AWS Strands Agents Agentcore
sammcj/agentic-coding
A skill your agent uses when working with AWS Strands Agents SDK or Amazon Bedrock AgentCore platform for building AI agents.
AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale.
Install AWS Agentic AI
$ npx skills add zxkane/aws-skills --skill aws-agentic-ai -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install zxkane/aws-skills aws-agentic-ai --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/zxkane/aws-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/aws-agentic-ai/skills/aws-agentic-ai .claude/skills/aws-agentic-ai && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "aws-agentic-ai" agent skill from https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai into .claude/skills/aws-agentic-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-agentic-ai", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-aiType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add zxkane/aws-skills --skill aws-agentic-ai -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install zxkane/aws-skills aws-agentic-ai --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zxkane/aws-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/aws-agentic-ai/skills/aws-agentic-ai .agents/skills/aws-agentic-ai && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "aws-agentic-ai" agent skill from https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai into .agents/skills/aws-agentic-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-agentic-ai", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add zxkane/aws-skills --skill aws-agentic-ai -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install zxkane/aws-skills aws-agentic-ai --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zxkane/aws-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/aws-agentic-ai/skills/aws-agentic-ai .cursor/skills/aws-agentic-ai && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "aws-agentic-ai" agent skill from https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai into .cursor/skills/aws-agentic-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-agentic-ai", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/zxkane/aws-skills.git --path plugins/aws-agentic-ai/skills/aws-agentic-ai--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add zxkane/aws-skills --skill aws-agentic-ai -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install zxkane/aws-skills aws-agentic-ai --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zxkane/aws-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/aws-agentic-ai/skills/aws-agentic-ai .gemini/skills/aws-agentic-ai && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "aws-agentic-ai" agent skill from https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai into .gemini/skills/aws-agentic-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-agentic-ai", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install zxkane/aws-skills aws-agentic-aiInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add zxkane/aws-skills --skill aws-agentic-ai -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/zxkane/aws-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/aws-agentic-ai/skills/aws-agentic-ai .github/skills/aws-agentic-ai && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "aws-agentic-ai" agent skill from https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai into .github/skills/aws-agentic-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-agentic-ai", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add zxkane/aws-skills --skill aws-agentic-ai -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install zxkane/aws-skills aws-agentic-ai --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zxkane/aws-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/aws-agentic-ai/skills/aws-agentic-ai .opencode/skills/aws-agentic-ai && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "aws-agentic-ai" agent skill from https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai into .opencode/skills/aws-agentic-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-agentic-ai", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
aws-agentic-ai- GitHub stars
- 367
- Token cost
- ~2.5k tokens
- SKILL.md length
- 803 words
- Files
- 40 (incl. scripts, references)
- Skills in repo
- 6
- Repo updated
- First seen
- Licence
- MIT
At a glance
AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale.
- Works in 4 steps: Upload OpenAPI schema to S3 → (API Key auth only) Create credential… → Create gateway target linking schema… → …
- Working with any AgentCore service including Gateway
- SKILL.md covers AWS Documentation Requirement, Available Services, Common Workflows and Deep-Dive References, plus 2 more sections
- Runs Python scripts from its folder
What it does
AWS Agentic AI is an agent skill from zxkane/aws-skills. AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. Use when working with any AgentCore service including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, or Evaluations. Covers agent deployment, MCP tool integration, credential management, agent discovery, governance workflows, and automated quality assessment. Essential when user mentions AgentCore, agent runtime, agent registry, agent evaluation, MCP gateway, deploy agent…
Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 44 other files, including scripts and reference files (for example `cross-service/agent-persistence-patterns.md`, `cross-service/credential-management.md` and `cross-service/registry-integration.md`).
It sits in Agent Workflows, covering MCP servers, Agent evaluation and testing and Observability. It works with Amazon Web Services, Model Context Protocol and Amazon Bedrock. The repository describes itself as: Claude Code plugins and agent skills for AWS development — IaC(CDK/SST), serverless, cost ops, and Bedrock AgentCore. The licence is MIT.
