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horizon-track

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

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https://deepseekmodel.com/api/download.php?id=ruvnet-ruflo-plugins-ruflo-goals-skills-horizon-track-skill-md&format=skill
Download .skill Standard format with system_prompt and model_config, ready for any agent framework
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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 Initialize horizon — define the objective, target date, and 3-7 milestones 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" : [ ] } 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 Work and record — as work progresses: Update milestone status Record session summary Store intermediate findings 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 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 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
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The downloaded .skill package contains the following fields.
Field Description
formatFormat tag (skill/v1)
skill_idUnique skill ID
nameSkill name
versionVersion
descriptionDescription
categoryCategories (array)
trigger_wordsTrigger words
tagsTags
sourceSource
source_urlSource URL (this page)
exported_atExported at (set per download)
system_promptSystem prompt body
model_configModel config: provider / model / temperature / max_tokens / top_p
examplesExamples
install_guideImport guide for Coze / Dify / Claude / custom frameworks
The same skill can be exported in different platform formats.
.skill Standard format with system_prompt and model_config, ready for any agent framework Download
.skillpro Enhanced format with scripts, tools, dependencies and hooks Download
.json Plain JSON export with system_prompt and model parameters only Download
Coze Markdown with frontmatter, for Coze platform import Download
Dify Dify DSL, import directly after creating an app Download

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