How do I install the Bedrock RAG skill?

Run npx skillmds@latest add sahit-sai/bedrock-rag in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Bedrock RAG skill do?

Indexes local markdown into a Bedrock-backed retrieval workflow. It is listed under AI & ML on SkillMD.

Is Bedrock RAG safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Bedrock RAG?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Bedrock RAG free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Bedrock RAG?

sahit-sai (@sahit-sai) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Bedrock RAG by sahit-sai — Agent Skill

Name: Bedrock RAG
Author: sahit-sai

File contents

bedrock-rag

Purpose

Chunks local markdown corpora and syncs them into an Amazon Bedrock Knowledge Base for semantic retrieval.

Runbook

Verify aws, python3, and jq are in PATH, then validate credentials with aws sts get-caller-identity --no-cli-pager.
Confirm BEDROCK_KB_ID and BEDROCK_MODEL_ID are set, and ensure AWS_REGION matches the Bedrock region hosting the knowledge base.
Run scripts/chunk.sh SOURCE_DIR to split all .md files into roughly 500-word JSONL chunks containing chunk_id, source_file, and content.
Review the chunk count before indexing; abort if the chunk count exceeds 1000 unless FORCE=1 is explicitly set.
Run scripts/index.sh CHUNKS_PATH to sync the chunk stream into the Bedrock Knowledge Base. The script ingests documents and then triggers a Bedrock ingestion job for auditable sync status.
Poll aws bedrock-agent get-ingestion-job until the job reports COMPLETE; abort on FAILED or STOPPED.
Run scripts/query.sh "your question" to verify semantic retrieval and inspect the returned citations.

Stop conditions

Abort if aws sts get-caller-identity fails.
Abort if chunk count exceeds 1000 unless FORCE=1.
Never delete existing knowledge-base contents — only add or update documents.
Abort if the Bedrock ingestion job returns FAILED or STOPPED.

Output format

{
  "chunks_synced": 128,
  "kb_id": "KB12345678",
  "model_id": "anthropic.claude-3-sonnet-20240229-v1:0",
  "sync_status": "COMPLETE"
}

Example invocations

skills/bedrock-rag/scripts/chunk.sh docs > docs/.bedrock-rag.chunks.jsonl
skills/bedrock-rag/scripts/index.sh docs/.bedrock-rag.chunks.jsonl
skills/bedrock-rag/scripts/query.sh "What deployment steps are documented?"
skills/bedrock-rag/scripts/run.sh docs --query "What changed in the onboarding guide?"

---name: bedrock-ragdescription: Indexes local markdown into a Bedrock-backed retrieval workflow.---# bedrock-rag## PurposeChunks local markdown corpora and syncs them into an Amazon Bedrock Knowledge Base for semantic retrieval.## Runbook1. Verify `aws`, `python3`, and `jq` are in PATH, then validate credentials with `aws sts get-caller-identity --no-cli-pager`.2. Confirm `BEDROCK_KB_ID` and `BEDROCK_MODEL_ID` are set, and ensure `AWS_REGION` matches the Bedrock region hosting the knowledge base.3. Run `scripts/chunk.sh SOURCE_DIR` to split all `.md` files into roughly 500-word JSONL chunks containing `chunk_id`, `source_file`, and `content`.4. Review the chunk count before indexing; abort if the chunk count exceeds `1000` unless `FORCE=1` is explicitly set.5. Run `scripts/index.sh CHUNKS_PATH` to sync the chunk stream into the Bedrock Knowledge Base. The script ingests documents and then triggers a Bedrock ingestion job for auditable sync status.6. Poll `aws bedrock-agent get-ingestion-job` until the job reports `COMPLETE`; abort on `FAILED` or `STOPPED`.7. Run `scripts/query.sh "your question"` to verify semantic retrieval and inspect the returned citations.## Stop conditions1. Abort if `aws sts get-caller-identity` fails.2. Abort if chunk count exceeds `1000` unless `FORCE=1`.3. Never delete existing knowledge-base contents — only add or update documents.4. Abort if the Bedrock ingestion job returns `FAILED` or `STOPPED`.## Output format```json{ "chunks_synced": 128, "kb_id": "KB12345678", "model_id": "anthropic.claude-3-sonnet-20240229-v1:0", "sync_status": "COMPLETE"}```## Example invocations- `skills/bedrock-rag/scripts/chunk.sh docs > docs/.bedrock-rag.chunks.jsonl`- `skills/bedrock-rag/scripts/index.sh docs/.bedrock-rag.chunks.jsonl`- `skills/bedrock-rag/scripts/query.sh "What deployment steps are documented?"`- `skills/bedrock-rag/scripts/run.sh docs --query "What changed in the onboarding guide?"`

Frequently asked questions

Bedrock RAG