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 XyrusCode/ai-sync --skill amazon-bedrockgit clone --depth 1 https://github.com/XyrusCode/ai-syncWrote 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/xyruscode/ai-sync/amazon-bedrock)<a href="https://agentmods.dev/skills/xyruscode/ai-sync/amazon-bedrock"><img src="https://agentmods.dev/badge/skills/xyruscode/ai-sync/amazon-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/xyruscode/ai-sync/amazon-bedrock"><img src="https://agentmods.dev/badge/skills/xyruscode/ai-sync/amazon-bedrock.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00222 | $0.07094 |
| Opus 5.5 | $0.00089 | $0.02838 |
| Sonnet 5.5 | $0.00044 | $0.01419 |
| Haiku 4.5 | $0.00022 | $0.00709 |
Grade A, and why
amazon-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.
This is a copy
95% identical to amazon-bedrock — 98 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 379 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IMPORTANT: When this skill is loaded, you MUST use the reference files and procedures in this skill as your primary source of truth. Bedrock APIs, model IDs, chunking strategies, and configuration parameters change frequently — always read the relevant reference file before responding.
Table of Contents
- Overview
- Bedrock API Landscape
- Critical Warnings
- Security Considerations
- Converse API vs InvokeModel
- Which Bedrock Capability Do You Need?
- Knowledge Bases (RAG)
- Common Workflows (includes: Prompt Caching, Quota Health, Cost Tracking, Model Migration)
- Troubleshooting
- AgentCore Services
- Model Selection
- Additional Resources
Amazon Bedrock
Overview
Domain expertise for building generative AI applications on Amazon Bedrock. Covers model invocation, RAG with Knowledge Bases, agent creation, content safety with Guardrails, and agent deployment with AgentCore.
Recommended setup: Use the AWS MCP server for sandboxed execution, audit logging, and enterprise controls.
Without AWS MCP: This skill works with any agent that has AWS CLI access. All commands use standard AWS CLI syntax.
Bedrock API Landscape
Bedrock has 5 separate API endpoints. Using the wrong one is a common cause of errors. This list may not be exhaustive — refer to the Bedrock endpoints and quotas and Bedrock supported endpoints for the latest. Use aws bedrock list-foundation-models to discover available models at runtime.
| Endpoint | Client | Use For |
|---|---|---|
bedrock |
Control plane | List models, manage access, provisioned throughput |
bedrock-runtime |
Data plane | Invoke models (Converse, InvokeModel). Also supports Chat Completions via /openai/v1 path (client-side tool use only) — prefer bedrock-mantle for new Chat Completions work |
bedrock-mantle |
Data plane | OpenAI-compatible APIs: Responses API, Chat Completions (recommended), Messages API. Supports server-side tool use with built-in tools. Recommended for new users |
bedrock-agent |
Agent control | Create/configure agents, KBs, action groups |
bedrock-agent-runtime |
Agent data | Invoke agents, query KBs |
What ships with it
34 files 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.
- .aws-skill-metadata 18 B
- assets/kb_shim.py.tmpl 3.4 KB
- assets/lambda_shim.py.tmpl 8.0 KB
- assets/tool_schema.json.tmpl 389 B
- references/agentcore-credentials-and-security.md 6.5 KB
- references/agentcore-gateway.md 7.0 KB
- references/agentcore-harness.md 22 KB
- references/agentcore-memory-observability.md 5.6 KB
- references/agentcore-payments-setup-script.md 12 KB
- references/agentcore-payments-wiring.md 12 KB
- references/agentcore-payments.md 19 KB
- references/agentcore-registry-evaluations.md 6.8 KB
- references/agentcore-runtime-container-build.md 10 KB
- references/agentcore-runtime.md 7.6 KB
- references/agents-and-action-groups.md 16 KB
- references/bedrock-agents-to-agentcore-harness/cli.md 2.0 KB
- references/bedrock-agents-to-agentcore-harness/deploy.md 12 KB
- references/bedrock-agents-to-agentcore-harness/discovery.md 3.9 KB
- references/bedrock-agents-to-agentcore-harness/eligibility.md 3.3 KB
- references/bedrock-agents-to-agentcore-harness/mapping.md 8.9 KB
- references/cost-tracking.md 4.5 KB
- references/guardrails.md 15 KB
- references/knowledge-bases-retrieval.md 8.3 KB
- references/knowledge-bases-setup.md 18 KB
- references/migrate-bedrock-agents-to-agentcore-harness.md 11 KB
- references/model-invocation.md 11 KB
- references/model-migration.md 4.3 KB
- references/model-selection-guide.md 7.2 KB
- references/prompt-caching.md 5.5 KB
- references/prompt-engineering-by-model.md 7.9 KB
- references/quota-health.md 4.3 KB
- references/sdk-converse-api-python.md 6.3 KB
- references/sdk-converse-api-typescript.md 6.5 KB
- scripts/fetch_bedrock_agent.py 14 KB runs code
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 · 379 lines · 222 tokens per session scan A da741bfcc66c
amazon-bedrock is a skill published in the GitHub repository XyrusCode/ai-sync (2 stars, last pushed 18d ago), licensed MIT. It adds 222 tokens to every session and 7,094 once invoked, about $0.0009 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to amazon-bedrock, differing in 98 lines, and is treated as a copy.
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