LLM Mart Basic
@llm-mart · Joined Jun 2026
Triage a fetched PR review with the user, comment by comment, drafting each reply and producing a REQUIREMENTS file for the accepted code changes. Takes the PR-REVIEW file produced by fetch-pr-review. Invoke manually only.
Refine a development ticket — or brainstorm a raw idea — into a validated, self-contained REQUIREMENTS document — the "what", verified against the codebase. Invoke manually only.
Assist a human reviewing a pull request or branch locally — diff a source branch against its target (auto-detected or from a PR link) and return concise, human-voice review comments with file and line locations. Read-only, never posts.
Triage a ticket or ticket set before work starts — compare it against the codebase and save a review covering verdict, feature walkthrough, and only the high-cost questions worth raising.
Run nx format, lint, test, and build on affected or specified projects, then fix unambiguous failures
Capture durable user feedback into the governing skill/doc, or propose creating a new skill when no suitable one exists, so future sessions don't repeat the mistake. Use when the user rejects, reverts, or overrides the agent's output or approach on something a skill/doc covers or
Self-review a changeset until merge-ready — a fresh-context reviewer checks it as a maintainer would, the author answers every finding, and a compact report for the PR proves the review happened.
Split a plan into small, individually reviewable tasks grouped into PR-sized batches, appended as a task section at the end of the plan file.
Voice rules for text published under a person's name and read as if a person typed it, such as chat replies, PR comments and descriptions, commit messages, review replies, and code comments. Defines the wording only, never the content.
Verify the engineer is ready to implement a feature — a teach-back conversation over a .TICKET-REVIEW.md where they explain the feature in their own words and the agent probes and corrects.
1:1 rebuild of award-winning creative websites (WebGL / scroll-animation / portfolio sites). Evidence-driven pipeline - mirror-first forensics, line-number-traceable reverse engineering of minified bundles, verbatim porting, quantitative verification gates. Use when user asks to
Use when auditing a specific page's SEO performance, content quality, and competitive position. The agent fetches the URL, Googles the primary keyword, reads the top 3 competitors, and produces a full 7-dimension audit — no exports, no analytics access required.
Use when planning a new article. The agent Googles the keyword, reads the top 10 results, classifies intent, maps the content gap, and produces a writer-ready brief with structure, outline, and on-page artifacts. No keyword tool required.
Use when writing a complete SEO article. Includes the full anti-AI-slop ruleset (banned vocabulary, banned phrases, banned structural patterns) and voice rules. The agent researches the SERP itself if needed — no keyword data exports required.
Use when rewriting or refreshing an existing page that's underperforming. The agent fetches the URL, analyzes the current content, researches the SERP, and rewrites using the full anti-AI-slop ruleset — no data exports needed.
Use when planning to rank for a specific keyword. The agent Googles it, reads the top 10, classifies intent, reads the top 3 competitor pages, and produces a 90-day ranking plan with intent, SERP analysis, and content recommendations.
Use when a page ranks for a keyword but isn't in the top 3 and you want to know exactly what's missing. The agent compares the page to the top-ranking competitors and produces a specific list of entities, subtopics, and relationships to add.
Use when auditing a page for E-E-A-T signals. The agent reads the page and scores Experience, Expertise, Authoritativeness, and Trustworthiness — then tells you exactly what to add to each dimension.
Use when planning a topic cluster (hub + spokes) for a new content area. The agent researches the space, identifies the hub topic, maps the spokes, and produces a specific content plan with internal linking strategy.
Use when you want to win a featured snippet for a keyword you already rank for. The agent checks the current snippet format, analyzes your content, and rewrites the relevant section to match what Google wants.
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。
Make any song you can imagine
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