LLM Mart Basic

Entry-point skill for designing robotics applications with AI agents. Turns requirements (robot type, task, hardware, sim-vs-real, GPU/budget) into a full stack decision — middleware, simulation, data, visualization, training frameworks — plus a scaffold plan and a written archit

Entry-point skill for designing robotics applications with AI agents. Turns requirements (robot type, task, hardware, sim-vs-real, GPU/budget) into a full stack decision (middleware, simulation, data, visualization, training frameworks) plus a scaffold plan and a written architec

Entry-point skill for designing robotics applications with AI agents. Turns requirements (robot type, task, hardware, sim-vs-real, GPU/budget) into a full stack decision (middleware, simulation, data, visualization, training frameworks) plus a scaffold plan and a written architec

Entry-point skill for shaping new robotics applications: lightweight brainstorming, project ideation, requirement/risk discovery, stack selection, comparing or choosing simulators/models/visualization, a first user-visible slice, and a concise architecture decision record. Use wh

Entry-point skill for shaping new robotics applications: lightweight brainstorming, project ideation, requirement/risk discovery, stack selection, comparing or choosing simulators/models/visualization, a first user-visible slice, and a concise architecture decision record. Use wh

Deploy headless robotics / sim / demo containers to Google Cloud Run: the build → Artifact Registry → Cloud Run path plus the gotchas that bite sim workloads (no UDP multicast for gz-transport/DDS, CPU allocated only while a request is open, session affinity for per-visitor insta

Deploy headless robotics / sim / demo containers to Google Cloud Run: the build → Artifact Registry → Cloud Run path plus the gotchas that bite sim workloads (no UDP multicast for gz-transport/DDS, CPU allocated only while a request is open, session affinity for per-visitor insta

Deploy headless robotics / sim / demo containers to Google Cloud Run: the build → Artifact Registry → Cloud Run path plus the gotchas that bite sim workloads (no UDP multicast for gz-transport/DDS, CPU allocated only while a request is open, session affinity for per-visitor insta

Data sourcing strategy for robotics and physical-AI: choose between offline datasets (HuggingFace hub, Open X-Embodiment and similar), simulation-generated data, and teleop/real-robot collection; plan storage formats, episode structure, and dataset versioning. Use when: 'where do

Data sourcing strategy for robotics and physical-AI: choose between offline datasets (HuggingFace hub, Open X-Embodiment and similar), simulation-generated data, and teleop/real-robot collection; plan storage formats, episode structure, and dataset versioning. Use when: 'where do

Data sourcing strategy for robotics and physical-AI: choose between offline datasets (HuggingFace hub, Open X-Embodiment and similar), simulation-generated data, and teleop/real-robot collection; plan storage formats, episode structure, and dataset versioning. Use when: 'where do

Set up and start Infinite Pokémon when a user asks to install, play, or launch the game with their AI assistant. Clone missing game files, install dependencies, build, and launch the local desktop or browser game; not for background map or NPC generation jobs.

Design interior furniture layouts, rugs, room names and inspectable details for new Infinite Pokémon maps within the supplied protected room and service constraints.

Generate grounded NPC intentions, dialogue, memory and optional movement policies for Infinite Pokémon from a supplied saved-run observation or map-design context.

Generate Infinite Pokémon map stories, terrain features, content profiles, NPC policies, and interiors from a host-supplied saved-run snapshot and output schema.

This skill should be used when the user asks to "write a chapter", "next chapter", "chapter outline", "draft chapter", "continue the story", "write a scene", "outline a chapter", or wants to write prose for a story project.

This skill should be used when the user asks to "create a character", "update a character", "add a character", "build a family tree", "character relationships", "character timeline", "character arc", "character profile", or needs to manage characters in a story project.

This skill should be used when the user asks to "pantsing", "discovery write", "write without an outline", "discovery draft", "write into the dark", "story kernel", "reconcile a chapter", "reverse outline", "cut a subplot", "dead end", "drafting sprint", "writing cadence", or wan

This skill should be used when the user asks to "process beta reader feedback", "alpha reader feedback", "feedback round", "synthesize reader feedback", "reader notes", "beta feedback", "readiness check", or wants to collect, reconcile, and act on external reader feedback for a s

This skill should be used when the user asks to "mystery", "fair play", "clue", "red herring", "romance beats", "HEA", "thriller", "ticking clock", "horror", "dread", "MG", "YA", "middle grade", "young adult", "science fiction", "sci-fi", "serial", "episodic", "web serial", "genr

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.

