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 zxkane/aws-skills --skill aws-agentic-aigit clone --depth 1 https://github.com/zxkane/aws-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/zxkane/aws-skills/aws-agentic-ai)<a href="https://agentmods.dev/skills/zxkane/aws-skills/aws-agentic-ai"><img src="https://agentmods.dev/badge/skills/zxkane/aws-skills/aws-agentic-ai/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/zxkane/aws-skills/aws-agentic-ai"><img src="https://agentmods.dev/badge/skills/zxkane/aws-skills/aws-agentic-ai.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
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 →
- medium Excessive Agency · line 7 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
- medium Data Exfiltration · line 19 Data is uploaded to cloud storage (S3 / GCS / Azure Blob). This may be a legitimate backup or exfiltration to an external bucket. Manual review is recommended.Fix: Verify the destination bucket is trusted and owned by you. Never upload credentials, secrets, or workspace contents to external or unverified cloud storage.
- medium Excessive Agency · line 86 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00127 | $0.02193 |
| Opus 5 | $0.00063 | $0.01097 |
| Sonnet 5 | $0.00025 | $0.00439 |
| Haiku 4.5 | $0.00013 | $0.00219 |
Grade A, and why
aws-agentic-ai 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 12d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- aws-agentic-ai — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AWS Bedrock AgentCore
AWS Bedrock AgentCore provides a complete platform for deploying and scaling AI agents with nine core services. This skill covers service selection, deployment patterns, and integration workflows using AWS CLI.
How to use this skill: Identify the service(s) the user needs from the table below, then read the corresponding service README before responding. For cross-service patterns (credentials, security, registry integration), check the Cross-Service Resources section. Verify AWS-specific details using the MCP documentation tools.
AWS Documentation Requirement
Always verify AWS facts using MCP tools before answering. Two documentation sources are available:
- AgentCore-specific docs (
mcp__acdocs__*) — bundled with this plugin, providessearch_agentcore_docsandfetch_agentcore_docfor AgentCore documentation - General AWS docs (
mcp__aws-mcp__*ormcp__*awsdocs*__*) — loaded via theaws-mcp-setupdependency for broader AWS documentation
Prefer the AgentCore docs MCP for AgentCore-specific questions. If MCP tools are unavailable, guide the user through the aws-mcp-setup skill's setup flow.
Available Services
| Service | Use For | Documentation |
|---|---|---|
| Gateway | Converting REST APIs to MCP tools | services/gateway/README.md |
| Runtime | Deploying and scaling agents | services/runtime/README.md |
| Memory | Managing conversation state | services/memory/README.md |
| Identity | Credential and access management | services/identity/README.md |
| Code Interpreter | Secure code execution in sandboxes | services/code-interpreter/README.md |
| Browser | Web automation and scraping | services/browser/README.md |
| Observability | Tracing and monitoring | services/observability/README.md |
| Agent Registry | Catalog, discover, and govern agents/tools (Preview) | services/registry/README.md |
| Evaluations | Automated agent quality assessment (LLM-as-a-Judge) | services/evaluations/README.md |
What ships with it
31 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.
- cross-service/agent-persistence-patterns.md 11 KB
- cross-service/credential-management.md 14 KB
- cross-service/registry-integration.md 12 KB
- cross-service/security-resource-policies.md 11 KB
- references/agentcore-oauth-integration.md 37 KB
- references/agentcore-runtime-core.md 58 KB
- references/agentcore-runtime-deploy.md 36 KB
- references/agentcore-runtime-protocols.md 28 KB
- scripts/a2a-server-template.py 1.3 KB runs code
- scripts/agui-server-template.py 2.2 KB runs code
- scripts/Dockerfile.runtime-template 1.2 KB
- scripts/gateway-custom-resource-lambda.py 5.6 KB runs code
- scripts/mcp-server-template.py 928 B runs code
- scripts/runtime-fastapi-template.py 4.4 KB runs code
- services/browser/README.md 10 KB
- services/code-interpreter/README.md 7.6 KB
- services/evaluations/README.md 12 KB
- services/gateway/deploy-template.sh 3.6 KB runs code
- services/gateway/deployment-strategies.md 11 KB
- services/gateway/README.md 3.3 KB
- services/gateway/troubleshooting-guide.md 10 KB
- services/gateway/validate-deployment.sh 5.8 KB runs code
- services/identity/README.md 6.7 KB
- services/memory/README.md 8.1 KB
- services/observability/README.md 14 KB
- services/registry/getting-started.md 11 KB
- services/registry/governance-workflows.md 11 KB
- services/registry/mcp-endpoint.md 8.2 KB
- services/registry/README.md 16 KB
- services/registry/sync-configuration.md 8.4 KB
- services/runtime/README.md 9.8 KB
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.
- 12d ago First seen · 150 lines · 127 tokens per session scan A b729476e3de0
aws-agentic-ai is a skill published in the GitHub repository zxkane/aws-skills (361 stars, last pushed 2mo ago), licensed MIT. It adds 127 tokens to every session and 2,193 once invoked, about $0.0006 per session on Opus 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.
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aws-cdk-mcp-server-mcp
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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.
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