Preload canonical RLM evidence workflows for subagent role tasks.
npx skills add https://github.com/GrinRus/ai_driven_dev --skill aidd-rlm
Please help me install this Agent Skill. Skill: aidd-rlm Source: https://github.com/GrinRus/ai_driven_dev/tree/main/skills/aidd-rlm Command: npx skills add https://github.com/GrinRus/ai_driven_dev --skill aidd-rlm
The aidd-rlm skill provides a shared, preload-only workflow for RLM evidence that ensures subagents operate against a canonical set of evidence operations, avoiding drift between roles.
Load the shared RLM workflow for subagents and call the canonical entrypoints to perform slice, build, verify, finalize, and pack operations.
High-intent search queries and answers about installing and using this skill.
To standardize RLM evidence workflows across subagents, you preload a shared workspace using canonical Python entrypoints. This ensures agents operate against consistent slice, build, verify, finalize, and pack operations without role drift.
Managing subagent roles like analyst and planner in an automation workflow is done by preloading a canonical matrix of roles. This centralizes evidence handling and pack generation to reduce duplication across agents.
You execute slice, build, and verify tasks consistently by calling shared Python-based entrypoints like rlm_slice.py, rlm_nodes_build.py, and rlm_verify.py. These runtime scripts enforce consistent payload schemas and packaging conventions across agents.
No specific dependencies are required to run RLM finalize and pack operations. The environment operates preload-only, utilizing built-in Python entrypoints like rlm_finalize.py and reports_pack.py to handle evidence packaging.
Subagent behavior drifts when handling evidence in multi-agent cycles due to the lack of a canonical RLM evidence workflow. Preloading a shared workspace before executing tasks ensures all agents share the same evidence operations and payload schemas.
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