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.

Assess ENCODE experiment quality using audit counts and replicate counts

Search ENCODE experiments by assay, organ, biosample, or target

Track ENCODE experiments locally with publications and provenance

List, search, and inspect ENCODE files by format, type, and assembly

Generate ENCODE citations for publications, grants, and presentations

Check if two ENCODE experiments are compatible for combined analysis

Cross-reference ENCODE data with PubMed, GEO, ClinicalTrials, and bioRxiv

Download ENCODE files (BED, FASTQ, BAM, bigWig) with MD5 verification

Log derived files and trace provenance back to ENCODE source data

Store, check, or clear ENCODE API credentials for restricted data

Assess ENCODE experiment quality using audit counts and replicate counts

Search ENCODE experiments by assay, organ, biosample, or target

Track ENCODE experiments locally with publications and provenance

Record the present state by mode — decision (ADR, RFC, rule), code (spec, doc, guide, scenario), or research (a ready report or one external material); a gate picks the document type.

First-time Archcore setup — wire host configs, measure the authored context, compose the full first-day seed in one preview, and create it on one confirm; import converts CLAUDE.md, AGENTS.md, rule files, ADRs, and docs into native documents; refresh adds new facts or drills into one domain.

Plan a feature or initiative through a computed route — a small fix exits with no documents, one capability gets a spec and a plan, a large initiative gets an umbrella PRD with one spec per capability; start with sdd, sources (market research), iso (regulated work), or research (technical investigation) to run that path directly.

Review branch changes against Archcore docs, or report project health; drift runs staleness detection, deep a full documentation audit, closeout closes a finished feature, experience captures a repeated pattern.

Verify that citations actually exist and that the claims they support are faithful to the cited source. Runs deterministic existence checks (Crossref / OpenAlex / Semantic Scholar / arXiv) plus a claim-faithfulness pass via the alterlab-citation-verifier skill.

Run a systematic, reproducible literature review on a topic and return an APA 7.0 annotated bibliography with a documented search strategy. Invokes the alterlab-deep-research pipeline in lit-review mode.

Run a full multi-perspective peer review of a manuscript, simulating an Editor-in-Chief plus three peer reviewers and a Devil's Advocate, and produce a structured editorial decision and revision roadmap. Invokes the alterlab-paper-reviewer skill.

Ultra-lightweight, open-source, self-hosted personal AI agent framework in Python with WebUI, tools, memory, MCP, multi-agent workflows, automation, and chat ap…

Governance framework for AI coding agents. It runs them through a five-step workflow (plan, build, review, test, ship) where no step counts as done without evid…

Ultimate Multi-Agent OS for Autonomous AI NPCs 2026

Personal AI Agent Hub 2026 — Build Your 24/7 Autonomous Assistant

Proven 2026 Multi-Agent AI Review System – Verdict-Driven Quality Control

Slash API Batch: Cut AI Costs by 50% in 2026

Web dashboard for Hermes Agent — multi-platform AI chat, session management, scheduled jobs, usage analytics

Agent Skills for Solopreneurs

AirLLM dramatically reduces inference memory usage, letting 70B large language models run on a single 4GB GPU card

Zero, your trustworthy AI teammate for real work.

一套 DSH runtime，Desktop、Web 与 TUI 三种开发体验。

Open-source operational advisor for ClickHouse — real-time monitoring plus AI-driven index/partition/materialized-view recommendations.

⚙️ TypeScript Style Guide and Agent Skill. A concise set of conventions and best practices for consistent, maintainable code.

Framework for AI agents to build and maintain a digital brain through Obsidian wiki

Apache Maka (Incubating) is a local-first AI agent workspace. Model messages, tool calls, tool results, permission decisions, and termination events are recorde…

Neo.mjs is a self-evolving software organism: a professional end-to-end AI engineering team whose cross-model swarm inhabits live apps via Neural Link, Active H…

Agentic development harness for Claude Code — SPEC-driven plan/run/sync, TRUST 5 quality gates, model+effort routing, and Claude×GLM multi-LLM cost control. Sin…

NocoBase is an open-source AI + no-code platform for building business systems fast. Instead of generating everything from scratch, AI works on top of productio…

An open-source AI coding agent that lives in your terminal.

PawWork — free, open-source desktop AI agent for macOS and Windows. Alternative to Codex App and Claude Cowork. BYOK with 75+ providers, ChatGPT OAuth, local mo…