LLM Mart Basic
@llm-mart · Joined Jun 2026
Create, edit, and debug vanilla Minecraft 26.x and 1.21.x datapacks, including functions, advancements, recipes, loot tables, predicates, tags, and pack metadata. Use when the deliverable is a datapack file tree without Java or loader APIs.
Operate EssentialsX on Minecraft 26.x and 1.21.x servers, including configuration, permissions, economy, kits, warps, homes, and moderation. Use for EssentialsX administration, not plugin development or general server deployment.
Generate or edit Minecraft raster assets such as pack icons, promo art, concept textures, thumbnails, banners, and UI mockups. Use when the deliverable is a bitmap image rather than JSON, SVG, or code-native assets.
Create, modify, debug, or migrate Minecraft mods for current NeoForge or Fabric 26.x, legacy 1.21.x, and Forge 1.20.1. Use for loader-based gameplay code and assets; use minecraft-multiloader when one codebase must target both modern loaders.
Build and maintain Architectury-based Minecraft 26.x or 1.21.x mods that share one codebase across NeoForge and Fabric. Use only when both loaders are required; use minecraft-modding for a single-loader project.
Create, modify, and debug server plugins for current Paper 26.x on Java 25 or legacy Bukkit-derived 1.21.x servers on Java 21. Use for JavaPlugin APIs, events, commands, schedulers, configuration, PDC, and Adventure, not client mods or vanilla datapacks.
Create and debug Minecraft 26.x and 1.21.x resource packs, including pack metadata, textures, models, blockstates, item definitions, sounds, fonts, animations, and shaders. Use for client-side visual or audio assets without gameplay code.
Set up, operate, tune, and troubleshoot Minecraft Java 26.x and legacy 1.21.x servers across Paper, Purpur, Folia, Velocity, Fabric, and NeoForge. Use for infrastructure, backups, proxies, and live operations, not plugin or mod development.
Design and implement automated tests for current Minecraft 26.x or legacy 1.21.x mods and plugins using JUnit, MockBukkit, NeoForge Game Tests, or Fabric Game Tests. Use for test code and test execution, not release publishing or gameplay implementation.
Create and debug Minecraft 26.x and legacy 1.21.x world generation for datapacks, NeoForge, or Fabric, including biomes, dimensions, features, structures, and biome modifiers. Use for worldgen data or registration, not general gameplay systems.
Operate WorldEdit safely for Minecraft 26.x and 1.21.x selections, region edits, masks, schematics, brushes, terrain shaping, and rollback. Use for command-driven world editing, not plugin development or general server administration.
Analyze prompt-cache effectiveness for Claude Code usage from the Agent Monitor dashboard — cache hit rate (total_cache_read / (total_cache_read + total_input)), cache_write vs cache_read reuse, cache-read vs cache-write spend, and the sessions with the poorest reuse. Pulls token
Break down Claude Code costs using the Agent Monitor pricing engine. Shows per-model costs (input, output, cache_read, cache_write at $/Mtok rates), per-session costs, daily trends, and compaction baseline token recovery. Use when analyzing spending, comparing model costs, or pla
Break down Claude Code usage by model family (Opus / Sonnet / Haiku) from the Agent Monitor dashboard — each family's share of tokens, share of cost, and the spots where an expensive model is doing cheap work. Pulls per-model token and cost splits from /api/pricing/cost, current
Calculate a productivity score using actual Agent Monitor metrics — session completion rates, cache efficiency (cache_read vs input), compaction pressure (baseline tokens), turn velocity (turn_count / total_turn_duration_ms), tool success ratio (PreToolUse vs PostToolUse), and th
Generate a comprehensive session report with per-model token usage (input, output, cache_read, cache_write including compaction baselines), cost breakdown via the pricing engine, tool invocations, agent hierarchy, compaction events, API errors, turn durations, and thinking block
Analyze Claude Code usage trends over time using the Agent Monitor's analytics API — daily session counts, daily event counts, token volumes by type, model distribution, tool usage rankings, and agent/event type distributions across 365-day retention windows.
Run a full audit of the user's Claude Code configuration via the Agent Monitor Config Explorer API: counts per surface (user vs project), duplicate or overlapping skills and subagents, hooks that run shell commands, and which surfaces are read-only vs mutable. Reads /api/cc-confi
Inventory hooks across the user, project, and project-local settings plus the ~/.claude/hooks scripts directory — read through the Agent Monitor Config Explorer API — and flag hooks that POST to the network or run arbitrary commands. Reads /api/cc-config/hooks and /api/cc-config/
Audit the configured MCP servers (user + project scope) via the Agent Monitor Config Explorer API: transport (stdio vs http), command/args and env variable names, headers, and the source file each definition came from. Reads /api/cc-config/mcp. Use when reviewing MCP integrations
/config-validate
Config validate
Validate application configuration with schemas, per-environment rules, runtime checks, and secure handling of sensitive values
/spark-preflight
Spark preflight
Preflight a DGX Spark system for an ML training or inference workload and emit env-report.json
/debug-trace
Debug trace
Set up debugging and tracing with remote debugging, distributed tracing, debug logging, profiling, and production diagnostics
/doc-generate
Doc generate
Generate API, architecture, code, and user documentation from a codebase and automate keeping it current
/error-analysis
Error analysis
Analyze and resolve errors across the full application lifecycle — from stack traces to distributed tracing — using systematic root-cause analysis and observability tools.
/error-trace
Error trace
Set up error tracking and monitoring — implement structured logging, configure alerts, and integrate with error tracking services for real-time error detection.
/multi-agent-review
Multi agent review
Coordinate specialized review agents in parallel or in sequence and synthesize their findings into one code review
/error-analysis
Error analysis
Analyze and resolve errors across the full application lifecycle — from stack traces to distributed tracing — using systematic root-cause analysis and observability tools.
/error-trace
Error trace
Set up error tracking and monitoring — implement structured logging, configure alerts, and integrate with error tracking services for real-time error detection.
/smart-debug
Smart debug
AI-assisted smart debugging — parse error messages, stack traces, and failure patterns to identify root causes and produce a fix with automated observability steps.
/code-migrate
Code migrate
Generate comprehensive migration plans and scripts for transitioning codebases between frameworks, languages, versions, or platforms with minimal disruption.
/deps-upgrade
Deps upgrade
Plan and execute safe, incremental dependency upgrades with minimal risk — including breaking-change migration paths and proper test verification.
/legacy-modernize
Legacy modernize
Orchestrate legacy system modernization using the strangler fig pattern with gradual component replacement
/component-scaffold
Component scaffold
Scaffold React and React Native components with TypeScript, tests, styles, and Storybook stories
/xss-scan
Xss scan
Scan React, Vue, Angular, and vanilla JavaScript code for XSS vulnerabilities and report fixes with secure coding examples
/full-stack-feature
Full stack feature
Orchestrate end-to-end full-stack feature development across backend, frontend, database, and infrastructure layers
/git-workflow
Git workflow
Orchestrate git workflow from code review through PR creation with quality gates
/onboard
Onboard
Create a role-specific onboarding plan for a new team member, from pre-arrival setup through the first 90 days
/pr-enhance
Pr enhance
Enhance a pull request with a generated description, review checklist, risk assessment, and test coverage report
/incident-response
Incident response
Orchestrate multi-agent incident response with modern SRE practices for rapid resolution and learning
Make any song you can imagine
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