LLM Mart Basic

Design AWS non-destructive task automation using EventBridge, Step Functions, Lambda, Systems Manager Automation, SNS, SQS, approvals, notifications, reporting, and evidence gathering. Use only for read-only or coordination-safe automation; do not use for destructive remediation

Investigate broad AWS incidents and observability gaps using CloudWatch metrics, logs, alarms, traces, EventBridge events, service health, runbooks, timelines, blast radius, root-cause discipline, and post-incident actions. Prefer RDS/Aurora investigator for database-specific per

Repair AWS pipeline configuration, buildspecs, workflow files, deployment steps, artifact wiring, release guardrails, and CodeDeploy integration in-repo. Use for non-destructive CI/CD corrections; do not trigger live pipeline runs or mutate cloud state.

Use this skill when reviewing AWS ACM Private CA (Private Certificate Authority) issuer configurations for cert-manager. Trigger on any request to audit AWSPCAIssuer, AWSPCAClusterIssuer, IRSA policy for cert-manager, certificate template ARNs, CRL configuration, or cross-account

Investigate Amazon RDS and Aurora-specific incidents involving latency, connection exhaustion, slow queries, lock waits, storage pressure, CPU/I/O saturation, replica lag, failover behavior, Performance Insights, and database capacity. Prefer this for database performance; prefer

Review AWS resilience and business continuity strategy across RTO/RPO, dependency maps, multi-AZ, multi-Region, failover/failback, game days, runbooks, drift, and recovery validation. Prefer data protection backup steward for backup-plan/vault/restore implementation details.

Review Amazon S3 data perimeter and exposure posture across Block Public Access, Object Ownership, ACL removal, bucket/access point policies, TLS-only access, encryption, replication, lifecycle, logging, cross-account access, and prefix boundaries. Prefer this for S3 data exposur

Review broad AWS security posture across Security Hub CSPM, GuardDuty, Inspector, Macie, Config, CloudTrail, IAM, public exposure, vulnerability findings, and remediation governance. Prefer compliance evidence mapper for audit evidence packs, IAM skill for policy surgery, S3 peri

Review AWS Lambda-centered serverless workloads for production readiness across execution roles, event sources, retries, DLQs/destinations, concurrency, idempotency, observability, deployment safety, performance, cost, and rollback. Prefer event-driven architecture for EventBridg

Patch AWS serverless rollout definitions across Lambda, API Gateway, EventBridge, SQS, SNS, event source wiring, aliases, versions, and deployment config. Prefer this for repo-side rollout corrections; do not perform live rollout actions or destructive operations.

Design and stress-test AWS cross-domain solution architectures when the request spans multiple AWS domains or needs an architecture decision record. Prefer narrower AWS skills for single-domain IAM, network, EKS, ECS, serverless, RDS, DynamoDB, S3, Bedrock, IaC, cost, security, m

Triage AWS tickets and alerts using priority, owner, evidence, incident context, escalation path, OpsCenter, health signals, and safe next steps. Prefer this for non-destructive request coordination and escalation; prefer deep domain skills for implementation or root-cause invest

Review AWS workload cost posture against the Well-Architected Framework Cost Optimization Pillar. Covers cost visibility, tagging compliance, commitment coverage, rightsizing, Spot and managed service adoption, and idle resource identification. Use when auditing cloud spend, plan

Review AWS workload reliability posture against the Well-Architected Framework Reliability Pillar. Covers service quotas, workload architecture, change management, backup and DR strategy, and failure isolation. Use when auditing availability design, planning disaster recovery, or

Review AWS workloads against the Well-Architected Framework Security Pillar: identity foundations, detective controls, infrastructure protection, data protection, and incident response readiness.

Use this skill for Microsoft Foundry and Azure AI Foundry operations governance: resource-versus-project boundary design, RBAC review, quota planning, network isolation, logging, and safe MCP-backed read or write execution. Trigger when the user asks how to run Foundry safely acr

Operate Azure Kubernetes Service with an adversarial production posture. Use for AKS architecture sanity checks, upgrade safety, node-pool strategy, workload identity, network policy, scaling, observability, and operator-readiness reviews.

Review Azure App Service and Web Apps for production readiness across plan tier fit, slots, networking, private ingress, identities, secrets, scaling, diagnostics, resilience, backup, rollback, and operator readiness. Use when a team wants a real go/no-go decision instead of shal

Use this skill for Azure Cosmos DB application development work, especially NoSQL data modeling, document structure, partition-aware access patterns, point reads, query design, SDK usage, transactional batch scope, consistency-aware reads, change feed integration, and Cosmos DB d

Use this skill for Azure Cosmos DB performance investigation, especially RU spikes, query latency, throttling, hot partitions, indexing inefficiency, partition-skew analysis, request-charge profiling, diagnostic-log review, and evidence-driven remediation planning.

