LLM Mart Basic

Use when "launch plan", "go-to-market", "GTM strategy", "how to launch", "launch checklist", "growth plan", or product is built and needs distribution strategy. Do NOT use for content generation (/content-gen) or SEO audit (/seo-audit).

Use when "privacy policy", "terms of service", "legal pages", "GDPR", "CCPA", "app store legal", or project needs compliance documents. Do NOT use for NDA or contracts.

Use when "audit memory", "check CLAUDE.md", "memory map", "rules audit", "context budget", "what loads in my session", or need to optimize agent context loading.

Use when "set up metrics", "track KPIs", "PostHog events", "funnel analysis", "when to kill or scale", "success metrics", or need analytics plan. Do NOT use for SEO metrics (/seo-audit).

Take a trained neural model to devices — ONNX export, int8 quantization, Core ML conversion, on-device benchmarking, download-on-demand delivery. Use when user says "сожми модель", "quantize the model", "convert to Core ML / ONNX", "model is too big for the app", "run the model o

Use when "run pipeline", "automate research to PRD", "full pipeline", "research and validate", "scaffold to build", "loop until done", "chain skills", or need multi-skill automation. Do NOT use for single skills (use the skill directly).

Use when "plan this feature", "create implementation plan", "write a spec", "battle plan", describing a feature/bug/refactor, or need task breakdown before building. Do NOT use for idea validation (/validate) or execution (/build).

Use when "reddit comment", "engage reddit", "build karma", "post to reddit", or need to participate in Reddit threads. NOT for thread discovery (/community-outreach) or social copy (/content-gen).

Use when "research this idea", "find competitors", "check the market", "domain availability", "market size", "analyze opportunity", or need evidence before validation. Do NOT use for idea scoring (/validate) or SEO auditing (/seo-audit).

Use when "retro", "evaluate pipeline", "what went wrong", "pipeline review", "check pipeline logs", or after pipeline completes and needs quality assessment.

Use when "review code", "quality check", "is it ready to ship", "final review", or after /build or /deploy completes. Do NOT use for planning (/plan) or building (/build).

Use when "scaffold project", "create new project", "start new app", "bootstrap project", "set up from PRD", or need project from PRD + stack template. Do NOT use for planning features (/plan) or PRD generation (/validate).

Use when "check SEO", "audit this page", "SEO score", "check meta tags", "SERP position", or need website SEO health check. Do NOT use for landing content (/landing-gen) or social media posts (/content-gen).

Use when "set up workflow", "configure TDD", "wire up dev workflow", or after /scaffold before /plan to generate workflow config. Do NOT use for founder setup (/init) or scaffolding (/scaffold).

Use when "design schemas", "structured output", "agent loop", "SGR", "constrained decoding", "tool dispatch", "Pydantic schema for LLM", or need to design a schema-guided reasoning pipeline for an agent or API. Do NOT use for general code review (/review) or planning (/plan).

Use when "audit skill", "review skill quality", "check skill", "skill score", "skill checklist", "is this skill good", or evaluating skill against best practices. Do NOT use for KB audits (/audit) or code review (/review).

Use when "help me decide", "should I do this", "evaluate decision", "STREAM analysis", "run decision framework", "pros and cons", or facing a high-stakes founder choice. Do NOT use for idea validation with PRD (/validate).

Use when "swarm research", "parallel research", "investigate fast", "3 agents", "team research", or want faster multi-angle alternative to /research. Do NOT use for solo research (/research) or idea scoring (/validate).

Build and hold a SwiftUI design system — a 12-column grid, spacing/type/radius/motion scales, surface levels, a component gallery, and the guards that stop it drifting back. Use when the user says "сделай по сетке", "дизайн-система", "разъезжается вёрстка", "магические числа в pa

Use when "validate idea", "score this idea", "should I build this", "go or kill", "generate PRD", "evaluate opportunity", or need idea scoring with PRD output. Do NOT use for deep research (/research first) or decision-only framework (/stream).

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.

