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/horizon-track

Track long-horizon objectives across multiple sessions with milestone checkpoints, progress persistence, and drift detection

From plugin
claude-flow
67k200 skills157 agents194 commands1 MCP
Install
$ npx -y skills add ruvnet/claude-flow --skill horizon-track --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/horizon-track

Context preview

The summary Claude sees to decide when to auto-load this skill.

Track long-horizon objectives across multiple sessions with milestone checkpoints, progress persistence, and drift detection

SKILL.md

horizon-track.SKILL.md
name: horizon-track
description: Track long-horizon objectives across multiple sessions with milestone checkpoints, progress persistence, and drift detection
argument-hint: "<objective-name>"
allowed-tools: mcp__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__memory_list mcp__plugin_ruflo-core_ruflo__memory_retrieve mcp__plugin_ruflo-core_ruflo__task_list mcp__plugin_ruflo-core_ruflo__task_summary mcp__plugin_ruflo-core_ruflo__progress_check mcp__plugin_ruflo-core_ruflo__progress_summary mcp__plugin_ruflo-core_ruflo__agentdb_hierarchical-store mcp__plugin_ruflo-core_ruflo__agentdb_hierarchical-recall mcp__plugin_ruflo-core_ruflo__hooks_intelligence_pattern-store mcp__plugin_ruflo-core_ruflo__session_save mcp__plugin_ruflo-core_ruflo__session_restore Bash Read Write

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]`:

   {
     "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__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

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__plugin_ruflo-core_ruflo__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
Read more
Ships withclaude-flow

An agent meta-harness for Claude Code and Codex. Agent = Model + Harness. The model writes; the harness gives it tools, memory, loops, sandboxes, and controls so it can actually work.

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