
- Add missing commands tables for Product and Quick extensions
- Replace duplicate Extension Commands section with link to Extensions
- Update Core Features table to reflect LevelUp as the knowledge extraction phase
Copy file name to clipboardExpand all lines: README.md
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@@ -267,36 +267,9 @@ team-ai-directives/
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-`plan.md`: AI refresh section before implementation
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-`context-template.md`: Structured placeholders for DISCOVERED_DIRECTIVES/DISCOVERED_SKILLS
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### Optional Architecture Support
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### Architecture Support
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The toolkit includes comprehensive architecture documentation support via the **Architect extension**, which creates and manages Architecture Decision Records (ADRs) and Architecture Descriptions (AD.md) using the Rozanski & Woods methodology.
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> **Note**: The Architect extension is bundled and auto-installed during `specify init`.
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**Key Features:**
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-**Two-level architecture** - System-level ADRs on main branch, feature-level ADRs on feature branches
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-**Automatic integration** - Hooks create feature ADRs during `/spec.plan` and validate alignment
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-**Greenfield & Brownfield** - `/architect.specify` for new projects, `/architect.init` for existing codebases
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**Quick Start:**
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```bash
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# Greenfield: Create ADRs from PRD
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/architect.specify "B2B SaaS platform for real-time supply chain management"
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# Brownfield: Reverse-engineer ADRs from code
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/architect.init "Django monolith with PostgreSQL, React frontend"
The toolkit includes architecture documentation support via the **Architect extension** (creates ADRs and Architecture Descriptions). Bundled and auto-installed during `specify init`. See the [Extensions section](#-extensions) for details.
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### Framework Options
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@@ -386,33 +359,26 @@ Use **`/spec.implement`** to execute all tasks and build your feature according
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/spec.implement
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```
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### 7. Generate session trace (optional)
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Use **`/spec.trace`** to generate comprehensive AI session execution traces capturing decisions, patterns, and outcomes.
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```bash
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/spec.trace
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```
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**Benefits**: Session traces document AI agent decision-making, execution context, quality gates, and reusable patterns. Stored in `specs/{BRANCH}/trace.md` with your feature artifacts. Optional but enriches `/levelup.specify` CDR extraction when present.
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### 7. Level up and contribute knowledge (optional)
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### 8. Level up and contribute knowledge
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Use the **levelup extension** to extract patterns from your completed feature and contribute reusable knowledge back to your team's shared repository via Context Decision Records (CDRs).
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Use the **LevelUp extension** to extract patterns from your completed feature and contribute reusable knowledge back to your team's shared repository via Context Decision Records (CDRs).
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```bash
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/levelup.specify # Extract CDRs from current feature spec (after /implement)
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/levelup.clarify # Resolve ambiguities in discovered CDRs
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/levelup.clarify # Resolve ambiguities in discovered CDRs
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/levelup.skills python-patterns # Build a skill from accepted CDRs
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/levelup.implement # Create PR to team-ai-directives
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/levelup.implement # Create PR to team-ai-directives
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/levelup.trace # Generate AI session execution traces
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```
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For brownfield projects, use `/levelup.init` to scan the entire codebase for patterns.
Repeat steps 3-7 for each new feature. The cycle of specification → planning → implementation → knowledge extraction continuously improves your team capabilities.
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For detailed step-by-step instructions, see our [comprehensive guide](./spec-driven.md).
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## 📽️ Video Overview
@@ -446,7 +412,7 @@ The core of Agentic SDLC Spec Kit is a structured workflow that guides AI-assist
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