LLM Mart Basic

Run the Upkeep repo audit locally against any repository path. Use when asked to audit a repo, check docs/spec/asset drift, or run upkeep without GitHub Actions.

Open-source infrastructure that turns scattered SKILL.md files into curated, retrieval-ready agent-skill corpora—with retrieval and evaluation tooling included.

Apply Chain-of-Verification (CoVe) prompting to improve response accuracy through self-verification. Use when complex questions require fact-checking, technical accuracy, or multi-step reasoning.

TRIGGER when: adding or upgrading any dependency — library, SDK, framework, API, IaC API version (K8s/Terraform/Helm), CRD, or container image. Use BEFORE writing the call. Forces context7/capy lookup instead of guessing.

Use in pre-implementation (idea-to-design) stages to understand spec/requirements and create a correct implementation plan before writing actual code. Turns ideas into a fully-formed PRD/design/specification and implementation-plan. Creates design docs and task lists in docs/feat

Compare two versions of a skill's instructions to detect degradations and complexity increases. TRIGGER when: user asks to diff, compare, or review changes to a skill's markdown instructions. Asymmetric check — only regressions count. Additions, clarifications, and strengthenings

After implementing a new feature or fixing a bug, make sure to document the changes. Use when writing documentation, after finishing the implementation phase for a feature or a bug-fix.

TRIGGER when: user asks to implement, fix, build, or work on something — whether from a docs/feat/wip plan OR a standalone task (bug fix, GitHub issue, one-off change). Examples: "work on task 1", "fix this bug", "implement feature X from the issue". Provides structured execution

Compare and merge two design docs for the same feature into a single source of truth. Use when you have competing or complementary design/implementation docs (e.g. from separate design runs) that need reconciling into one unified document.

Produce or update a domain-reference kit — the durable glossary + divergences/traps pages for a bounded context. Use for domain modelling, information architecture, building a domain glossary or domain model, mapping domain concepts to code (bindings), or surfacing the decidable

Apply Chain-of-Verification (CoVe) prompting to improve response accuracy through self-verification. Use when complex questions require fact-checking, technical accuracy, or multi-step reasoning.

TRIGGER when: adding or upgrading any dependency — library, SDK, framework, API, IaC API version (K8s/Terraform/Helm), CRD, or container image. Use BEFORE writing the call. Forces context7/capy lookup instead of guessing.

Use in pre-implementation (idea-to-design) stages to understand spec/requirements and create a correct implementation plan before writing actual code. Turns ideas into a fully-formed PRD/design/specification and implementation-plan. Creates design docs and task lists in docs/feat

Compare two versions of a skill's instructions to detect degradations and complexity increases. TRIGGER when: user asks to diff, compare, or review changes to a skill's markdown instructions. Asymmetric check — only regressions count. Additions, clarifications, and strengthenings

After implementing a new feature or fixing a bug, make sure to document the changes. Use when writing documentation, after finishing the implementation phase for a feature or a bug-fix.

Compare and merge two design docs for the same feature into a single source of truth. Use when you have competing or complementary design/implementation docs (e.g. from separate design runs) that need reconciling into one unified document.

Produce or update a domain-reference kit — the durable glossary + divergences/traps pages for a bounded context. Use for domain modelling, information architecture, building a domain glossary or domain model, mapping domain concepts to code (bindings), or surfacing the decidable

Designs, reviews, revises, and templates deployable system prompts for agent projects. Use whenever the user asks to write, improve, review, or debug an agent's system prompt, system message, or role/boundary instructions — including Chinese requests such as 系统提示词、角色设定、角色和边界规范、提示

Делает одностраничный сводный снимок бизнеса для собственника — позиция по деньгам (бухгалтерия), тренд продаж (платежи), движение по воронке (CRM), обязательства недели (календарь), срочный список наблюдения (почта, мессенджер) и единственное самое важное на сегодня. Сам пробует

Ранжирует топ-5 лидов, которым стоит позвонить сегодня, готовит тезисы разговора из истории переписки, бронирует время в календаре и набрасывает follow-up сообщения. Принимает необязательные аргументы количества и даты. Командная форма: вызывается по имени.

A green PR, a controller reporting success, and not one line of the new code running

Plan and organize sprint workflows

Generate daily standup reports

Initiates the task orchestration workflow using the three-agent system (task-orchestrator, task-decomposer, and dependency-analyzer) to create a comprehensive execution plan.

Check the current status of tasks in the orchestration system with various filtering and reporting options.

Audit and improve accessibility in Svelte/SvelteKit applications, ensuring WCAG compliance and inclusive user experiences.

Create new Svelte components with best practices, proper structure, and optional TypeScript support.

Help debug Svelte and SvelteKit issues by analyzing error messages, stack traces, and common problems.

Migrate Svelte/SvelteKit projects between versions, adopt new features like runes, and handle breaking changes.

Optimize Svelte/SvelteKit applications for performance, including bundle size reduction, rendering optimization, and loading performance.

Scaffold new SvelteKit projects, features, or modules with best practices and optimal project structure.

Migrate Storybook configurations and stories to newer versions, including Svelte CSF v5 and @storybook/sveltekit framework.

Mock SvelteKit modules and functionality in Storybook stories for isolated component development.

Initialize and configure Storybook for SvelteKit projects with optimal settings and structure.

Create comprehensive Storybook stories for Svelte components using modern patterns and best practices.

Diagnose and fix common Storybook issues in SvelteKit projects, including build errors, module problems, and configuration issues.

General-purpose Storybook assistance for SvelteKit projects, including setup guidance, best practices, and common tasks.

Analyze test coverage, identify testing gaps, and provide recommendations for improving test coverage in Svelte/SvelteKit projects.

Troubleshoot and fix failing tests in Svelte/SvelteKit projects, including debugging test issues and resolving common testing problems.

Set up comprehensive testing infrastructure for Svelte/SvelteKit projects, including unit testing, component testing, and E2E testing frameworks.

Create comprehensive tests for Svelte components and SvelteKit routes, including unit tests, component tests, and E2E tests.

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.