LLM Mart Basic

角色设计与对话创作专家。负责角色设定、语言风格档案、动机链、人物弧线、 对话质量、角色关系设计。被 story-long-write（Phase 2,4）和 story-short-write（Phase 2,3）调用。 也可审查角色一致性和对话质量。

事实一致性与伏笔状态检查专家（只读）。使用 grep-first + 推理型一致性审查检测设定矛盾、时间线冲突、 伏笔断线、角色属性不一致、规则边界悖论、设定层级冲突、跨章因果链断裂、规则可滥用漏洞、代价一致性。输出 S1-S4 分级冲突报告。 被 story-review、story-long-write（Phase 5）、story-short-write（Phase 4）调用。 不做任何创作判断。

叙事文本创作与去AI味专家。负责正文写作（三维度揉进、感知/反应）、 情绪弧线执行、开篇/收尾、去AI味（禁用词替换、句式去套路、节奏调整）。 被 story-long-write（Phase 4-5）和 story-short-write（Phase 3-4）调用。 也可执行完整去AI味流程和格式合规检查。

故事架构与世界观创作专家。负责题材选择、核心梗设计、世界观构建、大纲排布、 钩子/悬念/反转等叙事工程、情绪弧线设计、范围控制审查。 被 story-long-write（Phase 1-3）、story-short-write（Phase 1-2）调用。 也可审查已有内容的结构问题。

故事项目结构化查询 agent（只读）。响应关于角色状态、伏笔进度、设定出现位置、 时间线节点、写作进度的查询。使用 grep + read 从项目文件系统中检索信息， 返回结构化 JSON 摘要。 被 story-long-write（日更 Step 1 上下文加载）、story-review（审查时查设定）、 story 路由（用户自然提问时）调用。 不做任何创作判断或修改。

小说写作资料研究 agent。接收研究查询，优先使用 CDP (agent-browser) 搜索并提取完整正文， WebSearch/webReader 作为兜底。输出带来源引用的结构化 Markdown 参考文件。 被 story-long-write（Phase 4）、story-review、story skill 路由调用。

Generate cinematic AI shortfilm prompts (works with Seedance 2.0, Xiaoyunque, Sora, Kling, Jimeng, Veo) using the 5-stage structure from Mx-Shell's Zombie Scavenger. Trigger when the user wants transformation sequences, multi-shot narrative shorts, weapon-charge/combat segments,

Analyze a finished coder-eval run and write analysis.md — cluster failures into systemic patterns, diagnose prompts, criteria, config, environment and cost, and recommend concrete fixes. Use when the user wants to know why a run failed, what to fix, or what a run says about their

Generate and run a coder-eval activation suite for a Claude Code skill — does the agent actually engage it when it should, and leave it alone when it shouldn't? Use when the user asks whether a skill triggers, wants to test skill activation, or worries a skill has silently stoppe

Generate a GitHub Actions workflow that runs a coder-eval suite as a CI gate or on a schedule, using the published composite action — with the agent runtime, credentials, JUnit output and a score floor wired correctly.

Set up coder-eval in this repository — scan for what is worth evaluating (Claude Code skills, an MCP server, a CLI), then scaffold a task directory with one real, passing-or-failing task and the exact command to run it.

Review coder-eval task YAML that already exists — find criteria that cannot fail, prompts that give away the answer, fixtures with no cleanup, and near-duplicate tasks, each with a severity and a concrete fix. Read-only. Use when the user wants existing tasks reviewed, linted, au

Turn a natural-language description into one or more coder-eval task YAML files — minimal prompts, weighted success criteria that check output content, validated with `coder-eval plan`. Use when the user wants to write, add, or generate an evaluation task.

Run Google Antigravity (Gemini) as the agent under evaluation in Coder Eval — installation, authentication, model and skill configuration, and how its telemetry maps to sandboxed, weighted scoring.

Configure and run the default Claude Code agent in Coder Eval — the full agent-config surface, direct vs. Bedrock authentication, permission modes, sandbox isolation, skills/plugins, early stop, and token telemetry.

Run OpenAI Codex as the agent under evaluation in Coder Eval — installation, authentication, task configuration, and how Codex telemetry maps to sandboxed, weighted scoring.

Imported from uipath/coder_eval/docs/agents/HARNESS_PARITY.md.

