LLM Mart Basic

Generates a Mermaid sequence diagram showing how data moves between components. Use when tracing request flows or documenting data transformation pipelines.

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.

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.

Generate a typed client from an Anchor or Shank IDL (Codama or Anchor TS)

Migrate TypeScript from @solana/web3.js 1.x to @solana/kit

Plan a Solana feature before coding: accounts, PDAs, instructions, risks, tests

First-time-user product review: scorecard and fix roadmap; --harsh for a roast

Measure compute units per instruction and flag the expensive ones

Format, lint and commit with a conventional message on a kit-named branch

Resync external skill submodules to latest upstream versions

Scaffold a Solana project (Anchor, fullstack, frontend, Pinocchio) with the kit

Set up GitHub Actions CI for Solana programs: lint, build, test, audit

Configure MCP server API keys in .env and add the optional MCP servers

Run tests, auto-fix fmt and lint, and fix failures until green or stuck

Run C# tests: Unity Test Framework in batchmode, or dotnet test

Run Rust tests for programs (LiteSVM, Mollusk, Surfpool, Trident) and backends

Run TypeScript tests for programs (Anchor TS, Kit) and dApp frontends

Update solana-ai-kit to latest version from upstream

Write docs for a Solana program, SDK or component from its code and IDL

Allow all file creation (short for /ar:allow)

Set AutoFile policy to allow-all mode (full file creation permissions)

Set AutoFile policy to justify-create mode (require justification for new files)

Set AutoFile policy to strict-search mode (only modify existing files)

The fastest way to put Volcengine Ark in your terminal and your AI agent — go from prompt to generated media, multimodal answer, or deployed endpoint in a sin…

本地私有、开源的自进化跨平台 AI 内容发现 Agent：先理解你，再主动从 B站、小红书、抖音、YouTube、X、知乎、Reddit、微博等平台与开放 Web 寻找内容。（支持 deepseek harness 插件） | Local-first open-source cross-platform AI cont…

Persistent memory for AI coding agents — one verified kb_search replaces the grep/find/ls orientation loop. Cross-repo, CPU-only, zero token spend.

AI 时代的伯克希尔：基于 Claude Code / Codex 的价值投资研究框架。巴菲特·芒格·段永平·李录四大师方法论 + 多Agent并行研究。| AI-era Berkshire: a value investing research framework built for Claude Code / Co…

The batteries-included, No-Code FinOps automation platform, with the AI you trust.

Open-source 3D AI agent framework — GLB/glTF avatars with LLM brains, memory, emotions, and autonomous payments. MCP server · x402 · Solana/EVM · Three.js. Embe…

XLSX parser for LLMs, RAG, LangChain, LangGraph, CrewAI, Claude, MCP — turns Excel (.xlsx) into citation-ready JSON with formulas, charts, dependency graphs, an…

Hermes-Relay — Your Hermes AI agent, in your pocket — chat, voice, and control.

A minimalist, terminal-native coding agent written in C.

AI-powered OSINT agent with interactive REPL, MCP server, and CLI. 19 tools. Works with Claude, GPT-4, or local models. For authorized security research only.

AI pair programming in your terminal — one static binary, sub-ms startup, any model

Where data access meets operational intelligence

Build your own security agents. Open-source framework for agents with live, read-only access to your infrastructure, with no path to widen it. Reasons across AW…

Multi-workspace terminal aggregator with Claude Code AI integration

Go implementation of AI coding agent

Harness engineering beginner tutorial, from 0 to 1

Generate images directly in DeepSeek Harness chats

A smarter, self-hosted AI assistant — multi-user, multi-agent.

Turn any research paper into a commercialization report — 6 AI agents, TRL/MRL scoring, patent landscape, market intelligence, verified citations. DeepSeek / Op…

Power BI CLI - semantic models (.NET TOM) and PBIR reports for token-efficient AI agent usage, built for Claude Code