name: "weekly-learning-loop" description: "Make analysis improvements stick by loading agent identity, persisting learnings, and reinjecting them into the next cycle." domain: "analysis-operations" confidence: "high"

#source: "recurring learnings in Bender, Farnsworth, Hermes, and Leela histories"

#Context

A weekly AI workflow only improves if lessons survive the run that produced them. The loop is incomplete when learnings are written down but never loaded back into the next prompt.

#Patterns

• Load the correct agent identity before analysis or reskill work begins.
• Persist durable outcomes in history, wisdom, or skill files as part of the same operating cycle.
• Inject shared wisdom and skills into the next prompt so the model can act on prior learning.
• Keep fallback paths aligned with the same prompt contract so learnings apply across execution modes.

#Examples

• Good: call Copilot CLI with the registered agent name, not a file path.
• Good: store a repeatable lesson in .squad/skills/ once it shows up across multiple agents.
• Good: render prompts with shared wisdom and skill context so new runs inherit the last run's conclusions.

#Anti-Patterns

• Treating a post-run note as sufficient when the next run never reads it.
• Letting the fallback path drift to a different output contract.
• Keeping important learnings only in a single dated history entry.