LLM Mart Basic

Import knowledge from existing documents into structured KB entries. Reads source documents (Markdown, PDF, DOCX, plain text), extracts key information, and creates properly formatted KB entries with YAML frontmatter.

Add new sources to your knowledge base or re-scrape existing ones to pick up changes. Supports Notion, Slack, Confluence, and local files. Can be run anytime after /setup-knowledge-base.

Interactive onboarding to create a structured knowledge base. Defines categories, scaffolds the directory structure, creates the index, and optionally imports initial content. Run this first before using /kb-answer or /kb-import.

Mine the user's Claude Code + Cowork session history into a structured task profile, what they do with AI, how often, how successfully where friction lives, then propose atomic skills that would reduce iteration. Use when the user asks to "analyse my Claude use", "build a task pr

Personal diagnosis of where your Claude Code + Cowork spend goes. Reads local transcripts, prints your conversation length distribution, marathon share, cache rebuild costs, and per-project diagnosis (good projects and problem projects) right in the terminal. Then offers a deeper

Turn a task, plan, or feature request into a ready-to-paste Claude Code /goal command — a single completion condition with a measurable end state, a demonstrable proof, and the constraints that must not drift. Use when the user says "give me a goal", "goal prompt", "make this a /

Write a session handoff at the end of a session so the next session can start from where this one stopped without rereading the whole conversation. Use when user says "handoff", "wrap up", "write a handoff", "end of session", "park this session", "save where we are", or to RESUME

Find context from past Claude Code (CLI) and Claude Cowork (desktop) sessions on this Mac. Use when the user wants to recall something they did before but can't find it , phrasings like "where did I work on X", "find that session where I…", "when did I last do Y", "pull up the co

Build an MCP server end to end, tailored to how it will be used. Use when asked to build an MCP, create an MCP server, wrap an API as a tool, make a tool for Claude, expose a service to an agent, build a Claude connector, or turn a service into MCP tools. Asks up front who the se

Bundle skills, hooks, agents, or an MCP server into one installable Claude Code plugin and ship it. Use when asked to bundle tools into one plugin, package tools for a colleague, publish a plugin to a marketplace, share my tools with my team, or ship a plugin. Asks who it's for a

Check thesis chapters for consistency before submission — contradictory numbers, terminology drift, and broken cross-references — and read every rewritten or proposed sentence against the one it replaces before anyone sees the rewrite.

Convert thesis chapters and reading notes from Markdown to Word (.docx) and package for submission. Use when preparing materials for supervisors or examiners.

Map reading notes to thesis chapters and integrate key arguments into the manuscript. Use after completing a reading session to weave new material into the thesis.

Show literature-to-chapter coverage and the writing progress dashboard — which sources support which chapters, and word counts against targets.

Record reading notes to a structured notes file. Use when the user says "take notes" or "record this" during reading sessions.

Read PDF page by page with structured output — key arguments, terms glossary, thesis connections. Use when reading academic papers, books, or articles.

Open a reader panel. Instruction-bound amnesiac sub-agents read the abstract or introduction and report what they carried away, set against the author's intended points. Use when those sections change or the writing loop marks the panel stale.

Review a manuscript or chapter as an external reviewer, or run an own-work self-review in a fresh-context clean room, producing anchored findings and a recommendation.

Use when checking BibTeX reference records for missing fields, malformed identifiers, duplicate keys, or metadata mismatches before submission.

Build evidence-controlled literature reviews and gap maps with source-status labels, claim registers, citation-role plans, traceability tables, and overclaim audits. Use when drafting or auditing review papers, thesis literature reviews, scoping reviews, or evidence syntheses whe

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.

Systematic workflow for fixing bugs including issue creation, branch management, and PR submission

Bulk import GitHub issues to Linear

Explore multiple business timeline scenarios with constraint validation and decision optimization.

Demo changelog automation features

Perform comprehensive analysis of $ARGUMENTS to identify code quality issues, security vulnerabilities, and optimization opportunities.

Run project checks and fix any errors without committing

Setup continuous integration pipeline

Clean up merged and stale git branches

Fix all linting and formatting issues across the codebase

Test multiple code variations through simulation before implementation with quality gates and performance prediction.

Perform comprehensive code quality review

Convert code analysis to Linear tasks

Perform comprehensive code analysis with quality metrics and recommendations

Automatically create and execute a git commit using the first suggested commit message

Create well-formatted git commits with conventional commit messages and emoji

Model world constraints with assumption validation, dependency mapping, and scenario boundary definition.

Containerize application for deployment

Load project context by reading README.md and exploring relevant project files

Generate comprehensive architecture documentation

Create a new command following existing patterns and organizational structure

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.