LLM Mart Basic

Finance (CFO). Owns plugins/finance/** and nothing else. Delegate work in this department's remit here.

Corporate IT. Owns plugins/it-operations/** and nothing else. Delegate service desk, systems and network administration, endpoints, assets, identity lifecycle, and backup work here.

Reviewer-class. Read-only review of what other departments commit to — contract terms, privacy and data handling, risk acceptance, and compliance findings. Holds no write surface. Its findings are not overrulable by the department under review.

Legal & Risk (CLO/CCO). Owns plugins/legal-risk/** and nothing else. Delegate work in this department's remit here.

Marketing (CMO). Owns plugins/marketing/** and nothing else. Delegate work in this department's remit here.

Operations (COO). Owns plugins/operations/** and nothing else. Delegate work in this department's remit here.

People (CHRO). Owns plugins/people/** and nothing else. Delegate work in this department's remit here.

Enterprise PMO. Owns plugins/pmo/** and nothing else. Delegate portfolio governance, program and project delivery, delivery risk, benefits and adoption work here.

Product (CPO). Owns plugins/product/** and nothing else. Delegate work in this department's remit here.

Repository scaffolding — docs, CI, scripts, plugin manifests, and the README. Owns everything outside plugins/. Delegate structural and documentation work here.

Revenue (CRO). Owns plugins/revenue/** and nothing else. Delegate work in this department's remit here.

Reviewer-class. Read-only security review of what other departments build — authorization, data handling, secrets, dependencies, and designs that create exposure. Holds no write surface. Blocking findings are not overrulable by the department under review.

Security (CISO). Owns plugins/security/** and nothing else. Delegate threat modeling, security architecture, incident response, vulnerability management, and identity work here.

Technology (CTO/CIO). Owns plugins/technology/** and nothing else. Delegate work in this department's remit here.

Keeps the engagement record for client work. Use when they name a client or stakeholder. Use when they debrief a meeting or paste notes. Use when they ask what was agreed. Use when they run a POC, change the client's codebase, prove it on their staging, go live, or need evals bef

Imported from nitinjain999/platform-skills/assets/agents/navigator.template.md.

코드·설정·프리셋 변경 뒤 문서를 현행화한다. "문서 현행화", "README 갱신", "문서 최신화" 요청 시, 그리고 동작·경로·버전·수치를 바꾼 PR 을 올리기 직전에 사용. 문서의 주장을 실제 코드·명령 출력·공식문서와 대조해 정정하고, 다국어 짝 파일을 함께 갱신한다.

이 파일을 복제해 프로젝트 스킬을 만든다. 트리거 조건을 description 에 구체적으로 적을 것.

사용자가 /grill-me 를 호출하거나 아이디어·기능 요청·스펙을 구현 전에 굽고(grill)·압박 검증하고·심문해 달라고 할 때 사용. superpowers:brainstorming 의 opt-in 대체재 — 작업마다 사용자가 둘 중 하나를 고른다.

에이전트 영속 메모리를 실제 소스와 대조 — 각 메모리의 핵심 단언을 코드·DB·이슈 트래커·파일시스템에 대조해 stale 을 정정하고 dead 를 아카이브 후보로 리포트. 메모리가 30개를 넘거나, 스택/인프라 큰 변경(라이브러리 교체·버전 업그레이드·서버 이전·스키마 DROP) 직후, 메모리끼리 모순돼 보일 때, 또는 "메모리 정리/감사" 요청 시 사용.

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.

Implement issues (GitHub/GitLab/Bitbucket) using progressive analyze-specify-plan-implement workflow

Address PR/MR review feedback by reading comments, implementing fixes, and resolving threads. GitHub and GitLab support.

Retrospective analysis and improvement of workflow components with self-evolving patterns

Fix broken functionality from pasted error output, stack traces, or

Summarize recent git history since a baseline with structured analysis of what changed, why, and what to watch for.

Consolidate ephemeral LLM-generated markdown into permanent documentation.

Review pull requests with scope validation, code analysis, and line comments. Supports GitHub PRs and GitLab MRs.

