Horizon Track

Track long-running objectives that span multiple sessions, days, or weeks.

When to use

When an objective is too large for a single session — multi-week features, research programs, migration projects, or any work that requires persistent progress tracking across conversations.

Steps

1. Initialize horizon — define the objective, target date, and 3-7 milestones
2. Store horizon — call mcp__plugin_ruflo-core_ruflo__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": []
}
```

1. Session check-in — at the start of each session:

• Recall horizon: mcp__plugin_ruflo-core_ruflo__memory_retrieve key horizon-[name] namespace horizons
• Review milestone status
• Assess drift (are we still on track?)
• Plan this session's contribution

1. Work and record — as work progresses:

• Update milestone status
• Record session summary
• Store intermediate findings

1. 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

1. Milestone completion — when a milestone is done:

• Verify completion criteria met
• Store learned patterns via mcp__plugin_ruflo-core_ruflo__hooks_intelligence_pattern-store
• Advance to next milestone

1. 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