When your agent uses it
- Working with any AgentCore service including Gateway
- Code Interpreter
- Mentions AgentCore
- Agent evaluation
Example prompts
- “/aws-agentic-ai”
Requirements
- Python 3
- Docker
- Pre-approved tools (allowed-tools): mcp__aws-mcp__*, mcp__awsdocs__*, mcp__acdocs__search_agentcore_docs, mcp__acdocs__fetch_agentcore_doc, Bash(aws bedrock-agentcore *), Bash(aws bedrock-agentcore-control *), Bash(aws bedrock-agentcore-runtime *), Bash(aws bedrock *), Bash(aws s3 cp *), Bash(aws s3 ls *), Bash(aws secretsmanager *), Bash(aws sts get-caller-identity)
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Upload OpenAPI schema to S3
- (API Key auth only) Create credential provider and store API key
- Create gateway target linking schema (and credentials if using API key)
- Verify target status and test connectivity
What it can do on your machine
Read from SKILL.md and the folder at commit 68530c6. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
mcp__aws-mcp__*mcp__awsdocs__*mcp__acdocs__search_agentcore_docsmcp__acdocs__fetch_agentcore_docBash(aws bedrock-agentcore *)Bash(aws bedrock-agentcore-control *)Bash(aws bedrock-agentcore-runtime *)Bash(aws bedrock *)Bash(aws s3 cp *)Bash(aws s3 ls *)
…and 2 more on the same allowed-tools line.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 6 files in scripts/ (Python, from the files we listed), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
docs.aws.amazon.comawscli.amazonaws.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
AWS Agentic AI loads about 2.5k tokens when it runs, and up to ~43k if it reads all its reference files. Until then it costs about 168 tokens; SKILL.md has 803 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from zxkane/aws-skills at commit 68530c6, republished under its MIT licence (© zxkane). 803 words, ~2,515 tokens.
Download SKILL.mdSave it as
.claude/skills/aws-agentic-ai/SKILL.md (or your agent's skills folder). This skill also uses 39 other files; get the full folder from GitHub.- name
- aws-agentic-ai
- description
- AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. Use when working with any AgentCore service including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, or Evaluations. Covers agent deployment, MCP tool integration, credential management, agent discovery, governance workflows, and automated quality assessment. Essential when user mentions AgentCore, agent runtime, agent registry, agent evaluation, MCP gateway, deploy agent, register MCP server, discover agents, evaluate agent quality, agent credentials, or wants to build, deploy, catalog, or monitor AI agents on AWS.
- allowed-tools
- mcp__aws-mcp__*, mcp__awsdocs__*, mcp__acdocs__search_agentcore_docs, mcp__acdocs__fetch_agentcore_doc, Bash(aws bedrock-agentcore *), Bash(aws bedrock-agentcore-control *), Bash(aws bedrock-agentcore-runtime *), Bash(aws bedrock *), Bash(aws s3 cp *), Bash(aws s3 ls *), Bash(aws secretsmanager *), Bash(aws sts get-caller-identity)
- aliases
- bedrock-agentcore
- context
- fork
- model
- sonnet
- skills
- aws-mcp-setup
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 |
Common Workflows
Deploying a Gateway Target
Read services/gateway/README.md before implementing — Gateway setup involves deployment strategies, IAM, and auth choices that vary significantly by use case.
- Upload OpenAPI schema to S3
- (API Key auth only) Create credential provider and store API key
- Create gateway target linking schema (and credentials if using API key)
- Verify target status and test connectivity
Credential provider is only needed for API key authentication. Lambda targets use IAM roles, and MCP servers use OAuth.
Managing Credentials
Read cross-service/credential-management.md first — credential patterns differ across services and getting them wrong causes hard-to-debug auth failures.
- Use Identity service credential providers for all API keys
- Link providers to gateway targets via ARN references
- Rotate credentials quarterly through credential provider updates
- Monitor usage with CloudWatch metrics
Discovering Agents and Tools (Agent Registry)
Read services/registry/README.md first — the registry has governance workflows, MCP endpoint options, and sync modes that affect how records become discoverable.
- Create a registry to catalog your organization's AI resources
- Register resources (MCP servers, agents, skills, custom) with descriptive metadata
- Submit records for approval (auto-approve for dev, manual for production)
- Search and discover approved resources via CLI or MCP endpoint
Agent Registry is in Preview. Available in us-east-1, us-west-2, eu-west-1, ap-northeast-1, ap-southeast-2.
