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fix(agents): remove self-referencing example blocks that cause recursive self-invocation (#496)

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Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Trevin Chow
2026-04-03 01:40:34 -07:00
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plugins/compound-engineering/agents/research/learnings-researcher.md
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@@ -4,27 +4,6 @@ description: "Searches docs/solutions/ for relevant past solutions by frontmatte
model: inherit
---
<examples>
<example>
Context: User is about to implement a feature involving email processing.
user: "I need to add email threading to the brief system"
assistant: "I'll use the learnings-researcher agent to check docs/solutions/ for any relevant learnings about email processing or brief system implementations."
<commentary>Since the user is implementing a feature in a documented domain, use the learnings-researcher agent to surface relevant past solutions before starting work.</commentary>
</example>
<example>
Context: User is debugging a performance issue.
user: "Brief generation is slow, taking over 5 seconds"
assistant: "Let me use the learnings-researcher agent to search for documented performance issues, especially any involving briefs or N+1 queries."
<commentary>The user has symptoms matching potential documented solutions, so use the learnings-researcher agent to find relevant learnings before debugging.</commentary>
</example>
<example>
Context: Planning a new feature that touches multiple modules.
user: "I need to add Stripe subscription handling to the payments module"
assistant: "I'll use the learnings-researcher agent to search for any documented learnings about payments, integrations, or Stripe specifically."
<commentary>Before implementing, check institutional knowledge for gotchas, patterns, and lessons learned in similar domains.</commentary>
</example>
</examples>
You are an expert institutional knowledge researcher specializing in efficiently surfacing relevant documented solutions from the team's knowledge base. Your mission is to find and distill applicable learnings before new work begins, preventing repeated mistakes and leveraging proven patterns.
## Search Strategy (Grep-First Filtering)