Genera post LinkedIn cringe (italiano di default, ma funziona in qualunque lingua), calibrati su livello di cringe (1-10), registro (credibile / parodico / surreale deadpan alla Lynch) e moduli cringe scelti da un catalogo di 37, con la possibilità di partire da un fatto reale (u

Analizza i commenti di un post LinkedIn (tipicamente un post cringe generato con la skill linkedin-cringe) e produce un report markdown con le statistiche - quanti ci hanno creduto e quanti hanno colto lo scherzo, top ten per gradimento, toni, categorie di commentatori, cringe-me

Il Cringiometro. Dato l'URL (o il testo) di un post LinkedIn, ne misura il livello di cringe da 1 a 10 con la scala e il catalogo dei 37 moduli della skill linkedin-cringe, dice quali ganci ha preso, il registro, il sapore-AI e la lead-gen, e produce un report markdown più un'imm

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.

Check frontmatter against the schema

Pull knowledge into a project

Scan for credentials

Show what a claim rests on

Split an overloaded page

Find concept pages nobody has touched

Audit the tag vocabulary

How my sources developed over time

Show which pages an answer used

Add relation types where they matter

The weekly review

Implement an approved plan or issue in its own worktree, run the gate, open the pull request.

Close a finished session: sweep for unfinished work, ask once, land, file the follow-ups, hand off, tell the sessions that depend on this one, then archive.

Write the repository handoff file for the next session, and record any durable learning.

Merge an approved pull request, clean up its worktree and branch, then check whether a release is due.

Turn a topic or issue into a plan the reviewer approves in the native plan pane.

Answer a research question with parallel read-only gatherers and one synthesized digest.

Review the branch's diff in two fresh contexts — scope against the spec, then quality — and report findings only.

Transform a vague prompt into precise, structured AI instructions

Implement an approved plan or issue in its own worktree, run the gate, open the pull request.

Implementation of Podlite markup language

Open-source AI video workbench. Bring any model or your local ComfyUI, and let Claude Code / Codex / Cursor direct it over MCP — storyboard, references, generat…

《深入理解 AI Agent：设计原理与工程实践》（李博杰 著）开源主仓库：全书正文、编译版 PDF 与按章配套代码

Distilly — Distill how they think into reusable Skills for any Agent or Bot. Formerly Colleague Skill（原同事 Skill）.

本地优先的类飞书 Markdown 编辑器,内置可审改的 AI 助手 | Local-first WYSIWYG Markdown editor with a reviewable AI agent

A lightweight all-in-one terminal with 20+ protocols — SSH, RDP, SFTP, databases, Kubernetes and more. With a built-in autonomous AI Agent that plans and runs m…

一款专注于桌宠方向的AIAgent。特点是一键安装、功能齐全、极低开销、完全暴露上下文、全功能插件化、AI自主插件开发、永久唯一会话、类游戏引擎交互策略。具有极高的扩展性和拟人程度上限，非常适合想长期培养和自定义需求高的用户。

Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms, Tasks, Search & Drive with AI - Comprehensive Google Workspace MCP Server & CLI Tool

一个先接住情绪、再分析关系并给出可执行策略的 Codex 恋爱军师，内置心理、法律、社会、人文、哲学、婚姻家庭与性学知识库，支持多元关系。

AI-powered SRE platform — read-only infrastructure diagnostics with deep investigation, security governance, and team collaboration

An open-source AI desktop companion inspired by Cyrene, combining immersive Chat, personalized long-term memory, and an agentic Work mode.

A local-first AI agent with persistent memory, emotional intelligence, and a peer-to-peer skills economy.

The open-source, multi-agent alternative to Claude Tag. @ any agent, wherever work happens, your agents work alongside your team and each other, learning as the…

Simple LLM service identification - translate IP:Port to Ollama, vLLM, LiteLLM, or 60+ other AI services in seconds

🔥 Official Firecrawl MCP Server - Adds powerful web scraping and search to Cursor, Claude and any other LLM clients.

Claude in Chrome, reverse-engineered and open-source. No domain blocklist. Any Chromium browser. Same 18 MCP tools, same performance.

Agent-native flight & hotel search and booking — MCP server, CLI, and Python/JS SDKs. Hundreds of airlines plus the major booking sites, with per-flight reliabi…

Open-source desktop AI agent for tools, files, knowledge, workflows, and real deliverables.

Open-source AI job search: scan job portals, evaluate listings into a structured A-H report with a global 1-5 score, tailor your CV, track applications — runs l…

Privacy-first Codex skill for discovering, validating, completing, and tracking job applications