LLM Mart Basic

Invoke whenever spatial SQL or its execution backend is the decision: PostGIS, DuckDB Spatial, SpatiaLite, ST_* functions, recurring spatial joins, concurrent/growing workloads, or large GeoParquet queries. Covers backend selection, schemas, GiST/BRIN indexes, KNN, geometry versu

Always invoke for classical analysis, classification, validation, or comparability of satellite, aerial, or drone imagery. This skill owns sensor, product, processing-level and processing-baseline harmonization, including multi-date inputs; add change-detection only after compara

Always invoke before testing a geographic pattern for clustering, hotspots, dependence, or explanatory regression, even when aggregation or ordinary OLS is proposed as routine. Covers Moran's I, LISA, Getis-Ord Gi*, weights, MAUP and scale sensitivity for areas/grids, residual de

Always invoke to review, repair, or deliver geospatial or GeoAI code, including contract compliance, security, error handling, transactions, tests, scripts, functions, notebooks, packages, CI/CD, and repository changes, even when deployment is not requested. Pair with the domain

Always invoke for terrain, drainage, viewshed, or visibility analysis from elevation, even before the DEM or correct surface is chosen. Covers DTM-versus-DSM selection, slope, aspect, curvature, hillshade, conditioning, flow direction/accumulation, streams, watersheds, and catchm

Manage Preset teams, workspaces, memberships, invites, role identifiers, seat checks, and audit logs through direct Management API calls. Use only for direct API workflows; Do not use for MCP-only work.

Prepare direct Preset API access: auth, JWT exchange, base URLs, pagination, Rison parameters, response handling, and shared API setup. Use only for direct API workflows; Do not use for MCP-only work.

Use Snowflake Cortex Agent REST and SQL APIs for listing, describing, creating, updating, deleting, running agents, streaming responses, and SQL wrappers. Use only for direct API workflows; Do not use for MCP-only work.

Inspect Preset workspace dashboards, charts, dashboard composition, screenshots, thumbnails, chart data, and chart/dashboard operation routing through direct Superset API calls. Use only for direct API workflows; Do not use for MCP-only work.

Inspect or route Preset database connection configuration, validation, OAuth, upload, create, update, and delete workflows through direct Superset API calls. Use only for direct API workflows; Do not use for MCP-only work.

Inspect Preset workspace datasets, database metadata, schemas, tables, columns, metrics, and dataset/database workflow routing through direct Superset API calls. Use only for direct API workflows; Do not use for MCP-only work.

Review destructive, overwrite-capable, sparse-update, all-assets restore, database import, and secret-bearing Preset import workflows through direct Superset API calls. Use only for direct API workflows; Do not use for MCP-only work.

Review embedded analytics row-level security clauses, tenant filters, guest-token RLS rules, and external-viewer isolation for direct API workflows. Use only for direct API workflows; Do not use for MCP-only work.

Inspect embedded dashboard configuration, trusted domains, origins, guest-token routing, and embedded RLS routing through direct Superset API calls. Use only for direct API workflows; Do not use for MCP-only work.

Prepare and create Preset embedded dashboard guest tokens, external-user claims, resource claims, RLS claims, and token-handling plans through direct Superset API calls. Use only for direct API workflows; Do not use for MCP-only work.

Inspect and route direct Superset import/export workflows for dashboards, charts, datasets, databases, saved queries, themes, and asset bundles. Use only for direct API workflows; Do not use for MCP-only work.

Review Preset role, workspace membership, permission, access-control, DAR/RLS-adjacent, and effective-access changes through direct API calls. Use only for direct API workflows; Do not use for MCP-only work.

Prepare Snowflake Cortex direct API access: account URL, auth method, role, warehouse, database/schema context, privileges, and Cortex Agent routing. Use only for direct API workflows; Do not use for MCP-only work.

Run or route SQL Lab execution, result retrieval, exports, query stop, saved-query mutation, and permalink workflows through direct Superset API calls. Use only for direct API workflows; Do not use for MCP-only work.

