Horizon Track

When to use

Steps

1. Initialize horizon — define the objective, target date, and 3-7 milestones
2. Store horizon — call

   mcp__claude-flow__memory_store

   with namespace

   horizons

   and key

   horizon-[name]

   :
   json

   {
     "objective": "...",
     "created": "2026-04-28",
     "targetDate": "2026-05-15",
     "milestones": [
       {"id": "m1", "name": "...", "criteria": "...", "status": "pending"},
       {"id": "m2", "name": "...", "criteria": "...", "status": "pending"}
     ],
     "currentMilestone": "m1",
     "sessions": []
   }

3. Session check-in — at the start of each session:
   • Recall horizon:

     mcp__claude-flow__memory_retrieve

     key

     horizon-[name]

     namespace

     horizons

   • Review milestone status
   • Assess drift (are we still on track?)
   • Plan this session's contribution
4. Work and record — as work progresses:
   • Update milestone status
   • Record session summary
   • Store intermediate findings
5. Session check-out — at the end of each session:
   • Update horizon state in memory
   • Record what was accomplished
   • Note blockers or scope changes
   • Estimate remaining effort
6. Milestone completion — when a milestone is done:
   • Verify completion criteria met
   • Store learned patterns via

     mcp__claude-flow__hooks_intelligence_pattern-store

   • Advance to next milestone
7. Drift detection — flag when:
   • Progress rate suggests target date will be missed
   • Scope has grown beyond original definition
   • Dependencies have changed
   • Approach needs fundamental rethinking

Memory namespaces

• horizons

  — active horizon definitions and state

• horizon-sessions

  — per-session summaries keyed by

  [horizon]-[date]

• horizon-learnings

  — patterns and insights discovered during the horizon