Evaluating Agent Quality
Read services/evaluations/README.md first — evaluators, scoring modes, and IAM setup vary between online monitoring and on-demand testing.
- Instrument the agent with OpenTelemetry (ADOT) for trace collection
- Create evaluators (use built-in like
Builtin.Helpfulnessor create custom) - Set up online evaluation with sampling rate and data source
- Monitor scores in CloudWatch dashboards; investigate low-scoring sessions
Show full SKILL.md (321 more words)Show less
Monitoring Agents
Read services/observability/README.md for the full monitoring setup — observability configuration depends on your Runtime protocol and framework choice.
- Enable observability for agents
- Configure CloudWatch dashboards for metrics
- Set up alarms for error rates and latency
- Use X-Ray for distributed tracing
Deep-Dive References
Each service README (linked in the table above) contains sub-links to getting-started guides, troubleshooting, and advanced topics. Start with the service README and follow pointers from there.
Advanced Runtime & OAuth References
Deep-dive reference documentation for Runtime internals, deployment, OAuth integration, and communication protocols. Read these when building production Runtime deployments or configuring OAuth authentication:
- OAuth Integration:
references/agentcore-oauth-integration.md- Three-layer OAuth architecture (Inbound JWT, Outbound Credential Provider, Gateway OAuth), Cognito configuration, supported IdPs, end-to-end CDK examples - Runtime Core Mechanisms:
references/agentcore-runtime-core.md- Container contract, MicroVM Session model, Agent lifecycle (per-request vs per-session), tool integration (MCP/HTTP), startup flow - Runtime Deployment & Operations:
references/agentcore-runtime-deploy.md- CDK deployment (L1/L2 constructs), multi-Runtime architecture, security model, observability (OTel/CloudWatch), BedrockAgentCoreApp vs FastAPI comparison - Runtime Protocol Reference:
references/agentcore-runtime-protocols.md- HTTP, MCP, A2A, AG-UI protocol specifications with container contracts, endpoint specs, and selection guide
Runnable Script Templates
Production-ready templates in scripts/ for common deployment patterns:
| Script | Protocol | Description |
|---|---|---|
Dockerfile.runtime-template | — | ARM64 multi-stage Docker build for AgentCore Runtime |
runtime-fastapi-template.py | HTTP | FastAPI Runtime with SSE streaming and MCPClient |
mcp-server-template.py | MCP | MCP Server with Streamable HTTP transport |
a2a-server-template.py | A2A | A2A Server with Agent Card discovery |
agui-server-template.py | AG-UI | AG-UI Server with standard AG-UI event stream |
gateway-custom-resource-lambda.py | — | CDK Custom Resource Lambda for Gateway lifecycle |
Cross-Service Resources
For patterns and best practices that span multiple AgentCore services:
- Credential Management:
cross-service/credential-management.md- Unified credential patterns, security practices, rotation procedures - Registry Integration:
cross-service/registry-integration.md- Cross-service patterns with Gateway, Identity, Runtime - Security & Resource Policies:
cross-service/security-resource-policies.md- Resource-based policies, cross-account access, VPC/IP restrictions - Agent Deployment with S3 Files:
cross-service/agent-persistence-patterns.md- Deploy Strands Agents, OpenClaw, Claude Agent SDK on AgentCore with S3 Files and Session Storage
Additional Resources
- AWS Documentation: Amazon Bedrock AgentCore
- API Reference: Bedrock AgentCore Control Plane API
- AWS CLI Reference: bedrock-agentcore-control commands
© zxkane, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 39 other files (scripts, references) in plugins/aws-agentic-ai/skills/aws-agentic-ai of zxkane/aws-skills.
