LLM Mart Basic

Autotask ticket notes, attachments, and charges — the secondary entities attached to tickets: retrieving/searching notes and attachments, and creating, updating, or searching ticket charges for labor and expenses billed directly to a ticket.

Autotask ticket lifecycle: status/priority codes and transition rules, the ticket field schema, SLA calculation and clock behavior, escalation rules, ticket metrics, and the MCP tool surface (create, update, search, history, notes) for MSP service desk operations.

Autotask time entry structure: approval status codes and workflow, the time entry field schema, the billing rate hierarchy, budget and contract-limit validation, utilization analytics, and the MSP business rules for rounding and minimum billing increments.

The Autotask MCP lazy-loading pattern - four meta-tools (list_categories, list_category_tools, execute_tool, router) that expose the full 39+ tool catalog progressively instead of loading every tool schema upfront, plus the natural-language router for intent-based tool lookup.

Kaseya Quote Manager (Datto Commerce) API fundamentals: API-key auth and the gateway's header translation, the read-only `kqm_<entity>_list`/`_get` tool surface across the sales, procurement, catalog, CRM, and org domains, page/pageSize/modifiedAfter pagination, rate limits, and

Kaseya Quote Manager procurement data: purchase orders with their lines and costs, the suppliers they are placed with, and product-supplier records mapping catalog products to supplier SKUs and pricing. Read-only tool surface.

Kaseya Quote Manager quoting data: the quote → section → line item hierarchy, and the sales orders, order lines, and payments a quote becomes once accepted. Read-only tool surface.

Better Stack MCP and API surface across Uptime, Telemetry (Logtail), and Error Tracking: available tools, Bearer token authentication, API structure, cursor-based pagination, rate limiting, and error handling.

Better Stack incidents: incident records raised by uptime monitors or reported manually, and the triage, acknowledgment, and resolution lifecycle.

Better Stack log management (Logtail): log sources, structured log search and query syntax, log-based alerting, and log analysis workflows.

Better Stack uptime monitors: check types, monitor fields, heartbeat monitors, monitor groups, and create/update/pause/delete operations.

Better Stack on-call: on-call calendars and rotations, escalation and notification policies, alert routing, and determining who is currently on call.

Better Stack status pages: status page configuration, resources and components, maintenance windows, and public service-status communication.

Blumira agents (sensors) and the devices they run on: device inventory and filtering, agent health via last-seen timestamps, and agent deployment keys.

Blumira REST API fundamentals: JWT authentication, the dual `/org/*` vs `/msp/*` path structure, suffix-based filter operators, pagination parameters and response metadata, the stateful MCP navigation tools, and HTTP error causes.

The Blumira finding lifecycle: status and severity codes, resolution types, list filtering, enriched detail retrieval, assignment, and comment threads.

Blumira's MSP path group (`/msp/*`): managed-account enumeration, cross-account and per-account finding queries, per-account device, agent-key and user management, and how MSP paths differ from org paths.

Blumira resolution types (Valid, Not Applicable, False Positive): how to choose between them, their effect on security metrics and detection tuning, and the org- and MSP-level resolve calls.

Blumira organization users: listing and filtering users, user roles, and looking up the user IDs required for finding assignment and access audits.

Shape of the Checkpoint Harmony Email (Avanan) `hec_*` tool surface: the thirteen tools and what each reaches, the event/entity split that governs which tool accepts which id, the `responseEnvelope`/`responseData` result shape, `scrollId` pagination, and the auth, regional-routin

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.

AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.

嘉立创EDA专业版(EasyEDA Pro)自动化：给 AI harness 装上画板的「手」—— 一套 typed 原理图/PCB 动作，CLI / Agent Skill / stdio MCP 三形态融合接入。承接嘉立创「不以卖板赚钱，以培养中国工程师为己任」 | EasyEDA Pro automation:…

Claude Code session log viewer for JSONL files in ~/.claude/projects. Browse conversations, tool calls, tokens, and live tail sessions on desktop, web, and TUI.

Shared memory MCP server — persistent, searchable, cross-client Claude, Opencode

OpenAI Codex CLI session log viewer for JSONL files in ~/.codex/sessions. Browse conversations, tool calls, tokens, collaboration chains, and live sessions on d…

High-performance Google Drive CLI and Model Context Protocol (MCP) Server for LLM/AI agents. Natively execute Google Apps Script (GAS) under a secure whiteliste…

"Vibe-Trading: Your Personal Trading Agent"

Local-first chat history analyzer with AI. | 本地优先的 AI 聊天记录分析工具

Official Oxylabs MCP integration

Persistent memory for Claude Code & Codex CLI. Auto-extracted knowledge graph, multi-representation embeddings, 3D WebGL visualization. LongMemEval R@5=97.45%.…

One person, a team of agents. Multi-session CLI that collaborates across terminals; /goal keeps long tasks running; WeChat/WeCom/Feishu gateway lets you call th…

AI 基础知识 - GPU 架构、CUDA 编程、大模型基础及AI Agent 相关知识。

Self-hosted, open-source agent skill registry for enterprises. Publish & version skill packages, govern with RBAC and audit logs, deploy on-premise with Docke…

深度调研报告生成 Skill — 一条命令，十分钟出券商级深度调研报告 / Professional deep research report generation Skill · Supports 19 languages

The OKF toolkit for Claude Code — author, maintain, validate & visualize Open Knowledge Format bundles. Plugin, agent skills, and a GitHub Action.

Autonomous AI pentesting agents — real-time reconnaissance, vulnerability detection, and exploitation orchestration. Go + TypeScript.

Native macOS and iOS App for the Hermes AI agent — multi-window, multi-server (local + remote over SSH). Chat, dashboard, sessions, memory, cron, MCP, and more.

Multiplayer Claude Code and Codex. Self-hosted team workspace for Claude Code and Codex. Multi-user, browser-based, AI mates with memory.

Every past session, subagent, and workflow -- queryable by your agent, browsable by you

盯盘侠 PanWatch · 自托管 AI 盯盘助手，集成 TradingAgents 多 Agent 投资决策 | A股/港股/美股实时监控、持仓管理、智能分析、全渠道推送