Command: /cm-start [your objective]

Role: Workflow Orchestrator — You assess complexity, select the right workflow depth, and drive execution from objective to production code.

When this workflow is called, the AI Assistant should execute the following action sequence in the spirit of the CodyMaster Kit:

0. Load Working Memory: Per _shared/helpers.md#Load-Working-Memory
   • Update CONTINUITY.md → set Active Goal to the new objective

0.5. Skill Coverage Check (Adaptive Discovery): - Scan the objective for technologies, frameworks, or patterns mentioned - Cross-reference with cm-skill-index Layer 1 triggers - If gap detected → trigger Discovery Loop from cm-skill-mastery Part C: npx skills find "{keyword}" → review → ask user → install if approved - Log any discovered skills to .cm-skills-log.json

0.7. Code Intelligence Setup (cm-codeintell): - ALWAYS: Run skeleton indexer → bash scripts/index-codebase.sh → .cm/skeleton.md - Read .cm/skeleton.md (~5K tokens) → instant codebase understanding - Count source files → determine intelligence level (MINIMAL/LITE/STANDARD/FULL) - IF level >= LITE: generate architecture diagram → .cm/architecture.mmd - IF level >= STANDARD: check CodeGraph → codegraph status → index if needed - IF level >= FULL: also check qmd (cm-deep-search) - Log intelligence level to CONTINUITY.md

1. Understand Requirements (Planning & JTBD):

   • Read the objective provided in the /cm-start command.
   • Analyze requirements, ask clarifying questions if needed (apply cm-planning).
   • Consider multi-language support (i18n) from the start if the project requires it.

2. Detect Project Level: Per _shared/helpers.md#Project-Level-Detection

   • Analyze the objective to determine L0/L1/L2/L3 complexity
   • Present detected level and recommended skill chain to the user
   • Let user confirm or override the level

3. Execute Based on Level:

   L0 (Micro): Code + Test only

   • Skip planning. Apply cm-tdd directly → cm-quality-gate

   L1 (Small): Planning lite → Code → Deploy

   • Apply cm-planning (lightweight implementation plan)
   • Apply cm-tdd + cm-execution → cm-quality-gate

   L2 (Medium): Full analysis flow

   • Apply cm-brainstorm-idea if problem is ambiguous
   • Apply cm-planning (full implementation plan with task breakdown)
   • Create cm-tasks.json → launch RARV autonomous execution
   • Apply cm-quality-gate → cm-safe-deploy

   L3 (Large): Full + PRD + Architecture + Sprint

   • Apply cm-brainstorm-idea (mandatory)
   • Apply cm-planning with FR/NFR requirement tracing
   • Sprint planning → cm-tasks.json with sprints
   • Apply cm-execution (Mode E: TRIZ-Parallel for speed)
   • Apply cm-quality-gate → cm-safe-deploy

4. Track Progress:

   • Create or update cm-tasks.json by breaking the objective into specific tasks
   • Suggest /cm-dashboard for visual tracking
   • Suggest /cm-status for quick terminal summary

5. Complete: Per _shared/helpers.md#Update-Continuity

   • Record any new learnings or decisions made during this workflow

Note for AI: If this is a brand new project, suggest running cm-project-bootstrap first. If the working environment has a risk of accidentally switching accounts/projects, remind about cm-identity-guard (Per _shared/helpers.md#Identity-Check).