LLM Mart Basic

Runs scoped browser probes for focus, hit targets, overflow, themes, request failures, and performance attribution, with evidence linked to UI rule IDs. Use when asked to "verify this in the browser", "reproduce this finding", or "check the fix". For source audits and severity us

Data analysis and reference enrichment.

Maximize information density: preserve all instructions, remove prose filler.

Generate headlines, titles, and subject lines: charge, volume, tighten.

FFmpeg-based video creation from image and audio.

Plan multi-part content series: structure, cross-linking, cadence.

Generate blog topic ideas: problem mining, gap analysis, expansion.

Writing: voice creation and validation, prose editing, anti-AI cleanup, professional communication, translation.

Domain-specific: SAP Commerce, OpenSearch detection, WordPress validation, enterprise search.

Design a CLI interface: args, flags, help, output, errors, exit codes, config.

Frontend: UI design, distinctive visual styles, HTML artifacts, Three.js 3D.

CPU-only motion data processing pipeline for game animation: BVH import, contact detection, root decomposition, motion blending, FABRIK IK. No GPU required.

Jev-driven browser automation: Jev picks operations, programs execute, a text model writes field values only when Jev cannot pick one from the goal.

Audit CVE/vulnerability source coverage for a technology stack. Maps each component (container, library, base image, runtime) to authoritative CVE feeds, flags gaps, and produces audit-ready reports. Generic: works for any service or stack.

Kubernetes operations: debugging, security, RBAC, and infrastructure tooling.

GitHub: notification triage, profile rule extraction.

Write, compose, integrate, and improve programs that call Jev, TypeSafe's System One judgment model.

Run benchmark-selected GPT-5.6 work through the Codex CLI.

Jev request router: validates the requested outcome, then dispatches to the matched agent, skill, and pipeline.

Toolkit management: create and evaluate skills and agents, manage routing tables, generate Claude.md.

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.

CoalWash self-update — check for a newer version and offer to apply it, or set how updates are handled.

Create an AI skill from any source (URL, repo, PDF, video, notebook, etc.)

One-command skill creation and packaging for a target platform

Sync a scraping config's URLs against the live documentation site

Generate and run validation queries for the current change

Generate and run validation queries for the current change

Check code-intelligence prerequisites (ripgrep + a language server) and print install hints

Create space-efficient paper and proposal figures (HTML mockups, TikZ, or skia-canvas) from tool selection through LaTeX insertion

Design concise overview, mechanism, or workflow figures as editable PowerPoint objects

Run the implement-review staged-change review loop

Detect the work type (papers, proposals, code, figures, admin) and dispatch to the right domain skill

Run prun: parallel delegation fan-out on Agy workers (the session coordinates)

Audit a GitHub README and rewrite it with modern patterns for a scannable ten-second skim

Vet the staged change: run the implement-review review loop (short alias)

Create space-efficient paper and proposal figures (HTML mockups, TikZ, or skia-canvas) from tool selection through LaTeX insertion

Design concise overview, mechanism, or workflow figures as editable PowerPoint objects

Run the implement-review staged-change review loop

Detect the work type (papers, proposals, code, figures, admin) and dispatch to the right domain skill

Run prun: parallel delegation fan-out on Agy workers (the session coordinates)

Audit a GitHub README and rewrite it with modern patterns for a scannable ten-second skim

Your car as a chat-room agent: Raspberry Pi 5 + dashcam + local AI. CodeWatch's sibling for the garage.

The enterprise gateway for autonomous agents. Identity management, per-channel isolation, credential vault, per-session tamper-evident audit log.

AI agent for Home Assistant — talk to your home, create automations in plain language, analyze cameras with face recognition, and get proactive anomaly alerts.…

A desktop app to prototype agent ideas, inspect every harness step, replay failures, and evaluate performance, all in one place. Local-first, cloud-ready for ma…

Local-first AI memory you can see, edit, and override — portable across Claude Code, Codex, Cursor, Windsurf, and other MCP coding tools.

Web & desktop (Electron) client for the OCTO open workplace — one React + TypeScript codebase shipping browser and PC surfaces, with first-class AI agent UX.

同花顺官方 A股金融数据服务，提供股票实时行情、历史行情、财务报表、指数、板块、涨停等数据，适用于 AI Agent、量化研究和应用开发，支持 API、MCP、CLI 和 Python。Official Tonghuashun (HiThink) A-share financial data service provi…

Open-source desktop SQL workspace for PostgreSQL, MySQL/MariaDB, SQLite and 15+ more databases like DuckDB, ClickHouse, Redis and Firestore. Built-in MCP server…

🤖 Local-assist BOSS Zhipin CLI for AI agents — search, welfare filtering, shortlist, JSON-envelope output; low-risk & compliant by default.

The local-first sidebar AI agent for ComfyUI — runs on your own Claude OR ChatGPT subscription (no API keys, no extra LLM costs). Drives your live graph: edits,…

Persistent memory for Claude Code — identity, context, and continuity across sessions

🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库，覆盖8大社会科学学科的实证研究。CoPaper.AI…

A curated list of plugins, skills, MCP servers, patch/profile layers, orchestrators & UIs for DeepSeek Harness (DSH). Visualization · PPT · Coding · Agents · Lo…

World Memory Protocol.

Run your own organization of agents.

The most RAM efficient harness

AI-native, free, open-source alternative to Jira, Trello, ClickUp & Monday. Built for Scrum teams where humans and AI agents collaborate as equals — on the same…

Free open-source desktop app that scrapes JAV metadata and generates NFO + cover art for Jellyfin, Emby & Kodi. No Docker, no CLI — one-click install on Windows…

A cross-platform desktop app to manage Agent Skills in one place and sync them to multiple AI coding tools’ global skills directories — “Install once, sync ever…

天枢 (Tianshu) 是一个基于harness工程的终端编程智能体运行时（Tui X Gui），针对DeepSeek V4 做了前缀缓存工程优化（长会话实测稳态命中率 97–99%）和深度适配。它跳出了传统 AI 编程助手把大模型仅当成“工具”的局限，基于认知虚拟机 (CVM)、自感知层和信息素（Stigmergy…