LLM Mart Basic
@llm-mart · Joined Jun 2026
LiDAR and point cloud processing: PDAL pipelines, LAS/LAZ/COPC handling, ground classification, DTM/DSM/CHM generation, canopy and building metrics, and photogrammetric (SfM) point clouds. Use when the primary input is LAS, LAZ, COPC, LiDAR, or an unstructured 3D point cloud. Thi
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.
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.
/bug-fix
Bug fix
Systematic workflow for fixing bugs including issue creation, branch management, and PR submission
/bulk-import-issues
Bulk import issues
Bulk import GitHub issues to Linear
/business-scenario-explorer
Business scenario explorer
Explore multiple business timeline scenarios with constraint validation and decision optimization.
/changelog-demo-command
Changelog demo command
Demo changelog automation features
/check-file
Check file
Perform comprehensive analysis of $ARGUMENTS to identify code quality issues, security vulnerabilities, and optimization opportunities.
/check
Check
Run project checks and fix any errors without committing
/ci-setup
Ci setup
Setup continuous integration pipeline
/clean-branches
Clean branches
Clean up merged and stale git branches
/clean
Clean
Fix all linting and formatting issues across the codebase
/code-permutation-tester
Code permutation tester
Test multiple code variations through simulation before implementation with quality gates and performance prediction.
/code-review
Code review
Perform comprehensive code quality review
/code-to-task
Code to task
Convert code analysis to Linear tasks
/code_analysis
Code analysis
Perform comprehensive code analysis with quality metrics and recommendations
/commit-fast
Commit fast
Automatically create and execute a git commit using the first suggested commit message
/commit
Commit
Create well-formatted git commits with conventional commit messages and emoji
/constraint-modeler
Constraint modeler
Model world constraints with assumption validation, dependency mapping, and scenario boundary definition.
/containerize-application
Containerize application
Containerize application for deployment
/context-prime
Context prime
Load project context by reading README.md and exploring relevant project files
/create-architecture-documentation
Create architecture documentation
Generate comprehensive architecture documentation
/create-command
Create command
Create a new command following existing patterns and organizational structure
Make any song you can imagine
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