LLM Mart Basic

Autonomous task executor for spec-kit development. Executes a single task from tasks.md, verifies, commits, and signals completion.

Expert task planner for breaking plans into executable tasks. Masters POC-first workflow, task sequencing, quality gates, and constitution alignment.

This agent should be used to "create technical design", "define architecture", "design components", "create design.md", "analyze trade-offs". Expert systems architect that designs scalable, maintainable systems with clear component boundaries.

This agent should be used to "generate requirements", "write user stories", "define acceptance criteria", "create requirements.md", "gather product requirements". Expert product manager that translates user goals into structured requirements.

This agent should be used to "run verification task", "check quality gate", "verify acceptance criteria", "run [VERIFY] task", "execute quality checkpoint". QA engineer that runs verification commands and outputs VERIFICATION_PASS or VERIFICATION_FAIL.

This agent should be used to "update spec files", "refactor requirements", "revise design", "modify tasks after execution", "incrementally update specifications". Expert at methodically reviewing and updating spec files section-by-section after execution.

This agent should be used to "execute a task", "implement task from tasks.md", "run spec task", "complete verification task". Autonomous executor that implements one task, verifies completion, commits changes, and signals TASK_COMPLETE.

This agent should be used to "review artifact", "validate spec output", "check quality", "review research output", "review requirements", "review design", "review tasks", "review execution", "review prototype evidence". Read-only reviewer that validates artifacts against type-spe

This agent should be used to "create tasks", "break down design into tasks", "generate tasks.md", "plan implementation steps", "define quality checkpoints". Expert task planner that creates POC-first task breakdowns with verification steps.

This agent should be used to "decompose a large feature", "triage a big task", "break down into multiple specs", "create epic decomposition", or needs guidance on splitting large features into dependency-aware spec graphs.

dstack is an open-source control plane for GPU provisioning and orchestration across GPU clouds, Kubernetes, and on-prem clusters.

Create and manage dstack presets: a toolkit that streamlines model inference optimization with agents, and a portable preset format. Use together with the dstack skill, and only when the user explicitly asks to create a preset or manage existing presets, not for deploying or serv

Use with the dstack skill for model-serving work when the image, serving command, resources, backend/fleet choice, or service behavior is not proven. Guides task-first prototyping on real hardware, choosing fleets/backends that can reuse idle instances and caches, checking vLLM/S

Extract semantic structure and transferable style grammar from academic figures, PDFs, and paper or figure URLs for analysis, redraws, or reference-conditioned generation. Do not use it for paper-text-only figure planning.

Unified academic figure designer, semantic color & surface decision engine, and FigureSpec v1 compiler. Handles style selection, reference palette derivation, colorblind-safe token binding, SVMC visual metaphors, and normalized compact prose prompt compilation across classic-tech

Plan evidence-backed figures for a paper draft, markdown notes, outline, manuscript, PDF, or paper webpage. Supports Draft-to-Figure fast-track for Markdown notes as well as comprehensive multi-figure planning for full manuscripts.

Plan, generate, inspect, and refine academic figures from repositories, papers, draft notes, paper URLs, PDFs, or reference images. Supports fast-track draft-to-figure generation and user passthrough mode.

Analyze ML, AI4Science, Systems, and research repositories into an evidence-backed semantic architecture graph for paper figure planning. Code serves as supporting evidence; paper narrative and user intent remain the primary source of truth.

Coordinate agents via the AMQ CLI for file-based inter-agent messaging. Use this skill whenever you need to send messages to another agent (codex, claude, or any named handle), check your inbox, drain queued messages, set up co-op mode between agents, join a swarm team, route mes

Parallel-research-then-converge design workflow between two agents. Use this skill when the user wants two agents to independently think through a design problem before aligning on a solution — "spec X with codex", "design X together", "both agents think through X", "brainstorm a

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.

Iniciar planificación de archivos estilo Manus. Crear task_plan.md, findings.md, progress.md para tareas complejas.

Bridge Claude Code's /goal to the active plan. Derives a goal condition from task_plan.md and invokes /goal so Claude keeps working until the plan is complete. Available since v2.38.0.

Run a planning-aware cadence with Claude Code's /loop. Default tick checks plan status, runs check-complete, nudges progress.md update if stalled. Available since v2.38.0.

