LLM Mart Basic

Design Alibaba Cloud solutions — product selection (PolarDB vs RDS, ACK vs ASK vs SAE, MaxCompute vs AnalyticDB), architecture patterns, landing zone design, and disaster recovery strategies aligned to the Alibaba Well-Architected Framework.

Coordinate Alibaba Cloud support incidents — case creation with correct severity (紧急/高/中/低), Enterprise Support SLA enforcement, account manager escalation path, status page monitoring for CN-* and international, internal stakeholder communication, and post-incident evidence pack

Triage Alibaba Cloud operational alerts, incidents, and support tickets — P0/P1/P2/P3 classification, Alibaba Cloud Support SLA enforcement, account manager escalation, DingTalk war room coordination, evidence collection from CloudMonitor and SLS, and safe escalation paths.

Assess Alibaba Cloud cost posture: ECS instance family rightsizing, Savings Plans and Reserved Instance coverage, Preemptible Instance adoption, cost allocation tagging, OSS storage tiering, analytics pricing, and idle resource elimination.

Assess Alibaba Cloud workload reliability: multi-AZ ECS topology, SLB/ALB/NLB load balancing, Auto Scaling health policies, RDS/PolarDB HA failover, backup and cross-region DR, and Cloud Monitor/ARMS observability coverage.

Assess Alibaba Cloud workload security posture: RAM least-privilege, VPC isolation, KMS/HSM encryption, Cloud Security Center threat detection, ActionTrail audit, WAF/Anti-DDoS web protection, and Chinese regulatory compliance (MLPS 2.0, DSL, PIPL).

Use this skill when reviewing Argo Rollouts progressive delivery configuration. Trigger when the user asks about canary or blue-green Rollout strategy correctness, AnalysisTemplate success/failure conditions, traffic weighting provider alignment, canaryService isolation, PDB dead

Use this skill for Argo CD GitOps review across Application, AppProject, ApplicationSet, sync windows, RBAC, sync impersonation, and Argo CD Agent multi-cluster topologies. Trigger when the user asks whether an Argo CD configuration is safe for production, whether automated sync

Build, test, migrate, integrate, and deploy Amazon Bedrock AgentCore agents. Use for AgentCore runtime, local development, import/migration, deployment, Memory, Gateway/MCP tools, Identity, Observability, Browser, Code Interpreter, Evaluations, Registry, Payments, policy, and har

Review AWS API and edge delivery posture across API Gateway, CloudFront, AWS WAF, Shield, ALB, custom domains, TLS policies, authentication, authorization, throttling, quotas, caching, origin protection, logging, and abuse controls. Use when public APIs, web entry points, or edge

Review Amazon Bedrock agents, AgentCore, Guardrails, knowledge bases, action groups, memory, MCP/tool integrations, prompt-injection and prompt-leakage defenses, PII handling, encryption, logging, observability, and least-privilege IAM. Use for AWS-native GenAI and agent security

Assess AWS change impact using change sets, deployment blast radius, rollback readiness, dependency mapping, risk, go/no-go context, approval context, and stakeholder communication. Prefer this for non-destructive pre-change advisory work; prefer IaC or platform-specific skills f

Review AWS CI/CD and release safety across CodePipeline, CodeBuild, CodeDeploy, GitHub Actions, GitLab, artifact provenance, deployment gates, approvals, tests, progressive delivery, rollback, change correlation, and incident-prevention recommendations. Use when AWS releases or p

Map AWS compliance evidence for audits across Security Hub controls, AWS Config rules/conformance packs, Audit Manager assessments, evidence folders, manual evidence, AWS Artifact reports, CloudTrail, and control narratives. Use for evidence packaging and audit readiness, not gen

Review AWS cost anomalies using Cost Explorer, Cost Anomaly Detection, Budgets, usage spikes, commitments, and tagging gaps. Prefer this for proactive FinOps watch and non-destructive escalation; prefer aws-cost-optimization-governor for broader optimization strategy.

