LLM Mart Basic

Use Labtasker v2 to queue and run independent ML inference, evaluation, or experiment Tasks; migrate pipelines; design routes and Workers; inspect Task demand and Worker activity; and recover Tasks. Do not use it as a GPU allocator, cluster scheduler, workflow DAG, or artifact st

Write or revise Labtasker's README and user documentation, especially product positioning, tutorials, guides, examples, case studies, and navigation. Preserve the project's direct, plain-English style for ML researchers, agent-friendly workflow, and contract accuracy. Do not use

Change Labtasker's public Task lifecycle, HTTP API, Python API, CLI, configuration, query language, or persisted schema while keeping every public surface and invariant aligned. Do not use for internal refactors with no observable behavior change.

Prepare, validate, tag, or publish a Labtasker release. Use for version bumps, release readiness, release artifacts, tags, GitHub releases, or PyPI publication; do not use for ordinary development builds.

Develop and regression-test Labtasker's public Agent Skill with concise real-user requests, independent examiner and candidate agents, outcome checks, and bounded self-revision. Use when changing skills/labtasker, evaluating its usability, or adding feature workflow coverage; not

Grok Bot skills, plugins, and MCP servers.

Use this skill when creating or editing an agent.yaml (or .yml/.hcl) configuration file for Docker Agent (cagent), including defining agents, models/providers, built-in or MCP toolsets, multi-agent teams with sub_agents. Even if the user just says they want to "build an AI agent

Use this skill when exposing a Docker Agent as a server (MCP, HTTP API, A2A, ACP, or OpenAI-compatible chat), distributing an agent via an OCI registry with `docker agent share`, or measuring agent quality with `docker agent eval`. Even if the user just says they want to "turn my

Use this skill when running a Docker Agent with `docker agent run`, choosing a safety/approval mode, using the `--sandbox` isolation flag, setting up aliases, or troubleshooting a run (missing credentials, worktrees). Even if the user just says they want to "run my agent", "make

Use this skill when writing, reviewing, or optimizing Dockerfiles, even if the user just says their image is too large, their build is slow, or they need to harden a container for production. Covers multi-stage builds, layer caching, .dockerignore, non-root users, and image size

Use this skill when creating, modifying, or debugging Docker Compose configurations, even if the user just says they need to wire services together, add a database to their stack, or set up a local development environment with multiple containers. Covers service definitions, heal

Use this skill before running, or recommending, any Docker command that deletes, wipes, resets, or otherwise irreversibly changes state — even if the user just says to "clean up", "clear the cache", "start fresh", "wipe everything", "nuke it", "reset", "force remove", or "tear do

Use this skill when setting up, initializing, or Dockerizing a project, even if the user doesn't explicitly mention Docker but describes a need for containerized local development, adding a database or cache dependency, or running services without host-level installs. Covers Dock

Use this skill when authoring, planning, or running a declarative `sbxenv.yaml` file for Docker Sandboxes (`sbx env create/run/plan/exec/rm`), even if the user just says they want to "check in a sandbox config", "make onboarding reproducible for a sandbox", "run a setup script be

Use this skill when authoring, validating, packaging, signing, or composing a Docker Sandboxes kit `spec.yaml` (`sbx kit add/inspect/pack/pull/push/sign/validate/verify`), even if the user just says they want to "add a tool to a sandbox agent", "build a reusable sandbox extension

Use this skill when creating, running, reattaching to, listing, stopping, or removing Docker Sandboxes (the standalone `sbx` CLI that runs AI coding agents in isolated microVMs), even if the user just says they want to "run claude in a sandbox", "isolate an agent from my repo", "

Use this skill when configuring what a Docker Sandboxes (`sbx`) sandbox can reach on the network or which credentials it authenticates with, even if the user just says they want to "let the agent call an internal API", "block all network access", "give the agent a GitHub token",

Analyze engagement patterns across published posts to identify what works. Use when asked to review performance, find successful patterns, or optimize future content.

Generate LinkedIn post ideas from external sources (files, URLs, research). Use when the user provides source material (PDFs, URLs, articles) to brainstorm topics. NOT for writing or developing drafts - use write-linkedin-post instead.

Generate opinion piece ideas from recent LinkedIn posts (last 30 days). Use when asked to find opinion topics, brainstorm article ideas, or cross-pollinate content between LinkedIn and opinion pieces.

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.

Audit project dependencies for vulnerabilities, outdated packages, license conflicts, and supply chain risks — then provide actionable remediation strategies.

Refactor provided code for cleanliness, maintainability, and alignment with SOLID principles and modern best practices — no over-engineering.

Analyze and remediate technical debt — inventory debt items, score by impact, and produce a prioritized remediation plan with estimated effort.

Orchestrate comprehensive multi-dimensional code review using specialized review agents across architecture, security, performance, testing, and best practices

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

Execute tasks from a track's implementation plan following TDD workflow

Manage track lifecycle: archive, restore, delete, rename, and cleanup

Create a new track with specification and phased implementation plan

Git-aware undo by logical work unit (track, phase, or task)

Initialize project with Conductor artifacts (product definition, tech stack, workflow, style guides)

Display project status, active tracks, and next actions

Restore saved project context and decisions to resume a session

Save project context, decisions, and progress for a later session

Build features guided by data insights, A/B testing, and continuous measurement

Design and implement batch and streaming data pipelines with ingestion, orchestration, dbt transformations, data quality checks, and monitoring

Reduce cloud costs across AWS, Azure, and GCP through rightsizing, reserved and spot capacity, storage tuning, and cost monitoring

Migration monitoring, CDC, and observability infrastructure

SQL database migrations with zero-downtime strategies for PostgreSQL, MySQL, SQL Server

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

Audit project dependencies for vulnerabilities, outdated packages, license conflicts, and supply chain risks — then provide actionable remediation strategies.

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