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feat: Add Kiro CLI Knowledge Base support#41

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inariku wants to merge 1 commit intoawslabs:mainfrom
inariku:feature/knowledge-base-support
Open

feat: Add Kiro CLI Knowledge Base support#41
inariku wants to merge 1 commit intoawslabs:mainfrom
inariku:feature/knowledge-base-support

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@inariku
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@inariku inariku commented Jan 17, 2026

Summary

This PR adds support for Kiro CLI's Knowledge Base feature, enabling efficient on-demand loading of AI-DLC rule details.

Problem

Currently, all rule detail files are loaded into context when using steering mode. For large rule sets, this consumes significant tokens even when only specific rules are needed.

Solution

Add a custom agent configuration (agents/ai-dlc.yaml) that:

  • Keeps core-workflow.md as always-loaded steering context
  • Indexes aws-aidlc-rule-details/ as a Knowledge Base for efficient search and retrieval

Changes

  1. agents/ai-dlc.yaml - New custom agent configuration with Knowledge Base resource
  2. aidlc-rules/aws-aidlc-rules/core-workflow.md - Added Loading Method section supporting both Steering and Knowledge Base modes
  3. README.md - Added installation instructions for Knowledge Base mode

Compatibility

Benefits

  • Token efficiency: Rule details loaded on-demand via search instead of all at once
  • Scalability: Better suited for growing rule sets
  • User choice: Users can choose between ste- Keeps core-workflow.md as always-loaded steering

By subm- Indexes aws-aidlc-rule-details/ as you can use, modify, c

Changes

  1. agents/ai-dlc.yaml - New custom agent configuratio

- Add agents/ai-dlc.yaml custom agent configuration
- Update core-workflow.md with Loading Method section for both Steering and Knowledge Base modes
- Update README.md with Knowledge Base mode installation instructions
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@dreamorosi dreamorosi left a comment

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Hi @inariku thank you for the PR.

I think this should be an opt-in add-on to the workflow rather than a mandatory item in the core workflow.

This change ties the rules too much to Kiro and I think we want customers who are using other LLM harnesses to be able to use the workflows.

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2 participants