Prepare a PR end-to-end by updating documentation, running tests, dogfooding checks, and validating with code review.

Batch-resolve unresolved PR/MR review threads via GraphQL API (GitHub/GitLab)

Detect and fix drift between plugin.json registrations and capabilities reference documentation

Update pre-commit hooks and CI/CD workflows based on recent project changes

Scan and update dependencies across all ecosystems with conflict detection

Update project documentation with consolidation, debloating, AI slop detection, capabilities sync, and accuracy verification.

Audit and sync plugin.json registrations with actual disk contents. Detects missing or stale skills, commands, agents, hooks.

Review and update test coverage using TDD/BDD methodology with quality validation. Generates tests for changed code.

Generate or update tutorials with VHS and Playwright recordings

Bump project versions using git-workspace-review and version-updates skills.

Generate and self-execute a diff-derived test plan for a PR. Reads

Generate new documentation with human-quality writing.

Clean up AI-generated content and improve documentation quality.

LLM-powered toolkit for skill analysis, AI interviews, resume scoring, and job structuring. Automates professional skill taxonomy and interview processes with a…

【三年面试五年模拟】AIGC/LLM/AI Agent算法工程师面试资源平台。涵盖AIGC、LLM大模型、AI Agent、具身智能、传统深度学习、计算机视觉、自然语言处理、自动驾驶、机器学习、强化学习、大数据挖掘、世界模型、元宇宙、AGI等AI行业面试笔试干货经验与核心跨周期知识。

Easiest and laziest way for building multi-agent LLMs applications.

Drive Claude Code or OpenAI Codex from your phone — resume sessions, stream output, approve tool permissions remotely. End-to-end encrypted, zero-knowledge rela…

MCP server for autonomous agent economies: wallets, signed RTC micropayments, bounty discovery, BoTTube video publishing, and Beacon agent-to-agent messaging. G…

Free, open-source alternative to Microsoft Office with built-in AI agents — Word (.docx), Excel (.xlsx), PowerPoint (.pptx), PDF and Markdown editing for macOS,…

NSE BSE Indian Stock Market Data MCP server — search, screen & analyze all 8,200+ NSE/BSE stocks with 34 tools: live quotes, financials, technicals, 326-ratio s…

全流程 智能招投标 Agent：标书生成 · 招投标解读 · 标书检查 · 标书文档ai排版 · 商机发现 一键完成。 21 项合规检查 · 多模型切换 · RAG 知识库 · OCR 抽取。 从招标公告到可交付 docx 文档，全流程 AI 自动化。

Benzi is AI-native code intelligence infrastructure. Claude Code greps; Cursor embeds; Aider maps signatures; Benzi resolves — and answers in O(1). Every langua…

From ticket to reviewed pull request. Free and open-source, on your machine.

Get web data for AI agents and LLMs - fast, efficient, and reliable with Rust

Open-source, license-free MCP server for RTL waveform debug: reads FST waveforms (VCD/FSDB auto-convert) plus a SystemVerilog netlist, with 34 tools covering dr…

Open-source, desktop-grade AI agent that gets real work done — data analysis, slides, docs, video & web research. Built on OpenClaw; runs tools on your real des…

Parallel, isolated dev environments for humans and AI coding agents. Real containers. Real databases. Zero collisions.

OpenCode goal plugin for Codex-style goal mode, /goal slash commands, persistent objectives, and AI coding agent focus.

Battle-tested skill library for AI agents. Save 98% of API costs with ready-to-use code for crypto, PDFs, search, web scraping & more. No trial-and-error, no ex…

Deep Code 是专为 deepseek-v4 模型优化的终端 AI 编码助手，支持深度思考、推理强度控制以及 Agent Skills。

China Unicom's Yuanjing Wanwu Agent Platform is an enterprise-grade, multi-tenant AI agent development platform. It helps users build applications such as intel…

⚡ CloakBrowser MCP server for AI agents: Playwright-powered browsing, clean tool forwarding, Docker support, and multi-session HTTP transport.

MCP-NixOS - Model Context Protocol Server for NixOS resources