Review AWS cost optimization and FinOps posture across Cost Explorer, Budgets, Cost Optimization Hub, Compute Optimizer, Savings Plans, Reserved Instances, tagging, showback, idle resources, rightsizing, storage, data transfer, and forecast risk. Use when the user asks to reduce

Prepare AWS daily operations briefings using CloudWatch, Personal Health Dashboard, Trusted Advisor, cost signals, deployment timelines, incidents, risks, and action backlog. Prefer this for non-destructive business and engineering status coordination; prefer observability, cost,

Review AWS backup and data protection implementation across AWS Backup, EBS/RDS/EFS/S3 recovery patterns, vaults, vault lock, retention, encryption, cross-account/cross-Region copy, restore testing, lifecycle, and recovery evidence. Prefer resilience BCDR review for broader RTO/R

Patch AWS deployment hotfix config, release parameters, manifest mistakes, environment drift, rollback blockers, and rollout blockers in-repo. Use for rapid non-destructive deployment corrections; do not use for live deploy/apply/destroy actions.

Design, review, and improve AWS DevOps Agent-compatible skills, investigation workflows, learned skills, tool-use best practices, agent type targeting, frontmatter descriptions, reference materials, and operational output contracts. Use when creating or adapting skills for AWS De

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.

Treat an AI agent skill as both an instruction package and a software dependency: inspect what it says, what it runs, what it can access, and how it updates.

Add remote HTTP or local stdio MCP servers to Claude Code, choose the right scope, protect credentials, verify the connection, and test with least privilege.

Skills teach Claude a repeatable method, connectors provide governed access to apps and live data, and plugins package related capabilities for installation and sharing.

Use an agent skill to package reusable know-how and workflow instructions. Use an MCP server when an agent needs live, governed access to external data or actions.

Custom commands and skills can both create a slash-invoked workflow in Claude Code. The important choice is how the workflow is discovered, shared, and permissioned.

A useful Claude skill solves one recurring engineering job, is easy to inspect, and saves more time than it creates in setup and review.

Claude skills can live in your Claude account, your local Claude Code setup, or a repository. Install them where the sessions that need them can load them.

Build a portable AI agent skill from one repeatable job: a precise description, concise instructions, focused resources, and tests that prove it works.

AI agent skills package instructions, scripts, references, and templates into portable folders an agent loads only when the task calls for them.

AI made publishing cheap, which is exactly the problem. What separates a page worth ranking from a competent summary of the first ten results.

A prompt that works once isn't a quality system. Five cases, an observable rubric, and a regression set will tell you whether a change helped.

One character of YAML, four pods that never started, and two safety nets I didn't know were holding. Every restart is an audit. Schedule them before they schedule you.

"Verify your work" isn't an instruction. It's a mood. Here's the version that's an instruction. Verify with a different mechanism than the one that made the claim.

A prompt that works once may still fail in production. A lightweight eval set gives you repeatable cases, a clear rubric, and a way to see whether a prompt change actually improved the workflow.

The best AI tool is not the one with the longest feature list. It is the one that solves a defined job reliably, fits the workflow, handles data appropriately, and remains useful after the novelty wears off.

Use AI to speed research without losing trust. Learn to find primary sources, verify claims, preserve uncertainty, and keep an auditable source trail.

Better prompts aren't magic wording. They're short briefs that hand the model a task, the context it can't infer, the limits, and a quality bar.

Discover testnet↔mainnet subnet lineage from repo configs and open a PR for review (pass --dry-run to report only)

Triage raw inbox notes into reviewed repository destinations without deleting their sources.

Audit and rewrite content to remove AI writing patterns

Log a completed piece of content to content/log.md after the user confirms it was published.

Validate a dream artifact, review each proposal, and apply only individually accepted changes.

Run a curator pass against the validated memory directory and produce a proposal artifact.

End a session — log what happened, update state and the decision log, propose memory updates, and check for uncommitted or unpushed work

Find relevant context files by topic. Use when you need to load files for a topic without a slash command, or when a task spans multiple domains.

Inventory and migrate selected Gemini CLI workflows with dry-run review and parity checks.

Find repeated workflows in selected Gemini CLI sessions and draft portable skills after review.

Scan multi-session drift and offer individually reviewed fixes only after explicit approval.

Scan orphaned worktrees and stale branches, then offer explicit approval-gated cleanup.

