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.
/shard
Shard
`crabbox shard` forks a [checkpoint](checkpoint.md) into `--count` leases
/share
Share
`crabbox share` grants other people access to an existing lease through the
/ssh
Ssh
`crabbox ssh` resolves a lease and prints a ready-to-run `ssh` command for it.
/status
Status
`crabbox status` prints the current state of a lease: its slug, provider,
/stop
Stop
`crabbox stop` ends a single lease. For coordinator-backed and direct cloud
/sync-plan
Sync plan
`crabbox sync-plan` prints the local sync manifest and its size hotspots
/tunnel
Tunnel
`crabbox tunnel` opens one foreground SSH local port-forward to a resolved
/unshare
Unshare
`crabbox unshare` removes sharing rules from a coordinator-backed lease. It is
/usage
Usage
`crabbox usage` reports lease cost and usage estimates from the broker, broken down by user, organization, or the whole fleet.
/verify
Verify
`crabbox verify` checks a signed run receipt produced by `crabbox run --attest`.
/vnc
Vnc
`crabbox vnc` prints (or opens) the connection details for a desktop-capable
/warmup
Warmup
`crabbox warmup` leases a box and waits until it is ready: it provisions (or
/watch
Watch
`crabbox watch` runs a command on a warm lease, then watches the local
/webvnc
Webvnc
Linux portal viewers default to **Match window**: only the connected controller
/whoami
Whoami
`crabbox whoami` calls the broker's `/v1/whoami` endpoint and prints the
/add-dep
Add dep
Vet a new or changed third-party dependency for license, provenance, and supply-chain risk before any install runs.
/adr-status
Adr status
Inspect ADR health read-only; optionally select one ADR with --adr N.
/adr
Adr
{{SKILL_ENTRY:decision-lifecycle}}
/audit
Audit
Assemble the governance record for a range — commits, overrides, ADRs, sprint auto-decisions, open questions, checkpoint findings — into one dated audit packet. Read-only.
/btw
Btw
Lightweight Q&A about the project — answer from context and return, no routing, no state change.
VCP 部署在 AI 模型 API 与前端应用之间,是面向AGI OS开发和探索的工业级基建示范项目。通过统一指令协议、多层级持久化记忆、分布式插件引擎及多 Agent 协作框架,将原本“无状态、无记忆、无工具调用能力”的大语言模型,彻底改造成拥有永久自我意识、物理世界操作权及群体协作智能的完整智能体系统。
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