启动 Manus 风格的文件规划。为复杂任务创建 task_plan.md、findings.md、progress.md。

啟動 Manus 風格的檔案規劃。為複雜任務建立 task_plan.md、findings.md、progress.md。

Start Manus-style file-based planning. Creates task_plan.md, findings.md, progress.md for complex tasks.

Short alias for /plan. Starts Manus-style file-based planning: task_plan.md, findings.md, progress.md. Available since v3.0.0.

Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls. Now with automatic session recovery after /clear

Show current planning status at a glance - phases, progress, and any logged errors.

Run three-agent adversarial analysis -- advocate builds the case, adversary challenges it, judicial analyst synthesizes

Structured pre-execution briefing session -- collects case context through specialist panel, builds execution plan, supports resume and depth control

Analyze cantonal law for all 26 Swiss cantons -- cantonal court decisions, cantonal legislation, procedural specifics, and interaction with federal law

Validate, format, and look up Swiss legal citations including BGE/ATF/DTF decisions and statutory references

Create a reusable custom workflow by combining BetterCallClaude agents. Interview-based: pick agents, order them, define the output. Saved for future use with /bettercallclaude:workflow.

Analyze Swiss legal documents -- identify legal issues, extract key clauses, verify citations, and assess compliance

Diagnose MCP server connectivity — tests each server, reports status and impact in plain language, suggests fixes for issues.

Draft Swiss legal documents including contracts (OR), court submissions (ZPO), and legal opinions (Gutachten) with multi-lingual support

Force Federal Law Mode for Swiss federal legal analysis, overriding cantonal auto-detection

Show complete BetterCallClaude command reference, available agents, skills, and usage examples

Execute the BetterCallClaude 5-step Swiss legal framework: intake → research → strategy → adversarial → draft. A complete end-to-end pipeline for any Swiss legal matter, from document analysis through final legal output.

Make any song you can imagine

Leading AI-powered video generation platform that specializes in creating hyper-realistic talking avatars

Hermes Agent is an open-source, self-improving autonomous AI agent developed by Nous Research

Kilo Code is a popular, open-source AI coding agent and "agentic engineering" platform designed to help developers build, refactor, and debug software faster

General-purpose agent in one static Go binary. ReAct loop, ACP server for IDEs, OpenAI-compatible REST API with embedded web UI, Telegram gateway, cron schedule…

Autonomous agent framework with structured memory, safety hooks, and loop management. Built by the agent that runs on it.

TSP自托管、零运维的 A 股「选股 + 监控 + 回测」量化工作台 | 基于 TickFlow 数据源 | LLM能力驱使策略定制+个股分析+复盘 | 自由接入第三方数据源与个性化扩展数据 | 个人开源 ,非TickFlow官方项目

Curated, verified Agent Skills powered by ModelStudio.

Run Claude Code, Codex, Antigravity, Cursor Agent and OpenCode as one runtime — persistent sessions, multi-agent councils, an OpenAI-compatible endpoint, an MCP…

pi had nothing (nothing), so I made something (something) — sorry mariozechner-senpai, I went ahead and lovingly soiled your pure pi for you. opinionated fork o…

A persistent workspace for development work that self-improves and continues beyond one session.

Open-source memory and context for user-aware agents: scoped memory, provenance, retrieval quality, correction, boundaries, evals, and MCP/HTTP access.

📚 A zero-dependency, git-backed micro-lesson library for AI Agents to asynchronously share and search verified debugging experience. Python stdlib only. | http…

Deterministic, local-first memory and guardrails for AI coding agents with no LLM in the hot path.

Deterministic spec-orchestration for local LLMs in the pi coding agent — drives prompts through refine→research→grill→compose→critique, with bundled web/docs/fe…

Native Safari browser automation for AI agents. 97 tools via AppleScript — zero overhead, keeps logins, runs silently in background. Drop-in alternative to Chro…

Agent OS: keep specialist agents in a hub, spin up a temporary orchestrator per task. Local-first, works with any model.

Git for agent memory. Branches, diffs, PRs, and rollback for what your agents know.

Multi-Provider AI Gateway - No personal logs by design. Model autodiscovery, Failover groups, High availability, Android companion app, and more - "Because we h…

Production-grade MCP server for MikroTik RouterOS with secure AI-native network automation.