Guided onboarding or import for durable workspace context

Start a session — load state files, flag staleness, and give a briefing on current priorities, deadlines, and blockers

Create a morning heartbeat from repository state and update the local heartbeat log.

Mid-session checkpoint — append progress to today's session log and update state files if a priority shifted, without ending the session

Maintainer-only. Find every file that would newly ship to adopters, classify each one against the written distribution-boundary categories, default to withhold on no clean match, and ask the maintainer only where the taxonomy does not settle it. Drives the release CLI, which refuses to produce a manifest until every shipping file has an answer.

Audit memory, wiki, and auto-loaded files for duplication, conflicting instructions, and stale content. The default path researches, then asks you a single Apply / Discuss / Decline question; on Apply it applies the report, files any out-of-scope problem as a tech-debt issue, then commits, opens a PR, and merges it on a main-branch run like /update-deps. Pass --apply to re-run the apply-and-publish stage against the most recent report.

Fix the tech-debt backlog, a single issue or a recommended related batch, highest severity then oldest first, on a fresh isolated branch through the audit gate, closing the issue(s) on merge. Pass `list` to see the ordered backlog, `why <issue-number>` to explain the recommendation, or a bare `<issue-number>` to fix that issue directly.

Health-check and auto-heal this project's Claude integration, triage, heal, verify, and report an F-to-A+ grade.

Conduit — native SwiftUI iOS client for Hermes Agent

一个现代化的可视化规则引擎平台，用于编排复杂的 AI 工作流。在无限画布上设计、测试和部署 AI 流程——无需编写代码。结合 flowgram.ai 的能力与 Java 服务端，提供生产级工作流管理。在此之上提供AI Agent / Copilot的助手编排能力。

Job application tracker and AI-powered job search assistant. Helps job seekers manage their search journey with AI resume review, job matching, task logging, an…

📝 Markdown and HTML renderer for Svelte 5 — built for streaming AI agent output from Claude Code, ChatGPT, and agentic workflows. XSS-safe defaults, token cach…

ServiceNow MCP server: 500+ tools and 26 AI capabilities for any AI (Claude, ChatGPT, Gemini, Cursor, Copilot). Multi-transport (stdio, SSE, HTTP), A2A, dynamic…

Free crypto news API - real-time aggregator for Bitcoin, Ethereum, DeFi, Solana & altcoins. No API key required. RSS/Atom feeds, JSON REST API, historical archi…

Build production-ready AI agents in both Python and Typescript.

From agent user to agent builder: build a Claude Code-style coding agent from scratch in Python: 8 articles, 4 videos, one codebase

🪁 A lightweight, modern Kubernetes dashboard that unifies multi-cluster and resource management, enterprise-grade user governance (OAuth, RBAC, and audit logs)…

Stateful runtime management for LLM agents—inject, manipulate, and retrieve Python objects across turns.

AgentCall lets AI Agents join meetings with voice, video & screen-share to build together. Supports Google Meet, Teams, Zoom (Beta)

Kition brings Markdown, DataTable, WhiteBoard, a tool-using AI agent, browser research, and visual workflows into one desktop workspace.

IntentKit is an open-source, self-hosted cloud agent cluster that manages a collaborative team of AI agents for you.

A powerful Model Context Protocol (MCP) server providing comprehensive Google Maps API integration with LLM processing capabilities.

Turn your Claude Pro/Max subscription into an OpenAI-compatible API for your IDEs and devices — LAN auth, per-key quotas, response cache, disciplined cli.js ali…

Paw Work - selection-first web agent for Chrome: select on the live page, describe the outcome, take away an editable office file. BYOK, sandboxed, no server.

基于 AI Agent + MCP 工具链 + 渗透 Skill 编排， 配合大语言模型， 自然语言输入 → 自动完成「信息收集 → 漏洞发现 → 漏洞利用 → 报告生成」全流程。

Lexora — Personal AI workspace built around Desktop / 以 Desktop 为核心的个人 AI 工作台

First AI Journey for DevOps - with comprehensive learning paths, practical tips, and enterprise guidelines

AI coding agent with one Python core and three front-ends — headless CLI, Textual TUI, and an Electron desktop. Works with any OpenAI-compatible API, with risk-…