AWS Agent Skills is a collection of local instructions that help coding agents reason about AWS cloud services and engineering tasks. It supports agents working with areas such as identity, compute, storage, serverless, databases, networking, and security. The catalogue contains 18 service-focused skills and one plugin for using these capabilities with coding agents.
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 itsmostafa/aws-agent-skills --skill bedrockgit clone --depth 1 https://github.com/itsmostafa/aws-agent-skillsWrote 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/itsmostafa/aws-agent-skills/bedrock)<a href="https://agentmods.dev/skills/itsmostafa/aws-agent-skills/bedrock"><img src="https://agentmods.dev/badge/skills/itsmostafa/aws-agent-skills/bedrock/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/itsmostafa/aws-agent-skills/bedrock"><img src="https://agentmods.dev/badge/skills/itsmostafa/aws-agent-skills/bedrock.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00036 | $0.04763 |
| Opus 5.5 | $0.00014 | $0.01905 |
| Sonnet 5.5 | $0.00007 | $0.00953 |
| Haiku 4.5 | $0.00004 | $0.00476 |
Grade A, and why
bedrock 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 — 497 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AWS Bedrock
Amazon Bedrock provides access to foundation models (FMs) from AI companies through a unified API. Build generative AI applications with text generation, embeddings, and image generation capabilities.
Table of Contents
Core Concepts
Foundation Models
Pre-trained models available through Bedrock:
- Claude (Anthropic): Text generation, analysis, coding
- Nova / Titan (Amazon): Text, multimodal, embeddings
- GPT / gpt-oss (OpenAI): Text generation, reasoning
- Llama (Meta): Open-weight text generation
- Mistral: Efficient text generation
- Stable Image (Stability AI): Image generation and editing
Model Access
In commercial Regions, access to all serverless models is enabled by default (no console opt-in). In GovCloud (US), models are still enabled manually on the Model access page (third-party models also in the linked commercial account):
- First invocation of a third-party model auto-subscribes via AWS Marketplace (up to 15 min); caller needs
aws-marketplace:Subscribe,Unsubscribe,ViewSubscriptions - Anthropic models on
bedrock-runtimeneed a one-time use case form per account/org (put-use-case-for-model-access) - Invoking implies EULA acceptance; to block a model, deny both
bedrock:InvokeModelandbedrock:InvokeModelWithResponseStreamon it (SCP/IAM); streaming APIs such asConverseStreamuse the latter. Denyingaws-marketplace:Subscribealone does not block first use
Endpoints
| Endpoint | APIs | Use for |
|---|---|---|
bedrock-runtime.{region}.amazonaws.com (recommended) |
InvokeModel, Converse, Anthropic Messages (/anthropic), OpenAI Responses/Chat Completions (/openai/v1) |
Guardrails, cross-Region inference, prompt routing, application inference profiles |
bedrock-mantle.{region}.api.aws |
OpenAI Responses/Chat Completions (/openai/v1), Anthropic Messages |
Server-side tools (Web Search), background=true async, Projects/Workspaces, single-Region access to CRIS-only models |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 Changed · +103 lines f5796915438f
- 1mo ago First seen · 394 lines · 36 tokens per session scan A 2633bb2b7a51
bedrock is a skill published in the GitHub repository itsmostafa/aws-agent-skills (1,169 stars, last pushed 5d ago), licensed MIT. It adds 36 tokens to every session and 4,763 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
rag-observability-evals
Monitor and evaluate RAG systems with retrieval quality metrics, groundedness checks, hallucination detection, and continuous regression testing.
vector-database-ops
Deploy, manage, and optimize vector databases for AI applications. Covers Qdrant, Weaviate, pgvector, and Pinecone — collection management, indexing strategies, backup, and performance tuning for production RAG and semantic search workloads.
rag-infrastructure
Build and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search. Covers ingestion, chunking strategies, reranking, and production deployment patterns.
agentcore-harness-builder
Build production-ready AWS Bedrock AgentCore Harness agents end to end — declarative model + prompt, managed/BYO Memory, built-in Browser, Code Interpreter, Web Search and Knowledge Bases (RAG), Gateway/MCP tools + rate limits, inline functions, Skills (incl. AWS catalog), versioning + endpoints, advanced config…
llm-app-security
Secure LLM-powered applications with input validation, output controls, tenant isolation, and abuse prevention.
aws-bedrock-ai
WORKFLOW SKILL — Amazon Bedrock and AWS AI design: foundation model selection, knowledge bases (RAG), agents for bedrock, guardrails, provisioned throughput, batch inference, fine-tuning, KMS, VPC endpoints, regional GA, and per-provider licensing.