Inspect Preset SQL Lab bootstrap, query history, saved queries, result/export routing, query control, and SQL execution routing through direct Superset API calls. Use only for direct API workflows; Do not use for MCP-only work.

Fourteen posts of being wrong in production, compressed to checkboxes

Healthy nodes, a quiet network, 300 restarts in three days, and a latency budget measured in milliseconds

Discovery worked. Ping worked. Every TCP connection timed out, and later the tunnel only worked when someone had a terminal open.

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.

Search for RMM agents in Atera by customer or machine name

Search for Atera customers by name or criteria

Update fields on an existing Atera ticket

Triage open Auvik alerts, rank by severity, and recommend dismissals for known noise

Scan Auvik interface statistics for saturated links and recurring congestion

Inventory devices for an Auvik tenant with type, manage status, and lifecycle breakdown

Audit a tenant's networks, interfaces, and saved configurations; flag drift and missing backups

Single-tenant Auvik snapshot - devices, alerts, networks, billing usage

Phishing-simulation results and click-rate trend for a given window

Human risk score report for one client or the whole portfolio, built from training completion and phishing-simulation performance

Training completion snapshot for one client or the whole portfolio — completion rates, overdue users, and cadence status

Check backup health for one Axcient-protected device

Backup health overview across every device for one Axcient client

Azure cost and pricing analysis for a subscription — Advisor cost recommendations, retail pricing lookups, and quota-driven right-sizing signals, scoped to one subscription

Resource health and diagnostics triage for an Azure resource or subscription — Resource Health status, AppLens deep diagnostics, and Azure Monitor alert state

Portfolio-wide backup job health snapshot - failure count, at-risk clients, and storage trends

Restore-readiness check - has this actually been restore-tested, for one client or the whole portfolio

Retention and RPO compliance audit against contracted requirements, for one client or the whole portfolio

Create a new Better Stack uptime monitor

Triage current Better Stack incidents

Ultra-lightweight, open-source, self-hosted personal AI agent framework in Python with WebUI, tools, memory, MCP, multi-agent workflows, automation, and chat ap…

Governance framework for AI coding agents. It runs them through a five-step workflow (plan, build, review, test, ship) where no step counts as done without evid…

Ultimate Multi-Agent OS for Autonomous AI NPCs 2026

Personal AI Agent Hub 2026 — Build Your 24/7 Autonomous Assistant

Proven 2026 Multi-Agent AI Review System – Verdict-Driven Quality Control

Slash API Batch: Cut AI Costs by 50% in 2026

Web dashboard for Hermes Agent — multi-platform AI chat, session management, scheduled jobs, usage analytics

Agent Skills for Solopreneurs

AirLLM dramatically reduces inference memory usage, letting 70B large language models run on a single 4GB GPU card

Zero, your trustworthy AI teammate for real work.

一套 DSH runtime，Desktop、Web 与 TUI 三种开发体验。

Open-source operational advisor for ClickHouse — real-time monitoring plus AI-driven index/partition/materialized-view recommendations.

⚙️ TypeScript Style Guide and Agent Skill. A concise set of conventions and best practices for consistent, maintainable code.

Framework for AI agents to build and maintain a digital brain through Obsidian wiki

Apache Maka (Incubating) is a local-first AI agent workspace. Model messages, tool calls, tool results, permission decisions, and termination events are recorde…

Neo.mjs is a self-evolving software organism: a professional end-to-end AI engineering team whose cross-model swarm inhabits live apps via Neural Link, Active H…

Agentic development harness for Claude Code — SPEC-driven plan/run/sync, TRUST 5 quality gates, model+effort routing, and Claude×GLM multi-LLM cost control. Sin…

NocoBase is an open-source AI + no-code platform for building business systems fast. Instead of generating everything from scratch, AI works on top of productio…

An open-source AI coding agent that lives in your terminal.

PawWork — free, open-source desktop AI agent for macOS and Windows. Alternative to Codex App and Claude Cowork. BYOK with 75+ providers, ChatGPT OAuth, local mo…