Validate application configuration with schemas, per-environment rules, runtime checks, and secure handling of sensitive values

Preflight a DGX Spark system for an ML training or inference workload and emit env-report.json

Set up debugging and tracing with remote debugging, distributed tracing, debug logging, profiling, and production diagnostics

Generate API, architecture, code, and user documentation from a codebase and automate keeping it current

Analyze and resolve errors across the full application lifecycle — from stack traces to distributed tracing — using systematic root-cause analysis and observability tools.

Set up error tracking and monitoring — implement structured logging, configure alerts, and integrate with error tracking services for real-time error detection.

Coordinate specialized review agents in parallel or in sequence and synthesize their findings into one code review

Analyze and resolve errors across the full application lifecycle — from stack traces to distributed tracing — using systematic root-cause analysis and observability tools.

Set up error tracking and monitoring — implement structured logging, configure alerts, and integrate with error tracking services for real-time error detection.

AI-assisted smart debugging — parse error messages, stack traces, and failure patterns to identify root causes and produce a fix with automated observability steps.

Generate comprehensive migration plans and scripts for transitioning codebases between frameworks, languages, versions, or platforms with minimal disruption.

Plan and execute safe, incremental dependency upgrades with minimal risk — including breaking-change migration paths and proper test verification.

Orchestrate legacy system modernization using the strangler fig pattern with gradual component replacement

Scaffold React and React Native components with TypeScript, tests, styles, and Storybook stories

Scan React, Vue, Angular, and vanilla JavaScript code for XSS vulnerabilities and report fixes with secure coding examples

Orchestrate end-to-end full-stack feature development across backend, frontend, database, and infrastructure layers

Orchestrate git workflow from code review through PR creation with quality gates

Create a role-specific onboarding plan for a new team member, from pre-arrival setup through the first 90 days

Enhance a pull request with a generated description, review checklist, risk assessment, and test coverage report

Orchestrate multi-agent incident response with modern SRE practices for rapid resolution and learning

Your Personal AI Assistant; easy to install, deploy on your own machine or on the cloud; supports multiple chat apps with easily extensible capabilities.

Agent OS: the agent gets smarter on its own. We just hold the line: the grading command and expected result never make it into the success contract we hand it.…

Curated systems, benchmarks, and papers etc. on memory for LLMs/MLLMs --- long-term context, retrieval, and reasoning.

:memo: Vimlike Modal Text Editor in Rust

CI-native security testing for MCP servers. Attack simulation, schema drift detection, and health scoring before agents depend on them.

Hermes Agent memory plugin/provider for scope-aware recall, SQLite truth, LanceDB semantic search, and hybrid retrieval.

For You Agent——AI 时代的个人随身数字人格。把你的模型、AI 账号、技能、提示词和工作方式，带到每一个 AI 工具里。

A coding agent: give it a prompt and it reads, writes, runs commands, and searches code in a loop until the work is done, using native tool-calling across OpenA…

A secure persistent personal agent server in Rust. One binary, sandboxed execution, multi-provider LLMs, voice, memory, Telegram, WhatsApp, Discord, Teams, and…

Self-evolving agent: grows skill tree from 3.3K-line seed, achieving full system control with 6x less token consumption

DeepSeek Harness Desktop (dsh-desktop). EAC: Embracing All Creation (揽尽万象). Bundled Node.js runtime with full dsh-CLI kernel, one-click startup, 10 built-in UI…

See your agent think. Zero-config observability & governance for 26 AI agent runtimes: Claude Code, Cursor, OpenAI Codex, GitHub Copilot, Gemini CLI, Cline, Ope…

Save 94% on AI coding tokens. Index your codebase, agents search instead of reading files. Works with Claude Code, Codex, Copilot, Cursor, Gemini CLI. Local MCP…

Yet another coding agent harness, lightweight and written in go.

a coding Agent from pi. ∞ providers, sub-agents, hashline edits, and a permission gate

Omnigent is an open-source AI agent framework and meta-harness: orchestrate Claude Code, Codex, Cursor, Pi, and custom agents — swap harnesses without rewriting…

🧠 Leon is your open-source personal assistant.

The Station, an open-world multi-agent environment that models a miniature scientific ecosystem.

The Frontend Stack for Agents & Generative UI. React, Angular, Mobile, Slack, and more. Makers of the AG-UI Protocol

VelaTerm = iTerm2 + Codex, The Best Terminal for AI Coding