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.

Improve an existing agent through performance baselines, prompt engineering, A/B testing, and staged rollout

Optimize multi-agent system performance through profiling, context window tuning, coordination efficiency, and cost and latency tradeoffs

Debug issues using competing hypotheses with parallel investigation by multiple agents

Task delegation dashboard for managing team workload, assignments, and rebalancing

Develop features in parallel with multiple agents using file ownership boundaries and dependency management

Launch a multi-reviewer parallel code review with specialized review dimensions

Gracefully shut down an agent team, collect final results, and clean up resources

Spawn an agent team using presets (review, debug, feature, fullstack, research, security, migration) or custom composition

Display team members, task status, and progress for an active agent team

Build realistic API mock servers with request stubbing, dynamic data, test scenarios, and contract testing

Orchestrate end-to-end application performance optimization from profiling to monitoring

Orchestrate end-to-end feature development from requirements to deployment

Set up PreToolUse hook to block --no-verify and other git bypass flags in Claude Code projects

Generate comprehensive C4 architecture documentation (Context, Container, Component, Code) for a codebase using bottom-up analysis and four coordinated C4 agents.

Automate CI/CD pipelines, releases, and development workflows with GitHub Actions, pre-commit hooks, and infrastructure automation

Explain complex code, algorithms, and design patterns with step-by-step breakdowns, visual diagrams, and interactive examples

Generate API, architecture, code, and user documentation from a codebase and automate keeping it current

Restore saved project context and decisions to resume a session

Refactor provided code for cleanliness, maintainability, and alignment with SOLID principles and modern best practices — no over-engineering.

Analyze and remediate technical debt — inventory debt items, score by impact, and produce a prioritized remediation plan with estimated effort.

Make any song you can imagine

Leading AI-powered video generation platform that specializes in creating hyper-realistic talking avatars

Hermes Agent is an open-source, self-improving autonomous AI agent developed by Nous Research

Kilo Code is a popular, open-source AI coding agent and "agentic engineering" platform designed to help developers build, refactor, and debug software faster

General-purpose agent in one static Go binary. ReAct loop, ACP server for IDEs, OpenAI-compatible REST API with embedded web UI, Telegram gateway, cron schedule…

Autonomous agent framework with structured memory, safety hooks, and loop management. Built by the agent that runs on it.

TSP自托管、零运维的 A 股「选股 + 监控 + 回测」量化工作台 | 基于 TickFlow 数据源 | LLM能力驱使策略定制+个股分析+复盘 | 自由接入第三方数据源与个性化扩展数据 | 个人开源 ,非TickFlow官方项目

Curated, verified Agent Skills powered by ModelStudio.

Run Claude Code, Codex, Antigravity, Cursor Agent and OpenCode as one runtime — persistent sessions, multi-agent councils, an OpenAI-compatible endpoint, an MCP…

pi had nothing (nothing), so I made something (something) — sorry mariozechner-senpai, I went ahead and lovingly soiled your pure pi for you. opinionated fork o…

A persistent workspace for development work that self-improves and continues beyond one session.

Open-source memory and context for user-aware agents: scoped memory, provenance, retrieval quality, correction, boundaries, evals, and MCP/HTTP access.

📚 A zero-dependency, git-backed micro-lesson library for AI Agents to asynchronously share and search verified debugging experience. Python stdlib only. | http…

Deterministic, local-first memory and guardrails for AI coding agents with no LLM in the hot path.

Deterministic spec-orchestration for local LLMs in the pi coding agent — drives prompts through refine→research→grill→compose→critique, with bundled web/docs/fe…

Native Safari browser automation for AI agents. 97 tools via AppleScript — zero overhead, keeps logins, runs silently in background. Drop-in alternative to Chro…

Agent OS: keep specialist agents in a hub, spin up a temporary orchestrator per task. Local-first, works with any model.

Git for agent memory. Branches, diffs, PRs, and rollback for what your agents know.

Multi-Provider AI Gateway - No personal logs by design. Model autodiscovery, Failover groups, High availability, Android companion app, and more - "Because we h…

Production-grade MCP server for MikroTik RouterOS with secure AI-native network automation.