LLM Mart Basic

Abnormal Security threat detection: threat types (BEC, phishing, malware, socially-engineered attacks, spam, graymail, credential theft), attack vectors, severity assessment, remediation actions, and investigation workflows.

Atera alerts: alert types, severity levels, alert sources, the acknowledge/resolve lifecycle, and alert-to-ticket conversion.

Atera REST API fundamentals: X-API-KEY header authentication, OData-style pagination, the 700 requests/minute rate limit, endpoint conventions, and error handling.

Atera customers and contacts: customer records and fields, contact management, custom fields, and customer lifecycle operations.

Atera service desk tickets: ticket fields, statuses, priorities, comments, work hours, and billing duration.

Auvik alerts: severity tiers, status lifecycle, dismissal semantics, and the common alertName patterns that show up in MSP NOC queues.

Auvik MCP fundamentals: the JSON:API envelope shape, basic-auth credential model, region routing, cursor-based pagination, rate-limit handling, and the v1 vs v2 device API distinction.

Auvik device records: device types, manageStatus and onlineStatus, lifecycle and warranty fields, and choosing between the v1 list endpoint and the detailed device endpoints.

Auvik network and interface entities: the network entity model, IP-range scoping, interface-to-device relationships, and adminStatus vs operStatus.

Autotask REST API fundamentals: header-based authentication, zone detection, the query/filter DSL (14 operators, logical grouping, includes), pagination, rate limits, and CRUD conventions across the 215+ entity PSA.

Autotask billing item retrieval, approval-level workflows, and invoice search — covering billing item types, approval status filtering, and reconciliation of billable work against invoices for MSP finance teams.

Autotask Configuration Item (CI) asset management: CI types and categories, lifecycle status codes, the CI field schema, related-item relationships, DNS records, notes, and contract/billing associations for MSP infrastructure tracking.

Autotask contract and service agreement management - contract types (recurring services, block hours, time & materials, fixed price, retainer), service/service bundle associations, SLAs, and how contracts drive billing for MSP account managers.

Autotask CRM entities - companies (accounts), contacts, and sites/locations - including field references, company type classifications, and how these records underpin tickets, contracts, and projects for MSP account management.

Autotask expense report and expense item structure - the report/item parent-child relationship, approval status workflow, expense categories, payment types, and the billable vs reimbursable distinction for MSP operational expenses.

Autotask picklist and reference-data lookups — queues, ticket statuses, ticket priorities, and project phases — the instance-specific configured values required before creating or filtering tickets and other entities.

Autotask product catalog structure - Products, Services, and Service Bundles - and how Price Lists override default unit pricing. Covers product/service fields, inventory tracking, and cost-vs-billing margin analysis for MSP quoting and procurement.

Autotask project structure - projects, phases, tasks, and milestones - including project and task fields, status values, resource assignment, and how project work links to contract billing for MSP project managers.

Autotask quote structure and line items - quote item types (product, service, service bundle, labor, expense, shipping), the mutually-exclusive catalog reference rules, and the three discount mechanisms (unit, line, percentage) used to build customer proposals.

Autotask Service Call data model - the ServiceCall / ServiceCallTicket / ServiceCallTicketResource three-layer structure - covering fields, status codes, and how tickets and technicians (resources) are linked to scheduled work.

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.

A green PR, a controller reporting success, and not one line of the new code running

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 and decide, one file at a time, whether to ship it or withhold 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.

Persistent session memory for AI coding agents — local-first, with on-device inference, associative recall, and drift detection. Works with Claude Code, Cursor,…

Run Hermes Agent and OpenClaw on the same WeChat account

An AI co-scientist running on your desktop. Claude Science but better.

Emotion Ball 是一套面向 AI 助手的表情引擎:32 种状态表情全部由纯 SVG 与原生 JavaScript 实时驱动,零框架、零图片资源。AI 侧只需输出一个 emotionId,小球即可切换到对应表情,可直接用作聊天机器人、桌面宠物、悬浮助手的情绪表达层。

Agent communication SDK. The open-source agent communication layer for AI agents — email, WhatsApp, Slack, Discord, Telegram, SMS. Python & TypeScript.

The micro-VM for AI agents — light enough to embed on your laptop, elastic enough to power an agentic cloud.

Give each AI agent its own isolated machine with root, Docker, and systemd. Active defense detects and stops threats automatically.

Webhook integration skills for AI coding agents (Claude Code, Cursor, Copilot). Step-by-step guidance for setting up webhook receivers, signature verification,…

Token burn reducer and focus keeper for Claude Code, Codex, Copilot, Gemini CLI, and more: surgical read hints, PDF/Office/CSV/markdown file interception, 160+…

RuvNet Brain — a downloadable, source-grounded brain for Claude Code over Reuven Cohen's (rUv's) RuvNet stack: RuVector/RVF, Ruflo, AgentDB, RuLake, SPARC + 21…

💼 One MCP server to search job boards and company career sites

C++ MCP SDK - build Model Context Protocol (MCP) servers and clients in C++ / CPP. Enterprise-grade security, observability, connectivity. Stdio, HTTP+SSE, Stre…

Open-Source AI Presentation Generator and API (Gamma, Canva, Beautiful AI, Decktopus, Presentations AI Alternative)

Secure, Fast, and Extensible Sandbox runtime for AI agents.

Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.

Browser Harness | Self-healing harness that enables LLMs to complete any task.

The World's First Agentic IDE. Visual dashboard: live sessions, task management, code editor, terminal. Epic Swarm parallel workflows. Auto-proceed rules. Autom…

Agenta is a workspace where you and your team build agents and automations.

Terminal Director. One lightweight app, eight features, your whole dev workflow in a single window.

AI turns documents or topics into real, native PowerPoint decks—with native shapes, transitions and animations, data-backed charts and tables on demand, audio n…