LLM Mart Basic

Audit or repair ML experiment reproducibility and review whether code changes preserve recorded outputs. Use for reproducibility blockers, before-and-after experiment checks, or preparing a reproducible experiment package.

Investigate training failures, NaNs, missing gradients, misleading losses, and non-reproducible runs in PyTorch, Lightning, or TensorFlow/Keras. Use for a concrete training bug or regression, not an open-ended architecture or hyperparameter search.

Edit stiff or AI-sounding prose into natural writing while preserving meaning, facts, citations, and the author's point of view. Use when asked to humanize, de-AI, or make an existing draft sound more human, in its original language.

Translate and localize Polish and English technical documentation, UI text, and project updates. Use for natural PL/EN phrasing while preserving commands, placeholders, factual precision, and a project glossary.

Adapt a draft to a user's writing samples or explicit tone brief. Use for consistent personal or project voice across docs and posts; distinguish style evidence from factual content.

Prepare release notes and migration guidance from a verified revision range and project release policy. Use for release preparation; never present open PRs or planned changes as shipped features.

Reduce a reported software bug to a runnable, minimal reproduction with the exact command, environment, and observed failure. Use when verifying a bug report; do not infer success from a failing setup command.

Review a pull request for concrete correctness regressions using its diff, surrounding code, and relevant tests. Use for a requested PR review; distinguish actionable defects from optional preferences.

Review an upstream documentation change against the skills, agent instructions, or runbooks that cite it. Identify supported updates, unaffected instructions, and unresolved version or evidence gaps. Use with a supplied source diff or Skill Watch result.

Turn a repository issue into an evidence-backed triage note: observed behavior, missing reproduction details, possible duplicates, and the next useful action. Use for issue triage, not implementation or bulk issue closure.

Verify a proposed bug fix against an unchanged regression test and the relevant existing tests, reporting baseline and candidate outcomes separately. Use for fix verification, not a general claim that software is bug-free.

Draft factual project announcements, GitHub launch posts, and development updates for a specified audience or platform. Use to explain shipped work clearly, with evidence and a useful invitation for feedback.

Draft clear, respectful replies to issues, PR discussions, and technical support reports from available evidence. Use to explain status, request a minimal reproduction, or communicate a project decision without inventing commitments.

Create or improve a repository README from actual project evidence, with a clear purpose, usable quickstart, and honest limitations. Use for project landing documentation and onboarding, rather than long tutorials or release notes.

Write a focused regression test from an established bug reproduction and public behavior. Use when a fix needs a test that fails on the affected version; avoid mirroring the implementation or weakening assertions.

Build a step-by-step technical tutorial around a reproducible outcome, with prerequisites, checkpoints, and recovery steps. Use for hands-on guides when a README quickstart is too short.

Write or revise interface labels, errors, empty states, and confirmation text from actual product behavior. Use for UI microcopy with clear next actions, preserved localization tokens, and explicit length constraints.

Investigate training failures, NaNs, missing gradients, misleading losses, and non-reproducible runs in PyTorch, Lightning, or TensorFlow/Keras. Use for a concrete training bug or regression, not an open-ended architecture or hyperparameter search.

Edit stiff or AI-sounding prose into natural writing while preserving meaning, facts, citations, and the author's point of view. Use when asked to humanize, de-AI, or make an existing draft sound more human, in its original language.

Translate and localize Polish and English technical documentation, UI text, and project updates. Use for natural PL/EN phrasing while preserving commands, placeholders, factual precision, and a project glossary.

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.

`crabbox attach` follows the recorded events of an active coordinator run and

`crabbox azure` groups Azure provider setup commands. It currently has a single

`crabbox bench` records and reports local benchmark timing observations. It is a

`crabbox cache` inspects, purges, or warms package and build caches on a

`crabbox capsule` captures, replays, and tracks lightweight failure capsules.

Save the state of a lease, then restore it onto another box or fork it into a

`crabbox claims list` prints the lease claims stored on the current machine. It

`crabbox cleanup` sweeps direct-provider machines and local provider state that

`crabbox code` bridges a Linux lease's `code-server` workspace into the

`crabbox config` inspects and updates user configuration. It has three

`crabbox connect` resolves a lease and opens an interactive SSH session to it.

`crabbox cp` copies files or directories between the host and a Crabbox-owned

`crabbox desktop` drives a visible desktop session on a lease that was warmed

`crabbox doctor` runs a preflight before you commit to a long workflow. It is

`crabbox egress` gives a lease mediated outbound network: a lease-local browser

`crabbox events` prints the broker's event log for a recorded run.

`crabbox heartbeat` refreshes the idle deadline for one owned lease and prints

`crabbox history` lists recorded remote command runs from the broker. Each run is

`crabbox image` holds the trusted-operator controls for provider base images:

`crabbox init` onboards the current repository: it writes the minimal config

Atom Agent, Open-Source Governed AI Agent Platform for Self-Hosted Automation

The agent engineering intelligence harness, optimized tools, memory system, subagents and mixture of models packages ⚚

Sesori iOS/Android app and the Sesori Bridge CLI — drive Claude, Codex, OpenCode, Cursor, Pi, OMP, Hermes coding sessions from your phone

🧠 RepoBrain (formerly Antigravity) — Give your repo a brain. ChatGPT for your codebase: works in Claude Code, Cursor, Codex, Windsurf & more.

Use ChatGPT (Codex), Claude, and Grok (X Premium) subscriptions as DeepSeek Harness LLM providers — OAuth login in the web UI, no API keys

Latitude is the open-source AI monitoring platform.

Open-source AI browser agent for Chrome and Firefox (monorepo) 🧠

Official Model Studio CLI（阿里云百炼 CLI）built for AI Agent frameworks, exposing models, search, multimodal, and workflow capabilities as structured tool calls.

Curated DeepSeek Harness (DSH) plugins, extensions, tools, skills, clients, runtimes, integrations, and verified references — English and Chinese.

Turn papers, code, and docs into presentation-ready, natively editable PPTX in Codex / Claude Code. Native charts and equations, speaker notes, click-build anim…

Grix : Work with agents like talking to people.

Local First Ai Agent. Optimized for Local Ai models. Long context window. Proper tools callings. Runs privately on your device.

Model Context Protocol server that integrates AgentQL's data extraction capabilities.

AI that ships your tickets.

Metadata-driven CLI for AI Agent Bots — 48 operations across 7 domains, structured JSON envelope I/O, zero interactive prompts.

An open-source, extensible, self-hosted agent workspace with multi-runtime support for Codex, Claude Code, and more, plus reusable local apps for custom interfa…

Open-source Windows desktop client and GUI for DeepSeek Harness — zero-setup installer with Codex, plugins, skills, SSH, mobile remote access, and 11 skins.

The open-source AI workbench for scientific research

DeepSeek-native AI coding agent for your terminal. Engineered around prefix-cache stability — leave it running.

A Fully free agentic browser driver for AI , few tools, full control, real stealth, top-tier token efficiency.