LLM Mart Basic
@llm-mart · Joined Jun 2026
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.
/story-cover
Story cover
网文封面生成。分析书名题材,生成专业封面图。
/story-deslop
Story deslop
网文去AI味。检测并清除文本中的AI写作痕迹,让文字回归自然。
/story-import
Story import
逆向导入已有小说。将已写好的小说反向解析为标准项目目录结构。
/story-long-analyze
Story long analyze
长篇网文拆文。深度拆解爆款长篇小说的黄金三章、人设、爽点、节奏。
/story-long-scan
Story long scan
长篇网文扫榜。分析起点、番茄、晋江等平台排行数据,提炼市场趋势。
/story-long-write
Story long write
长篇网文写作。从大纲到正文,辅助长篇网络小说的创作。
/story-review
Story review
多视角对抗式审查。使用多个 Agent 对作品进行多维度审稿。
/story-setup
Story setup
网文写作环境部署与检查。部署 hooks、rules、agents、项目指令等基础设施;传入 check 只检查不改动。
/story-short-analyze
Story short analyze
短篇网文拆文。拆解爆款短篇的故事核、结构、情感线和反转设计。
/story-short-scan
Story short scan
短篇网文扫榜。分析知乎盐言、番茄短篇等平台热门数据。
/story-short-write
Story short write
短篇网文写作。辅助短篇小说创作,从构思到成稿。
/story
Story
网文工具箱路由入口。根据模糊意图自动分发到对应的写作、拆文或扫榜工具。
/browser-cdp
Browser cdp
浏览器操控。通过 CDP 复用 Chrome 登录态执行浏览器自动化。
/story-cover
Story cover
小说封面生成。根据书名、作者名和题材生成专业网文封面。
/story-deslop
Story deslop
网文去 AI 味。检测并清理模板化、解释腔和过度工整表达。
/story-import
Story import
逆向导入已有小说,将成稿或半成品解析为可续写项目。
/story-long-analyze
Story long analyze
长篇网文拆文,分析黄金三章、人设、爽点和长线节奏。
/story-long-scan
Story long scan
长篇网文扫榜,分析起点、番茄、晋江等平台趋势。
/story-long-write
Story long write
长篇网文写作,从选题、大纲到逐章正文和持续追踪。
/story-review
Story review
多视角小说审查;ZCode 项目 agents 不可用时自动降级 solo。
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