LLM Mart Basic
@llm-mart · Joined Jun 2026
Route work into the right maestro tier and drive the SPEC/NOTES/VERIFY bundle lifecycle - open, resume, close, recall.
Settle unknowns and lock decisions before implementation - pick the mode per unknown (grill, research, prototype, model, wayfind), recall past bundles, walk one fork at a time, record every settled choice with a rationale, and open the bundle only when a Full trigger holds.
Verify and close - cross-check coverage, run the VERIFY table, deliver the verdict, harvest durable lessons into decisions, close the bundle, and never claim remote state from local evidence.
Drive one accepted implementation unit - smallest falsifiable behavior, minimum edits, evidence that names the real falsifier; red tests only inside a Full bundle.
Use the Unoplat Code Confluence CLI to run the local app, configure providers, add repositories, and generate AGENTS.md artifacts with automatic PR publication. Trigger when users mention Unoplat, Code Confluence, repository setup, AGENTS.md generation, or agent-md workflows.
Author and visually review evidence-based software architecture diagrams as D2 v0.7.1 source rendered with ELK to canonical SVG.
Guidance for TypeScript monorepos with mixed package managers. Use when codebase has inherited package manager and a workspace root.
Long-term Markdown project memory for AI coding agents. Use when the user wants to record, recall, audit, sync, or compress project decisions, architecture, conventions, monorepo scopes, or `.lore/` entries, including natural-language requests like "remember this decision" or exp
Refactor large DataRobot skill files by moving detailed content into directly linked reference files while preserving meaning. Use when a skill triggers context-window warnings, needs progressive disclosure, or should be chunked without changing guidance.
Use when the user wants to design, build, code, simulate, or deploy an AI agent (not a predictive model) to DataRobot; mentions agent_spec.md, dr-assist, datarobot-agent-assist, dress rehearsal, swarm simulation, or the DataRobot agent template; wants to scaffold a LangGraph, Cre
Guidance for setting up CI/CD pipelines for DataRobot application templates using GitLab, GitHub Actions, and Pulumi for infrastructure as code. Use when setting up CI/CD pipelines, configuring deployments, or managing infrastructure for DataRobot application templates.
Tools and guidance for data upload, dataset management, data validation, and preparing data for DataRobot projects. Use when uploading datasets, managing data, or validating data for DataRobot.
Use when the user wants to find DataRobot capabilities — skills, MCP servers, agents, or platform resources — for a task. Fetches the live DataRobot catalog directly so results are always current, regardless of third-party search index lag. Also checks the user's own DataRobot in
Instrument any external or existing AI agent with OpenTelemetry to send traces, logs, and metrics to DataRobot for monitoring, observability, and governance. Use when the user says "add tracing/observability/monitoring to my agent", wants to instrument an existing agent project i
Guidance for feature engineering, feature discovery, feature importance analysis, and understanding DataRobot's automated feature engineering capabilities. Use when working with feature engineering, feature discovery, or analyzing feature importance in DataRobot.
Tools and guidance for deploying DataRobot models, managing deployments, configuring prediction environments, and deployment operations. Use when deploying models, creating or updating deployments, or configuring prediction environments.
Tools and guidance for model explainability, prediction explanations, feature impact analysis, SHAP values, SHAP distributions, anomaly assessment, and model diagnostics. Use when analyzing model explanations, feature impact, SHAP values, SHAP distributions, anomaly assessment, o
Tools and guidance for monitoring model performance, tracking data drift, managing model health, and detecting prediction anomalies. Use when monitoring deployed models, tracking drift, or investigating prediction anomalies.
Comprehensive guidance for training models in DataRobot, including project creation, AutoML configuration, feature engineering, and model selection. Use when training models, creating AutoML projects, or selecting models in DataRobot.
Tools and guidance for making predictions with DataRobot deployments, including real-time predictions, batch scoring, prediction dataset generation, and prediction explanations (SHAP/XEMP). Use when making predictions, running batch scoring, generating prediction datasets, or exp
/do
Do
Smart router: classify requests and route to the correct agent + skill
/generate-claudemd
Generate claudemd
Generate project-specific CLAUDE.md from repo analysis.
/github-notifications
Github notifications
Triage GitHub notifications: fetch, classify, report actions needed.
/github-profile-rules
Github profile rules
`github-profile-rules` — extract programming rules and coding conventions from a GitHub user's public profile via API.
/gm-brilliant-implementation
Gm brilliant implementation
Run the complete 34-stage implementation workflow for a large, multi-system, multi-wave, or CPU-delegated 5 Star Booker GM program.
/install
Install
Plan, then apply, the VexJoy Agent install with the vexinstall engine
/pr-review
Pr review
Comprehensive PR review using specialized agents, with automatic retro knowledge capture
/reddit-moderate
Reddit moderate
Reddit moderation: fetch modqueue, classify content, take mod actions
/retro
Retro
Learning system interface: stats, search, graduate learnings. Backed by learning.db (SQLite + FTS5).
/system-upgrade
system-upgrade
Systematic upgrade pipeline for adapting agents, skills, and hooks when Claude Code ships updates, user goals change, or retro learnings accumulate.
/full-equity-research
Full equity research
agentii.full-equity-research — the spec 046 kit command. Use the Skill tool to run agentii:full-equity-research on this workspace.
/synthesize
Synthesize
agentii.synthesize — the spec 046 kit command. Use the Skill tool to run agentii:synthesize on this workspace.
/agent-diversity-review
Agent diversity review
Run the Agent Diversity Review gate and emit the result table
/create-specialist-agent
Create specialist agent
Scaffold a new spawnable specialist agent def and register it in the agent taxonomy
/customer-changelog-check
Customer changelog check
Audit whether user-visible changes in the current session have matching CHANGELOG.md entries; report MISSING with suggested lines; --fix auto-appends
/dashboard-cockpit
Dashboard cockpit
Repeatable pass upgrading an Angular admin dashboard into a compact black-and-cyan developer-cockpit PWA
/drift-check
Drift check
Run the drift-detection checklist (incl. agent-drift signals); report + fix in-turn
/final-review
Final review
Orchestrate the final review fan-out (integration + diversity + risk + release readiness)
/improve-lint
improve-lint
Run the AI-augmented lint self-improvement loop on the current project. Scans `.lint-history/` for recurring violation patterns (≥3 hits in 30d window), drafts a Claude-ready prompt to author a new semgrep rule for the top candidate, and surfaces the proposal under `.lint-history/proposals/<ts>.md`. Non-blocking analysis. See rules/lint-doctrine.md § Self-improving.
/install-lint-stack
install-lint-stack
Bootstrap industry-leading lint+autofix+commit-hygiene stack on the current project. Drops in lefthook, oxlint, ESLint, Prettier, Stylelint, markdownlint, ruff, shellcheck, shfmt, yamllint, hadolint, actionlint, jscpd, knip, semgrep, gitleaks, commitizen + git-cz-emoji (emoji-mandatory commits), and semantic-release. Idempotent — re-runs upgrade safely. See rules/lint-doctrine.md.
Make any song you can imagine
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