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.

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.

Make any song you can imagine

Leading AI-powered video generation platform that specializes in creating hyper-realistic talking avatars

Hermes Agent is an open-source, self-improving autonomous AI agent developed by Nous Research

Kilo Code is a popular, open-source AI coding agent and "agentic engineering" platform designed to help developers build, refactor, and debug software faster

General-purpose agent in one static Go binary. ReAct loop, ACP server for IDEs, OpenAI-compatible REST API with embedded web UI, Telegram gateway, cron schedule…

Autonomous agent framework with structured memory, safety hooks, and loop management. Built by the agent that runs on it.

TSP自托管、零运维的 A 股「选股 + 监控 + 回测」量化工作台 | 基于 TickFlow 数据源 | LLM能力驱使策略定制+个股分析+复盘 | 自由接入第三方数据源与个性化扩展数据 | 个人开源 ,非TickFlow官方项目

Curated, verified Agent Skills powered by ModelStudio.

Run Claude Code, Codex, Antigravity, Cursor Agent and OpenCode as one runtime — persistent sessions, multi-agent councils, an OpenAI-compatible endpoint, an MCP…

pi had nothing (nothing), so I made something (something) — sorry mariozechner-senpai, I went ahead and lovingly soiled your pure pi for you. opinionated fork o…

A persistent workspace for development work that self-improves and continues beyond one session.

Open-source memory and context for user-aware agents: scoped memory, provenance, retrieval quality, correction, boundaries, evals, and MCP/HTTP access.

📚 A zero-dependency, git-backed micro-lesson library for AI Agents to asynchronously share and search verified debugging experience. Python stdlib only. | http…

Deterministic, local-first memory and guardrails for AI coding agents with no LLM in the hot path.

Deterministic spec-orchestration for local LLMs in the pi coding agent — drives prompts through refine→research→grill→compose→critique, with bundled web/docs/fe…

Native Safari browser automation for AI agents. 97 tools via AppleScript — zero overhead, keeps logins, runs silently in background. Drop-in alternative to Chro…

Agent OS: keep specialist agents in a hub, spin up a temporary orchestrator per task. Local-first, works with any model.

Git for agent memory. Branches, diffs, PRs, and rollback for what your agents know.

Multi-Provider AI Gateway - No personal logs by design. Model autodiscovery, Failover groups, High availability, Android companion app, and more - "Because we h…

Production-grade MCP server for MikroTik RouterOS with secure AI-native network automation.