Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ihatesea69/kiro-kit --skill bedrock-raggit clone --depth 1 https://github.com/ihatesea69/kiro-kitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ihatesea69/kiro-kit/bedrock-rag)<a href="https://agentmods.dev/skills/ihatesea69/kiro-kit/bedrock-rag"><img src="https://agentmods.dev/badge/skills/ihatesea69/kiro-kit/bedrock-rag/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ihatesea69/kiro-kit/bedrock-rag"><img src="https://agentmods.dev/badge/skills/ihatesea69/kiro-kit/bedrock-rag.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Output Handling · line 135 Model output is used without validation or sanitization. Unvalidated output injected into downstream contexts (SQL, shell, HTML) enables injection attacks and arbitrary code execution.Fix: Validate and sanitize all model output before using it in downstream contexts. Use parameterized queries for SQL, shell quoting for commands, and HTML encoding for web output.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00052 | $0.01273 |
| Opus 5.5 | $0.00021 | $0.00509 |
| Sonnet 5.5 | $0.00010 | $0.00255 |
| Haiku 4.5 | $0.00005 | $0.00127 |
Grade A, and why
bedrock-rag scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG on Bedrock Knowledge Bases
Activate this skill when building retrieval-augmented answering on AWS. For the
framework-agnostic, self-hosted-vector-DB flavour, see the data-ai preset's
RAG material; this skill is the AWS-native path.
When to Use
- A chatbot must answer from internal documents with citations
- Choosing a chunking strategy, embedding model, or vector store
- Wiring retrieval into a Strands agent as a tool
- Diagnosing "the answer is wrong" in a RAG system
The Pipeline
S3 source → StartIngestionJob → chunk → embed → vector store
↓
user query → (optional rewrite) → Retrieve → rerank → prompt + context
↓
generate → citations → output guardrail → user
Most RAG failures are retrieval failures, not generation failures. Before touching the prompt, check whether the correct chunk was retrieved at all. If it was not, no amount of prompt engineering fixes it.
Chunking
The single highest-leverage config choice.
| Strategy | Use when |
|---|---|
| Fixed-size + overlap | Homogeneous prose; the safe default (~300–500 tokens, 10–20% overlap) |
| Hierarchical | Long structured documents; retrieve small, return the parent for context |
| Semantic | Topic boundaries matter more than length; costs more to ingest |
| No chunking | Documents are already short and self-contained (FAQs, tickets) |
Preserve structure in the chunk text — a heading path prepended to each chunk materially improves retrieval on technical corpora. Attach metadata (source document, section, last-modified, access tier) at ingestion; metadata filtering at query time is how you enforce per-user document access without a second index.
Retrieval and Generation
Retrieve when the agent should reason over the results:
resp = bedrock_agent_runtime.retrieve(
knowledgeBaseId=KB_ID,
retrievalQuery={"text": query},
retrievalConfiguration={"vectorSearchConfiguration": {
"numberOfResults": 10,
"overrideSearchType": "HYBRID", # semantic + keyword
"filter": {"equals": {"key": "access_tier", "value": user_tier}},
}},
)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 149 lines · 52 tokens per session scan A bf78761d7787
bedrock-rag is a skill published in the GitHub repository ihatesea69/kiro-kit (19 stars, last pushed 2mo ago), licensed MIT. It adds 52 tokens to every session and 1,273 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
molecular-rag
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llmwiki
Build and use a persistent, citation-traceable knowledge wiki from documents, research papers, notes, or project documentation. Use for llmwiki projects, retrieving reusable evidence for an agent task, or compiling a corpus for repeated questions. A one-off file read or live web search does not need a wiki.
bailian-cli
A command-line hub for Alibaba Cloud Bailian and DashScope resources. It covers applications, models, knowledge bases, usage, authentication, files, MCP services, pipelines, and installing or updating related agent skills.
bailian-kb
A command-line manager for Alibaba Cloud Bailian knowledge bases, which are collections of documents prepared for search and question answering. It handles the stored documents, search services, text chunks, and data-centre files rather than everyday searches.
building-agents
Use when building or restructuring an LLM agent — provider adapter, tool calling, structured output, RAG, agent loop, eval gate, cost routing, tracing, MCP server — model-agnostic across OpenAI/Anthropic/Gemini/OSS so a model swap is a config change. NOT vector-store SQL alone (that is postgresdb) or service…
llm-application-dev
Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.