Command: /cm-start [your objective]

TL;DR

• Use to kick off a CM session — entry point
• Detects: stack (Phase 2), suggests skills, reads continuity + learnings
• Next: cm-brainstorm-idea or cm-planning

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:

1. Load Working Memory:

Per _shared/helpers.md#Load-Working-Memory — use Smart Spine order:

1. Check .cm/context-bus.json → any active pipeline? any prior skill output to reuse?
2. Load L0 indexes: learnings-index.md (~100 tok) + skeleton-index.md (~500 tok)
3. Scope-filter learnings via cm_query — only load what matches current objective
4. Read CONTINUITY.md → set Active Goal to the new objective
5. Run token budget check: cm continuity budget → confirm no category is over soft limit

⚡ Total context load: ~700 tokens. Full load used to be ~3,200. Only escalate to L2 (full files) if L0 index explicitly flags a match.

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

0.6. Stack & Tier Detection (Phase 2):

• cm stack detect --write → writes .cm/project-skills.md (frameworks + suggested skills)
• cm tier classify --write → writes .cm/project-tier.md (LITE/STANDARD/PROFESSIONAL/ENTERPRISE)
• The tier sets the default Vibecoding mode and adaptive depth:
• LITE/STANDARD → render skill TL;DR only
• PROFESSIONAL/ENTERPRISE → render full protocol
• Inject the suggested-skills list into the skill chain shortlist
• These reports are token-light (~300 tok combined) and skipped if files exist and are <24h old

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 >= STANDARD: also check qmd (cm-deep-search) for existing semantic vector databases and initialize/update if needed.
• 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.

1. 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

1. 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

• Init OpenSpec (create openspec/changes/[initiative-name]/ folder and artifacts manually)
• Apply cm-brainstorm-idea if problem is ambiguous
• Apply cm-planning (full implementation plan with OpenSpec tasks.md)
• Create cm-tasks.json from tasks.md → launch RARV autonomous execution
• Apply cm-quality-gate → cm-safe-deploy

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

• Init OpenSpec (create openspec/changes/[initiative-name]/ folder and artifacts manually)
• Apply cm-brainstorm-idea (mandatory)
• Apply cm-planning with FR/NFR requirement tracing
• Sprint planning → openspec/changes/[objective]/tasks.md sync with cm-tasks.json
• Apply cm-execution (Mode E: TRIZ-Parallel for speed)
• Apply cm-quality-gate → cm-safe-deploy

1. Track Progress:

• Create openspec/changes/[objective]/tasks.md (for standardized spec tracking)
• Create or update cm-tasks.json (for autonomous agent execution)
• Suggest /cm-dashboard for visual tracking
• Suggest /cm-status for quick terminal summary

1. Complete:

Per _shared/helpers.md#Update-Continuity

• Record any new learnings or decisions made during this workflow
• If inside a skill chain: cm continuity bus → verify context bus reflects completed step
• Refresh L0 indexes: cm continuity index (auto-runs on addLearning, manual refresh here)

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).