LLM Mart Basic
@llm-mart · Joined Jun 2026
Runs CloudWatch Logs Insights queries via AWS SDK for JavaScript v3. Analyzes Lambda cold starts, API Gateway latency, and ECS container logs. Generates anomaly detection alarms with math expressions.
Builds CloudWatch Logs Insights queries and metric alarms using AWS SDK v3 (@aws-sdk/client-cloudwatch-logs, @aws-sdk/client-cloudwatch). Generates cross-account observability dashboards with CloudWatch Metrics Insights.
Analyzes AWS CloudWatch Logs using the CloudWatch Logs API and Logs Insights query syntax. Identifies error patterns, calculates error rates, and generates metric filters from log data.
Investigates anomalous patterns in AWS CloudWatch Logs using the CloudWatch Logs Insights API and CloudWatch Anomaly Detection. Correlates log spikes with deployment events via AWS CodeDeploy API.
Scans AWS CloudWatch Logs using the CloudWatch Logs Insights API and CloudWatch Anomaly Detection API. Identifies unusual error patterns, latency spikes, and log volume anomalies across log groups.
Uses AWS SDK CloudWatchClient GetMetricData and CloudWatch Logs Insights StartQueryExecution to automate incident triage. Correlates alarms via DescribeAlarms with X-Ray trace segments for root cause analysis.
Manages AWS CodePipeline stages and actions using AWS SDK for JavaScript (CodePipeline, CodeBuild, CodeDeploy APIs). Automates blue-green deployments and cross-account pipeline configurations.
Uses boto3 and the AWS IAM Access Analyzer API to enumerate all roles, policies, and users, then flags permission combinations that could allow privilege escalation to AdministratorAccess. Outputs findings mapped to MITRE ATT&CK TA0004 with remediation steps and least-privilege r
Imported from agentskillexchange/skills/skills/aws-lambda-mcp-server.
The official AWS Labs MCP server collection provides AI agents with structured access to AWS documentation, service APIs, billing data, and infrastructure metadata through the Model Context Protocol, built and maintained by AWS for secure cloud automation workflows.
Imported from agentskillexchange/skills/skills/aws-s3-mcp-server.
Orchestrates AWS data pipelines using @aws-sdk/client-s3 and @aws-sdk/client-sqs. Manages S3 object lifecycle with PutObjectCommand/GetObjectCommand, processes SQS message queues via ReceiveMessageCommand with long polling, and configures S3 event notifications to SQS for event-d
Resolves AWS SDK v3 client commands and service endpoint signatures using @aws-sdk/client-* packages. Maps IAM permission requirements to specific API calls with request/response type definitions.
Executes automated diagnostics using the AWS Systems Manager Automation API and SSM Documents. Collects system metrics via the CloudWatch GetMetricData API and correlates with AWS Health events.
Execute AWS Systems Manager Automation runbooks and Run Command documents using the SSM API and boto3. Supports cross-account execution, rate controls, and parameter store integration.
Coordinates remediation playbooks with AWS Systems Manager Automation, Incident Manager, and CloudWatch alarm context for repeatable operational recovery. Useful for agents that need to recommend or launch the right runbook when alarms cross into known failure territory.
axe-core is the industry-standard accessibility testing engine by Deque Systems that automatically detects WCAG 2.0, 2.1, and 2.2 violations in web interfaces. It integrates into any existing test framework — Playwright, Cypress, Selenium, Jest — to catch accessibility issues dur
The Azure CLI provides cross-platform command-line access to Microsoft Azure services for managing VMs, databases, Kubernetes, AI services, and hundreds of cloud resources with consistent hierarchical commands.
Protect an OpenClaw workspace with repeatable Synology NAS backups, integrity checks, and safer restores instead of ad hoc file copying.
Run repeatable cross-forge repository backup jobs from one config instead of hand-scripting clone and export steps per provider.
Every VM came back. The cluster did not. Declarative systems converge on config, and the datapath isn't config.
A surprising share of AI-in-the-terminal failures aren't the AI. They're zsh, and a version of bash from 2006.
A Claude Code plugin turns standalone project configuration into a namespaced, installable extension that teams and communities can update as one unit.
None of the safety came from the model. It came from six boring habits.
Skills package instructions and references. Subagents run work in a separate context and return results. They solve different problems and can be composed deliberately.
Six hours in, one step left, everything green, and the incident that didn't happen
CLAUDE.md carries persistent project context. Skills load reusable procedures when relevant. Separating stable facts from task-specific workflows keeps both easier to maintain.
Twenty minutes recovering secrets that never existed, and the one sentence from a human that ended it
An API request routing a model's tool call through an approval gate to a remote MCP server
31 config keys, two audits, and why the first one was wrong in both directions
The official MCP Registry stores standardized server metadata rather than package code. Publishers verify a namespace, describe installation or remote access, and submit immutable versions.
Everyone looks at the Dockerfile. The file that actually leaked the key was the project file.
Remote MCP authorization uses established OAuth standards, but secure integration still requires issuer validation, least-privilege scopes, protected token handling, and server-side enforcement.
"Copy it over and switch the reference" is two steps, and the outage lives in the one nobody checks
stdio fits local processes and prototypes. Streamable HTTP fits hosted services and shared integrations. The right choice follows where the capability runs and who must reach it.
The most important rule wasn't about what I could change. It was about what I was allowed to display.
Tools perform operations, resources expose readable context, and prompts provide reusable templates. Choosing the correct primitive makes an MCP server easier to understand and govern.
Use MCP Inspector to connect to local or remote servers, inspect capabilities, call tools, read resources, test prompts, and diagnose failures before release.
Build an MCP server in TypeScript with focused tools, validated schemas, local and remote transports, Inspector tests, and production security controls.
An MCP server exposes tools, resources, or prompts through a standard protocol so an AI application can discover and use external capabilities.
/weekly
Weekly
The weekly review
/build
Build
Implement an approved plan or issue in its own worktree, run the gate, open the pull request.
/close-out
Close out
Close a finished session: sweep for unfinished work, land and hand off, file the follow-ups, tell the sessions that depend on this one, then archive.
/handoff
Handoff
Write the repository handoff file for the next session, and record any durable learning.
/land
Land
Merge an approved pull request, clean up its worktree and branch, then check whether a release is due.
/plan
Plan
Turn a topic or issue into a plan the reviewer approves in the native plan pane.
/research
Research
Answer a research question with parallel read-only gatherers and one synthesized digest.
/review
Review
Review the branch's diff in two fresh contexts — scope against the spec, then quality — and report findings only.
/ia-refine-prompt
ia-refine-prompt
Transform a vague prompt into precise, structured AI instructions
Make any song you can imagine
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