Load provenance, architecture, and memory context into a GroundedContext structure.
npx skills add https://github.com/Rwb3n/haios --skill ground-cycle
Please help me install this Agent Skill. Skill: ground-cycle Source: https://github.com/Rwb3n/haios/tree/main/.claude/skills/ground-cycle Command: npx skills add https://github.com/Rwb3n/haios --skill ground-cycle
This skill automates the loading of provenance-architecture-memory-context data to ground cognitive work before major cycles like planning, investigation, and implementation.
Invoke the skill at the start of a cycle: Skill(skill="ground-cycle", args="{work_id}")
High-intent search queries and answers about installing and using this skill.
To load provenance and architecture context before a planning cycle, invoke the ground-cycle skill with a specific work ID. It aggregates provenance-architecture-memory-context data and outputs a GroundedContext structure containing epoch, chapter, arc, provenance_chain, and architectural_refs for downstream use.
Context loading for cognitive work gathers provenance, architecture definitions, and memory references to ground AI operators. It is needed at the start of major cycles like planning, investigation, and implementation to ensure the operator has required reading and historical memory concepts loaded.
You can prepare a grounded context structure by invoking Skill(skill='ground-cycle', args='{work_id}') before starting the investigation cycle. This automatically generates a GroundedContext output with fields like provenance_chain, memory_concepts, and required_reading_loaded.
Yes, you can manually trigger context loading for an implementation cycle by calling Skill(skill='ground-cycle', args='{work_id}'). While it is designed for automatic invocation at cycle start, manual triggering ensures the GroundedContext structure with architectural_refs and memory_concepts is loaded on demand.
The GroundedContext output includes epoch, chapter, arc, provenance_chain, architectural_refs, memory_concepts, and required_reading_loaded. These fields provide the necessary architecture and memory context to ground subsequent cognitive work.
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