- SKILL.md
- cross-service/agent-persistence-patterns.md
- cross-service/credential-management.md
- cross-service/registry-integration.md
- cross-service/security-resource-policies.md
- references/agentcore-oauth-integration.md
- references/agentcore-runtime-core.md
- references/agentcore-runtime-deploy.md
- references/agentcore-runtime-protocols.md
- scripts/Dockerfile.runtime-template
- scripts/a2a-server-template.py
- scripts/agui-server-template.py
- scripts/gateway-custom-resource-lambda.py
- scripts/mcp-server-template.py
- scripts/runtime-fastapi-template.py
- services/browser/README.md
- … and 24 more
Open the folder on GitHubat commit 68530c6
Compare with similar skills
AWS Agentic AI next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| AWS Agentic AI this skillzxkane/aws-skills | 367 | — | ~2.5k | Automated safety check: Pass | MIT | |
| AWS Strands Agents Agentcoresammcj/agentic-coding | 162 | — | ~3k | Automated safety check: Pass | Apache-2.0 | |
| Hcls Deploy Agentaws-samples/amazon-bedrock-agents-healthcare-lifesciences | 274 | — | ~813 | Automated safety check: Pass | MIT-0 | |
| AWS Agentic AICommandCodeAI/agent-skills | 133 | — | ~1.5k | Automated safety check: Pass | MIT | |
| Deploy Observabilityaliyun/alibabacloud-observability-mcp-server | 166 | — | ~2.6k | Automated safety check: Notes | None | |
| AWS Agentic AIsickn33/agentic-awesome-skills | 47k | 1 repos | ~3.2k | Automated safety check: Pass | MIT |
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Questions about AWS Agentic AI
What does AWS Agentic AI do?
AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. AWS Agentic AI is an agent skill from zxkane/aws-skills. AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale.
When should I use AWS Agentic AI?
AWS Agentic AI fits situations like: working with any AgentCore service including Gateway; code Interpreter; mentions AgentCore; agent evaluation.
How do I install AWS Agentic AI in Claude Code?
Run `npx skills add zxkane/aws-skills --skill aws-agentic-ai -a claude-code`. Or copy the skill folder (plugins/aws-agentic-ai/skills/aws-agentic-ai in zxkane/aws-skills) into .claude/skills/aws-agentic-ai in your project. Claude Code loads it when a task matches its description.
How do I install AWS Agentic AI in Codex?
Run `npx skills add zxkane/aws-skills --skill aws-agentic-ai -a codex`. Or copy the skill folder (plugins/aws-agentic-ai/skills/aws-agentic-ai in zxkane/aws-skills) into .agents/skills/aws-agentic-ai in your project. Codex loads it when a task matches its description.
Can I use AWS Agentic AI in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add zxkane/aws-skills --skill aws-agentic-ai -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/aws-agentic-ai, .gemini/skills/aws-agentic-ai, .github/skills/aws-agentic-ai and .opencode/skills/aws-agentic-ai in your project.
What does AWS Agentic AI need to run?
Going by SKILL.md and its folder, AWS Agentic AI needs Python for the scripts in its folder. Our summary lists: Python 3; Docker. Its frontmatter pre-approves these tools: mcp__aws-mcp__*, mcp__awsdocs__*, mcp__acdocs__search_agentcore_docs, mcp__acdocs__fetch_agentcore_doc, Bash(aws bedrock-agentcore *), Bash(aws bedrock-agentcore-control *), Bash(aws bedrock-agentcore-runtime *), Bash(aws bedrock *), Bash(aws s3 cp *), Bash(aws s3 ls *), Bash(aws secretsmanager *), Bash(aws sts get-caller-identity).
Does AWS Agentic AI access the network?
SKILL.md names 2 domains. As links in the text: docs.aws.amazon.com and awscli.amazonaws.com. This is read from the text; nothing was executed.
Is AWS Agentic AI safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does AWS Agentic AI use?
AWS Agentic AI is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does AWS Agentic AI use?
About 2.5k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 41k tokens, read only when the agent opens those files.
What are the alternatives to AWS Agentic AI?
Skills that share tags, products or a category with AWS Agentic AI: AWS Strands Agents Agentcore (sammcj/agentic-coding, 162 stars), Hcls Deploy Agent (aws-samples/amazon-bedrock-agents-healthcare-lifesciences, 274 stars), AWS Agentic AI (CommandCodeAI/agent-skills, 133 stars) and Deploy Observability (aliyun/alibabacloud-observability-mcp-server, 166 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains AWS Agentic AI?
zxkane (a GitHub user) maintains it in zxkane/aws-skills, which has 367 GitHub stars. The repository holds 6 skills in this directory. The repository was last updated on June 15, 2026.
Source: zxkane/aws-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.